From 21d895a98ae5469cb232891352e160ce47d669cd Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 11:09:14 +0200
Subject: [PATCH 01/20] Creating a strong foundation

---
 .github/workflows/docs.yml                    |  29 -
 .github/workflows/lint.yml                    |  29 +
 .gitignore                                    |   3 +
 .lintbaseline.json                            | 285 +++++++
 PLAN.md                                       | 790 ++++++++++++++----
 docs/assets/paligemma/fine-tuning-01.jpg      | Bin 0 -> 9904 bytes
 docs/assets/paligemma/fine-tuning-02.jpg      | Bin 0 -> 15179 bytes
 docs/assets/paligemma/fine-tuning-03.jpg      | Bin 0 -> 9075 bytes
 docs/assets/paligemma/fine-tuning-04.jpg      | Bin 0 -> 8558 bytes
 docs/assets/paligemma/fine-tuning-05.jpg      | Bin 0 -> 12278 bytes
 docs/assets/paligemma/fine-tuning-06.jpg      | Bin 0 -> 14881 bytes
 docs/assets/paligemma/fine-tuning-07.jpg      | Bin 0 -> 9761 bytes
 docs/assets/paligemma/fine-tuning-08.jpg      | Bin 0 -> 8895 bytes
 docs/assets/paligemma/fine-tuning-09.jpg      | Bin 0 -> 7249 bytes
 docs/assets/paligemma/fine-tuning-10.jpg      | Bin 0 -> 8771 bytes
 docs/assets/paligemma/fine-tuning-11.jpg      | Bin 0 -> 6170 bytes
 docs/assets/paligemma/fine-tuning-12.jpg      | Bin 0 -> 9299 bytes
 docs/assets/paligemma/fine-tuning-13.jpg      | Bin 0 -> 7249 bytes
 docs/assets/paligemma/fine-tuning-14.jpg      | Bin 0 -> 8771 bytes
 docs/assets/paligemma/fine-tuning-15.jpg      | Bin 0 -> 6170 bytes
 docs/assets/paligemma/fine-tuning-16.jpg      | Bin 0 -> 9299 bytes
 docs/assets/paligemma/fine-tuning-17.jpg      | Bin 0 -> 7249 bytes
 docs/assets/paligemma/fine-tuning-18.jpg      | Bin 0 -> 8771 bytes
 docs/assets/paligemma/fine-tuning-19.jpg      | Bin 0 -> 6170 bytes
 docs/assets/paligemma/fine-tuning-20.jpg      | Bin 0 -> 9299 bytes
 docs/assets/paligemma/fine-tuning-21.jpg      | Bin 0 -> 7249 bytes
 docs/assets/paligemma/fine-tuning-22.jpg      | Bin 0 -> 8771 bytes
 docs/assets/paligemma/fine-tuning-23.jpg      | Bin 0 -> 6170 bytes
 docs/assets/paligemma/fine-tuning-24.jpg      | Bin 0 -> 9299 bytes
 docs/assets/paligemma/fine-tuning-25.jpg      | Bin 0 -> 7249 bytes
 docs/assets/paligemma/fine-tuning-26.jpg      | Bin 0 -> 8771 bytes
 docs/assets/paligemma/fine-tuning-27.jpg      | Bin 0 -> 6170 bytes
 docs/assets/paligemma/fine-tuning-28.jpg      | Bin 0 -> 9299 bytes
 docs/assets/paligemma/fine-tuning-29.jpg      | Bin 0 -> 9917 bytes
 docs/assets/paligemma/fine-tuning-30.jpg      | Bin 0 -> 6125 bytes
 docs/assets/paligemma/fine-tuning-31.jpg      | Bin 0 -> 3092 bytes
 docs/assets/paligemma/fine-tuning-32.jpg      | Bin 0 -> 5762 bytes
 docs/assets/paligemma/fine-tuning-33.jpg      | Bin 0 -> 8127 bytes
 docs/assets/paligemma/fine-tuning-34.jpg      | Bin 0 -> 9793 bytes
 docs/models/diffusiongemma/inference.md       |   1 -
 docs/models/functiongemma/fine-tuning.md      |   2 -
 docs/models/functiongemma/formatting.md       |   8 +-
 docs/models/gemma-3n/model-card.md            |   5 +-
 docs/models/gemma-4/index.md                  |  12 +-
 docs/models/gemma-4/model-card.md             |  10 +-
 docs/models/paligemma/fine-tuning.md          |  72 +-
 docs/models/paligemma/inference.md            |  10 +-
 docs/models/recurrentgemma/fine-tuning.md     |   4 -
 docs/models/recurrentgemma/model-card.md      |   5 +-
 docs/models/releases.md                       | 125 ++-
 docs/models/shieldgemma/model-card-v1.md      |   6 +-
 docs/models/shieldgemma/model-card-v2.md      |   2 +-
 docs/stylesheets/custom.css                   |  28 -
 scripts/check_fidelity.py                     | 156 ++++
 scripts/convert_notebook.py                   | 197 ++++-
 scripts/fixtures/admonitions.expected.md      |  18 +
 scripts/fixtures/admonitions.md.txt           |  16 +
 scripts/fixtures/base64_image.expected.md     |  16 +
 scripts/fixtures/base64_image.md.txt          |  14 +
 .../fixtures/list_continuation.expected.md    |   7 +
 scripts/fixtures/list_continuation.md.txt     |   9 +
 scripts/fixtures/prompt_template.expected.md  |  26 +
 scripts/fixtures/prompt_template.md.txt       |  20 +
 scripts/fixtures/split_code_block.expected.md |  17 +
 scripts/fixtures/split_code_block.md.txt      |  15 +
 scripts/lint_docs.py                          | 570 +++++++++++++
 scripts/port_docs.py                          | 123 +++
 scripts/sources.toml                          | 181 ++++
 scripts/test_convert_notebook.py              | 165 ++++
 69 files changed, 2678 insertions(+), 298 deletions(-)
 delete mode 100644 .github/workflows/docs.yml
 create mode 100644 .github/workflows/lint.yml
 create mode 100644 .lintbaseline.json
 create mode 100644 docs/assets/paligemma/fine-tuning-01.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-02.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-03.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-04.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-05.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-06.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-07.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-08.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-09.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-10.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-11.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-12.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-13.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-14.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-15.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-16.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-17.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-18.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-19.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-20.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-21.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-22.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-23.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-24.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-25.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-26.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-27.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-28.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-29.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-30.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-31.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-32.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-33.jpg
 create mode 100644 docs/assets/paligemma/fine-tuning-34.jpg
 delete mode 100644 docs/stylesheets/custom.css
 create mode 100644 scripts/check_fidelity.py
 create mode 100644 scripts/fixtures/admonitions.expected.md
 create mode 100644 scripts/fixtures/admonitions.md.txt
 create mode 100644 scripts/fixtures/base64_image.expected.md
 create mode 100644 scripts/fixtures/base64_image.md.txt
 create mode 100644 scripts/fixtures/list_continuation.expected.md
 create mode 100644 scripts/fixtures/list_continuation.md.txt
 create mode 100644 scripts/fixtures/prompt_template.expected.md
 create mode 100644 scripts/fixtures/prompt_template.md.txt
 create mode 100644 scripts/fixtures/split_code_block.expected.md
 create mode 100644 scripts/fixtures/split_code_block.md.txt
 create mode 100644 scripts/lint_docs.py
 create mode 100644 scripts/port_docs.py
 create mode 100644 scripts/sources.toml
 create mode 100644 scripts/test_convert_notebook.py

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
deleted file mode 100644
index 5c33c5f..0000000
--- a/.github/workflows/docs.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-name: Documentation
-on:
-  push:
-    branches:
-      - master
-      - main
-permissions:
-  contents: read
-  pages: write
-  id-token: write
-jobs:
-  deploy:
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/configure-pages@v6
-      - uses: actions/checkout@v7
-      - uses: actions/setup-python@v6
-        with:
-          python-version: 3.x
-      - run: pip install zensical
-      - run: zensical build --clean
-      - uses: actions/upload-pages-artifact@v5
-        with:
-          path: site
-      - uses: actions/deploy-pages@v5
-        id: deployment
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
new file mode 100644
index 0000000..c0cf166
--- /dev/null
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,29 @@
+name: Checks
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+  workflow_dispatch:
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      # Both scripts are stdlib-only (tomllib requires 3.11+), so there is nothing to install.
+      - name: Converter golden tests
+        run: python scripts/test_convert_notebook.py
+
+      - name: Lint documentation
+        run: python scripts/lint_docs.py --baseline
+
+      # Advisory: shows the full Phase 0 backlog without affecting the job result.
+      - name: Backlog (informational)
+        if: always()
+        run: python scripts/lint_docs.py || true
diff --git a/.gitignore b/.gitignore
index c55c473..93000a2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,3 +15,6 @@ venv/
 .idea/
 .vscode/
 *.swp
+
+# Cached upstream sources for scripts/port_docs.py
+.sources/
diff --git a/.lintbaseline.json b/.lintbaseline.json
new file mode 100644
index 0000000..e015dc5
--- /dev/null
+++ b/.lintbaseline.json
@@ -0,0 +1,285 @@
+{
+  "_comment": "Known violations at baseline time. Shrink to zero; never grow. Regenerate with: uv run python scripts/lint_docs.py --write-baseline",
+  "checks": [
+    "links",
+    "images",
+    "internal",
+    "stubs",
+    "frontmatter",
+    "orphans",
+    "fences",
+    "admonitions",
+    "size"
+  ],
+  "count": 290,
+  "known": [
+    "fences|docs/get-started/capabilities/audio.md|prompt template tagged as python: 'Transcribe the following speech segment in {LANGUAGE} into {...'",
+    "fences|docs/get-started/capabilities/audio.md|prompt template tagged as python: 'Transcribe the following speech segment in {SOURCE_LANGUAGE}...'",
+    "fences|docs/get-started/capabilities/image.md|`python` block split in two -- likely blank lines inside one block",
+    "frontmatter|docs/community/discord.md|no frontmatter block",
+    "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
+    "frontmatter|docs/community/gemmaverse.md|no frontmatter block",
+    "frontmatter|docs/cookbook.md|missing required key: description",
+    "frontmatter|docs/demos/concurrent.md|no frontmatter block",
+    "frontmatter|docs/deployment/google-cloud.md|no frontmatter block",
+    "frontmatter|docs/deployment/huggingface.md|no frontmatter block",
+    "frontmatter|docs/deployment/langchain.md|no frontmatter block",
+    "frontmatter|docs/deployment/litert.md|no frontmatter block",
+    "frontmatter|docs/deployment/llamacpp.md|no frontmatter block",
+    "frontmatter|docs/deployment/lmstudio.md|no frontmatter block",
+    "frontmatter|docs/deployment/mlx.md|no frontmatter block",
+    "frontmatter|docs/deployment/mobile.md|no frontmatter block",
+    "frontmatter|docs/deployment/ollama.md|no frontmatter block",
+    "frontmatter|docs/deployment/qat.md|no frontmatter block",
+    "frontmatter|docs/deployment/sglang.md|no frontmatter block",
+    "frontmatter|docs/deployment/vllm.md|no frontmatter block",
+    "frontmatter|docs/deployment/web.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/audio.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/function-calling.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/image.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/text.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/thinking.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/video.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/vision.md|no frontmatter block",
+    "frontmatter|docs/get-started/index.md|no frontmatter block",
+    "frontmatter|docs/get-started/prompt-formatting.md|missing required key: description",
+    "frontmatter|docs/get-started/prompt-formatting.md|missing required key: title",
+    "frontmatter|docs/get-started/quickstart.md|missing required key: description",
+    "frontmatter|docs/get-started/quickstart.md|missing required key: title",
+    "frontmatter|docs/models/datagemma/index.md|no frontmatter block",
+    "frontmatter|docs/models/diffusiongemma/explained.md|no frontmatter block",
+    "frontmatter|docs/models/diffusiongemma/index.md|no frontmatter block",
+    "frontmatter|docs/models/diffusiongemma/inference.md|no frontmatter block",
+    "frontmatter|docs/models/diffusiongemma/model-card.md|no frontmatter block",
+    "frontmatter|docs/models/embeddinggemma/fine-tuning.md|no frontmatter block",
+    "frontmatter|docs/models/embeddinggemma/index.md|no frontmatter block",
+    "frontmatter|docs/models/embeddinggemma/inference.md|no frontmatter block",
+    "frontmatter|docs/models/embeddinggemma/model-card.md|no frontmatter block",
+    "frontmatter|docs/models/functiongemma/fine-tuning.md|no frontmatter block",
+    "frontmatter|docs/models/functiongemma/formatting.md|no frontmatter block",
+    "frontmatter|docs/models/functiongemma/full-sequence.md|no frontmatter block",
+    "frontmatter|docs/models/functiongemma/index.md|no frontmatter block",
+    "frontmatter|docs/models/functiongemma/inference.md|no frontmatter block",
+    "frontmatter|docs/models/functiongemma/model-card.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-1/index.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-2/index.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-3/index.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-3n/index.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-3n/model-card.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-4/index.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-4/model-card.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-scope/index.md|no frontmatter block",
+    "frontmatter|docs/models/index.md|no frontmatter block",
+    "frontmatter|docs/models/paligemma/fine-tuning.md|no frontmatter block",
+    "frontmatter|docs/models/paligemma/index.md|no frontmatter block",
+    "frontmatter|docs/models/paligemma/inference.md|no frontmatter block",
+    "frontmatter|docs/models/paligemma/model-card-v1.md|no frontmatter block",
+    "frontmatter|docs/models/paligemma/model-card-v2.md|no frontmatter block",
+    "frontmatter|docs/models/paligemma/prompting.md|no frontmatter block",
+    "frontmatter|docs/models/recurrentgemma/fine-tuning.md|no frontmatter block",
+    "frontmatter|docs/models/recurrentgemma/index.md|no frontmatter block",
+    "frontmatter|docs/models/recurrentgemma/inference.md|no frontmatter block",
+    "frontmatter|docs/models/recurrentgemma/model-card.md|no frontmatter block",
+    "frontmatter|docs/models/releases.md|no frontmatter block",
+    "frontmatter|docs/models/shieldgemma/index.md|no frontmatter block",
+    "frontmatter|docs/models/shieldgemma/model-card-v1.md|no frontmatter block",
+    "frontmatter|docs/models/shieldgemma/model-card-v2.md|no frontmatter block",
+    "frontmatter|docs/tools/index.md|no frontmatter block",
+    "frontmatter|docs/tools/model-selector.md|no frontmatter block",
+    "frontmatter|docs/tools/vram-calculator.md|no frontmatter block",
+    "frontmatter|docs/tuning/distributed.md|no frontmatter block",
+    "frontmatter|docs/tuning/full-finetune.md|no frontmatter block",
+    "frontmatter|docs/tuning/keras-lora.md|no frontmatter block",
+    "frontmatter|docs/tuning/overview.md|no frontmatter block",
+    "frontmatter|docs/tuning/qlora.md|no frontmatter block",
+    "frontmatter|docs/tuning/vision-qlora.md|no frontmatter block",
+    "frontmatter|docs/tutorials/visual-guides/diffusiongemma.md|no frontmatter block",
+    "frontmatter|docs/tutorials/visual-guides/gemma-4-12b.md|no frontmatter block",
+    "frontmatter|docs/tutorials/visual-guides/gemma-4.md|no frontmatter block",
+    "frontmatter|docs/tutorials/visual-guides/mtp.md|no frontmatter block",
+    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_6b3b62e7_0.png",
+    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_6b3b62e7_1.png",
+    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_76f17518_3.png",
+    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_a6927bd0_0.png",
+    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_b041486b_1.png",
+    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_bc2920e6_1.png",
+    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_ebb5fd08_0.png",
+    "images|docs/get-started/capabilities/thinking.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/thinking_files/output_0f334997_1.png",
+    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/ar_vs_diffusion.png",
+    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/cond_overview-1.png",
+    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/md_forward_reverse_diffusion.png",
+    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/patch_encoder.png",
+    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/usd_forward_reverse_diffusion.png",
+    "images|docs/models/diffusiongemma/inference.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf_files/output_Gsxfo7TuN0Vu_0.png",
+    "images|docs/models/functiongemma/fine-tuning.md|hotlinked image: https://ai.google.dev/static/gemma/docs/functiongemma/finetuning-with-functiongemma_files/output_6JBty2rq05Jz_0.png",
+    "images|docs/models/functiongemma/formatting.md|hotlinked image: https://ai.google.dev/gemma/docs/images/functiongemma_flow.png",
+    "images|docs/models/gemma-3n/index.md|hotlinked image: https://ai.google.dev/static/gemma/docs/images/gemma-3n-parameters.png",
+    "images|docs/models/gemma-4/model-card.md|hotlinked image: https://ai.google.dev/static/gemma/images/gemma4_banner.png",
+    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/audio/roses-are.wav",
+    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/thinking",
+    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision",
+    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n",
+    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
+    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/audio",
+    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/thinking",
+    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision",
+    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
+    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
+    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/images/cat.png",
+    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/images/kitchen.jpg",
+    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/images/surprise.png",
+    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
+    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
+    "links|docs/get-started/capabilities/text.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
+    "links|docs/get-started/capabilities/text.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
+    "links|docs/get-started/capabilities/text.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
+    "links|docs/get-started/capabilities/thinking.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
+    "links|docs/get-started/capabilities/thinking.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
+    "links|docs/get-started/capabilities/thinking.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
+    "links|docs/get-started/capabilities/video.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
+    "links|docs/get-started/capabilities/video.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
+    "links|docs/get-started/capabilities/video.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
+    "links|docs/get-started/prompt-formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure",
+    "links|docs/get-started/quickstart.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/datagemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/diffusiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma/explained",
+    "links|docs/models/diffusiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf",
+    "links|docs/models/diffusiongemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma/explained",
+    "links|docs/models/diffusiongemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma",
+    "links|docs/models/embeddinggemma/fine-tuning.md|link to upstream docs: http://ai.google.dev/gemma/docs/embeddinggemma/model_card#prompt_instructions",
+    "links|docs/models/embeddinggemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers",
+    "links|docs/models/embeddinggemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers",
+    "links|docs/models/embeddinggemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers",
+    "links|docs/models/embeddinggemma/inference.md|link to upstream docs: http://ai.google.dev/gemma/docs/embeddinggemma/model_card#prompt_instructions",
+    "links|docs/models/embeddinggemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers",
+    "links|docs/models/embeddinggemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
+    "links|docs/models/embeddinggemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma",
+    "links|docs/models/functiongemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma",
+    "links|docs/models/functiongemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/mobile-actions",
+    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure",
+    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
+    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma",
+    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/images/functiongemma_flow.png",
+    "links|docs/models/functiongemma/full-sequence.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
+    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
+    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices",
+    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/function-calling-with-hf",
+    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/mobile-actions",
+    "links|docs/models/functiongemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
+    "links|docs/models/functiongemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma",
+    "links|docs/models/functiongemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma",
+    "links|docs/models/gemma-1/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/gemma-2/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/gemma-3/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/huggingface_inference#audio",
+    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/huggingface_inference#vision",
+    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#conditional-parameter",
+    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#matformer",
+    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#ple-caching",
+    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/get_started",
+    "links|docs/models/gemma-3n/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n",
+    "links|docs/models/gemma-3n/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#parameters",
+    "links|docs/models/gemma-3n/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n},",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/audio",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/thinking",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/image",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/video",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core#previous-models",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_4",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/get_started",
+    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/mtp/overview",
+    "links|docs/models/gemma-4/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/paligemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
+    "links|docs/models/paligemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma",
+    "links|docs/models/paligemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/paligemma/inference-with-keras",
+    "links|docs/models/paligemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/get_started#models-list",
+    "links|docs/models/paligemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions##prompt_task_syntax",
+    "links|docs/models/paligemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#evaluation_approach",
+    "links|docs/models/paligemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
+    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#evaluation_approach",
+    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
+    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
+    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure",
+    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure#system-instructions",
+    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma#categories",
+    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/inference-with-keras",
+    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions#prompt_task_syntax",
+    "links|docs/models/recurrentgemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
+    "links|docs/models/recurrentgemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/setup",
+    "links|docs/models/recurrentgemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/recurrentgemma/model_card",
+    "links|docs/models/recurrentgemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
+    "links|docs/models/recurrentgemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/setup",
+    "links|docs/models/recurrentgemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#implementation_information",
+    "links|docs/models/recurrentgemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#model_data",
+    "links|docs/models/recurrentgemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/3n",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/business-email-assistant",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/codegemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/datagemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-aps",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma_scope",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/mtp/overview",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/model-card-2",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/personal-code-assistant",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma",
+    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning",
+    "links|docs/models/shieldgemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/shieldgemma/model_card",
+    "links|docs/models/shieldgemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/shieldgemma/model_card_2",
+    "links|docs/models/shieldgemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
+    "links|docs/models/shieldgemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
+    "links|docs/models/shieldgemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma",
+    "links|docs/models/shieldgemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
+    "links|docs/models/shieldgemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma",
+    "links|docs/models/shieldgemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma/model_card_2#harm-types",
+    "orphans|docs/get-started/capabilities/vision.md|page exists but is not in nav",
+    "orphans|docs/get-started/index.md|page exists but is not in nav",
+    "orphans|docs/models/index.md|page exists but is not in nav",
+    "orphans|docs/tools/index.md|page exists but is not in nav",
+    "stubs|docs/community/discord.md|stub: 46 chars of content (minimum 400)",
+    "stubs|docs/community/gemma-skills.md|stub: 62 chars of content (minimum 400)",
+    "stubs|docs/community/gemmaverse.md|placeholder link: example.com",
+    "stubs|docs/cookbook.md|stub: 395 chars of content (minimum 400)",
+    "stubs|docs/demos/concurrent.md|stub: 52 chars of content (minimum 400)",
+    "stubs|docs/deployment/google-cloud.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/deployment/huggingface.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/deployment/langchain.md|stub: 59 chars of content (minimum 400)",
+    "stubs|docs/deployment/litert.md|stub: 52 chars of content (minimum 400)",
+    "stubs|docs/deployment/llamacpp.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/deployment/lmstudio.md|stub: 33 chars of content (minimum 400)",
+    "stubs|docs/deployment/mlx.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/deployment/mobile.md|stub: 40 chars of content (minimum 400)",
+    "stubs|docs/deployment/ollama.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/deployment/qat.md|stub: 69 chars of content (minimum 400)",
+    "stubs|docs/deployment/sglang.md|stub: 50 chars of content (minimum 400)",
+    "stubs|docs/deployment/vllm.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/deployment/web.md|stub: 39 chars of content (minimum 400)",
+    "stubs|docs/get-started/capabilities/vision.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/index.md|stub: 0 chars of content (minimum 400)",
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+    "stubs|docs/tools/model-selector.md|stub: 58 chars of content (minimum 400)",
+    "stubs|docs/tuning/distributed.md|stub: 45 chars of content (minimum 400)",
+    "stubs|docs/tuning/full-finetune.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/tuning/keras-lora.md|stub: 36 chars of content (minimum 400)",
+    "stubs|docs/tuning/overview.md|stub: 54 chars of content (minimum 400)",
+    "stubs|docs/tuning/qlora.md|placeholder text: 'Content coming soon'",
+    "stubs|docs/tuning/vision-qlora.md|stub: 45 chars of content (minimum 400)",
+    "stubs|docs/tutorials/visual-guides/diffusiongemma.md|stub: 50 chars of content (minimum 400)",
+    "stubs|docs/tutorials/visual-guides/gemma-4-12b.md|stub: 54 chars of content (minimum 400)",
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+    "stubs|docs/tutorials/visual-guides/mtp.md|stub: 66 chars of content (minimum 400)"
+  ]
+}
diff --git a/PLAN.md b/PLAN.md
index bddc6a0..b6982f5 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -1,143 +1,647 @@
-# Gemma docs port
-
-I want to create zensical documentation (see https://github.com/zensical/zensical and https://zensical.org/docs/get-started/) out of the Gemma 4 documentation that can originally be found here: https://ai.google.dev/gemma/docs
-
-The goal is to create a documentation experience that goes beyond merely communicating factual data. We want a developer experience that makes users want to use Gemma as much as possible. An experience that drives curiosity and makes newcomers feel welcome to the Gemma ecosystem. Likewise, it should give partners additional methods for sharing their content/blogs/demos/etc. 
-What we are looking for is a platform that creates a developer experience that makes Gemma 4 an easy-to-use solution for all types of developers. With interactive tutorials, animated explainers, videos, demos, etc., the documentation should contain a wealth of information without being overwhelming. 
-
-## ToC
-
-The idea is to port (part off) the documentation over and create an entirely new interface / user experience for the documentation. I'm thinking of the following nav bar at the top:
-
-* Home
-* Getting Started
-* Models
-* Tutorials
-* Deployment
-* Tuning
-* Community
-* Tools
-
-With the following subsections:
-
-* Getting Started
-  * Overview
-  * Quickstart
-  * https://ai.google.dev/gemma/docs/capabilities/audio
-  * https://ai.google.dev/gemma/docs/capabilities/text/basic
-  * https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4
-  * https://ai.google.dev/gemma/docs/capabilities/vision
-  * https://ai.google.dev/gemma/docs/capabilities/vision/image
-  * https://ai.google.dev/gemma/docs/capabilities/vision/video
-  * https://ai.google.dev/gemma/docs/capabilities/thinking
-* Tutorials
-  * A Visual Guide to Gemma 4: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4
-  * A Visual Guide to Gemma 4 12B: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4-12b
-  * A Visual Guide to MTP for Gemma 4: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4
-  * A Visual Guide to DiffusionGemma 4: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-diffusiongemma
-* Models
-  * Gemma 4: https://ai.google.dev/gemma/docs/core/model_card_4
-  * Gemma 3: https://ai.google.dev/gemma/docs/core/model_card_3
-  * Gemma 2: https://ai.google.dev/gemma/docs/core/model_card_2
-  * Gemma: https://ai.google.dev/gemma/docs/core/model_card
-  * EmbeddingGemma: https://ai.google.dev/gemma/docs/embeddinggemma/model_card
-  * DiffusionGemma: https://ai.google.dev/gemma/docs/diffusiongemma/model_card
-  * FunctionGemma: https://ai.google.dev/gemma/docs/functiongemma/model_card
-  * PaliGemma: https://ai.google.dev/gemma/docs/paligemma/model_card
-  * ShieldGemma: https://ai.google.dev/gemma/docs/shieldgemma/model_card
-* Deployment
-  * LMStudio
-  * llama.cpp
-  * vLLM
-  * LiteRT
-  * SGLang
-  * HuggingFace
-  * Ollama
-* Tuning
-  * https://ai.google.dev/gemma/docs/tune
-  * https://ai.google.dev/gemma/docs/core/huggingface_text_finetune_qlora
-  * https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora
-  * https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetune
-* Community
-  * https://deepmind.google/models/gemma/gemmaverse/
-  * https://ai.google.dev/gemma/docs/discord
-* Ecosystem
-* Demos
-  * Concurrent: https://github.com/google-gemma/cookbook/tree/main/apps/concurrent
-* Tools
-  * (V)RAM calculator
-  * Model Selector
-  * Gemma Skills: https://github.com/google-gemma/gemma-skills
-
-
-For all the google.dev links you can simply append them with .md.txt to get the raw file. 
-
-## Requirements
-
-
-
-### Agent-first
-
-The documentation should be easily accessible for any Agent that you point at it but in particular the Gemma 4 models (with E2B as a potential in-browser variant). 
-
-* **Every page is a skill**  
-  * Yaml frontmatter contains a summary (larger than what is found in llms.txt) but also metadata for the agent, like total number of tokens, code languages on the page, etc.  
-  * Llms.txt is then a skill to traverse the website, so the agent knows how to load the frontmatter first of a given page before reading the entire thing
-
-**NOTE**: The current documentation site does have:
-
-* [https://ai.google.dev/gemma/docs/llms.txt](https://ai.google.dev/gemma/docs/llms.txt)  
-* [https://ai.google.dev/gemma/docs/core.md.txt](https://ai.google.dev/gemma/docs/core.md.txt) 
-
-* **Use Gemma to traverse the Docs**  
-  * Using transformers.js, we can add a chatbot to the site that allows for easily finding information
-
-### Techstack
-
-* Techstack  
-  * [Zensical](https://github.com/zensical/zensical), [ReadtheDocs](https://about.readthedocs.com/), etc.  
-  * ReadtheDocs POC  
-    * [https://gemma-cookbook.readthedocs.io/en/latest/](https://gemma-cookbook.readthedocs.io/en/latest/)  
-    * [Testing branch](https://github.com/bebechien/gemma-cookbook/tree/read-the-doc)  
-    * [Converting script](https://user.git.corp.google.com/bebechien/gemma-doc/+/refs/heads/main/convert_md.py) (devsite markdown \-\> read-the-docs format)  
-    * [Juyeong Ji](mailto:bebechien@google.com) to explore ideal toc with this tool  
-    * Theme? E.g., [Qwen](https://qwen.readthedocs.io/en/latest/)
-
-### Tutorials / Demos
-
-Demos come in various formats and would benefit from having an overview where users can easily track the cool demos and tutorials to play with. 
-
-* Notebook tutorials  
-* GitHub repositories  
-* [Juyeong Ji](mailto:bebechien@google.com) turtle-gemma / AIventure to readthedocs  
-* Interactive applications (HF space)  
-* Interactive applications on the docs itself (js-based VRAM calculator for instance)  
-  * Great example is: [https://www.philschmid.de/how-managed-agents-work](https://www.philschmid.de/how-managed-agents-work) 
-
-### Resources
-
-We’ve created many resources with various launches (including images and videos) that are not consolidated in a single place. We propose to add a page with:
-
-* Visual Guides  
-* Resources for “train the trainer” (see [resources](https://critique.corp.google.com/cl/946846636) and [page](https://gemma-ttt.com/))  
-* Videos
-
-### Ecosystem
-
-The ecosystem is what drives adoption of Gemma. The current documentation does not have a single page giving an overview of partners, inference tools, etc. We want to include everyone that we love working with:
-
-* Open source: HF, llama.cpp, MLX, etc.   
-* 1P: …
-
-### Navigation
-
-As addressed below in more detail, we propose a different navigation mechanism that makes it easier to traverse the documentation for users with more clearly defined headers:
-
-* Getting Started  
-* Models  
-* Tutorials  
-* Deployment  
-* Fine-tuning  
-* Ecosystem  
-* Tools
-
+# Gemma Docs — Port & Re-imagination Plan
+
+Porting and re-imagining the Gemma documentation onto [Zensical](https://github.com/zensical/zensical).
+Source: <https://ai.google.dev/gemma/docs> (append `.md.txt` to any page for raw markdown).
+
+**Goal:** the best open-weights developer experience on the web. Not just accurate reference —
+an experience that makes people *want* to build with Gemma, welcomes newcomers, and gives
+partners a place to showcase their work.
+
+**Gemma 4 is front and center.** All other models are documented, but the docs are
+implicitly about Gemma 4 unless stated otherwise.
+
+---
+
+## Working agreement
+
+- **Bite-sized steps.** Each step below is independently testable and roughly one session of work.
+- **Every step ends with a verification command** and a "done when" condition. Run it before moving on.
+- **Maarten does all commits and pushes.** Agents stage nothing and push nothing — finish the step,
+  report what changed, and stop.
+- **Handover-friendly.** Each step is self-contained enough that a fresh agent can pick it up
+  from this file alone. If you complete a step, tick its box and note anything surprising
+  under *Notes* at the bottom.
+- **Foundation before fun.** Phases 0–2 must be complete and clean before Phase 4 work starts.
+  A WebGPU playground next to 34 "Content coming soon" pages reads as a prototype, not a product.
+
+### Content fidelity — non-negotiable
+
+**Ported content must not change.** The original documentation has been validated for accuracy;
+that validation is only transferable if the words survive the port intact.
+
+- A port is a **format** migration, not an editorial one. Convert devsite syntax to Zensical
+  syntax, fix links and image paths, repair converter damage — and change nothing else.
+- Do **not** reword, tighten, restructure, "improve", modernize, or re-order ported prose.
+  Do not silently correct something that looks wrong: flag it under *Notes* and ask.
+- Code snippets are copied verbatim. Changing a flag or an argument invalidates the validation.
+- **New pages may remix.** Combining snippets from several source pages into a new page is fine
+  and encouraged — but the snippets themselves are still copied, not rewritten.
+- **Start with a straight port.** Port a page as-is first, confirm it renders well, and only
+  then consider whether it needs a companion page. Don't port and redesign in one motion.
+- Every ported page records its source in frontmatter (`source_url`, `ported_at`) so any page
+  can be diffed against upstream later.
+
+Rule of thumb: if a diff against the source `.md.txt` shows a prose change, it needs a reason
+written down.
+
+Local preview:
+
+```bash
+uv run zensical serve
+```
+
+---
+
+## Current state (audit, Aug 2026)
+
+| Metric | Value |
+|---|---|
+| Markdown files | 80 |
+| Nav entries | 76 |
+| Real ported content (>1.5 KB) | 44 |
+| One-line stubs (<250 B) | 34 |
+| Pages with frontmatter | 9 / 80 |
+| Links still pointing at `ai.google.dev/gemma/docs` | 122 |
+| Images hotlinked to `ai.google.dev/static` | 18 (0 vendored) |
+| Orphan pages (exist, not in nav) | 4 |
+| Orphan stylesheet (exists, not in `extra_css`) | 1 (`custom.css`) |
+
+Known defects:
+
+- Malformed code fences from the notebook converter — e.g. `docs/models/gemma-4/index.md:117`
+  wraps prose in a ```` ```python ```` fence mid-sentence.
+- 7 prompt-template blocks tagged `python` that are plain text.
+- ~16 unconverted devsite `Note:` / `Key Point:` / `Caution:` callouts.
+- `docs/models/paligemma/fine-tuning.md` is 435 KB (base64 images inlined by the converter).
+- `docs/community/gemmaverse.md` still ships `example.com` placeholder cards.
+- Modality data disagrees across `quickstart.md`, `models/index.md:14`, and `models/gemma-4/index.md:45`.
+
+---
+
+## Target navigation
+
+Eight top-level tabs. Capabilities stay under **Get started** — they are fundamental Gemma 4
+capabilities and belong in one place where the full picture is visible.
+
+```
+Home
+Get started    Quickstart · Install & setup · Choose your model
+               Prompt & chat format
+               Capabilities: Text · Thinking · Function calling
+                             Vision (Image, Video) · Audio
+                             Multi-Token Prediction · Long context
+Models         Gemma 4 (overview, model card, benchmarks)
+               Variants: Gemma 3n · DiffusionGemma · FunctionGemma
+                         EmbeddingGemma · PaliGemma · ShieldGemma · CodeGemma
+               Previous generations: Gemma 3 · 2 · 1
+               Research: RecurrentGemma · DataGemma · Gemma Scope
+               Releases
+Run            Local:  Ollama · llama.cpp · LM Studio · MLX · Transformers
+                       LiteRT · WebGPU · Mobile
+               Serve:  vLLM · SGLang · TGI · Google Cloud (GKE / Cloud Run)
+               Quantization & QAT · Speculative decoding
+Fine-tune      Overview · QLoRA · Full fine-tune · Vision QLoRA
+               Keras LoRA · Distributed · Dataset prep · Evaluation
+Learn          Visual guides · Tutorials · Notebooks · Videos
+Demos          Interactive tools · Apps & Spaces
+Ecosystem      Partners · Gemmaverse · Discord · Skills · Contribute
+```
+
+**Models stays model-card-centric.** It closely mirrors the upstream structure on purpose —
+model cards are canonical reference and shouldn't be creatively restructured. The improvement
+is in presentation, consistency, and cross-linking, not reorganization.
+
+**Demos absorbs Tools.** "Interactive tools" = things we built (VRAM calculator, model selector,
+tokenizer playground). "Apps & Spaces" = embedded HF Spaces and showcase apps. This gives Demos
+real substance instead of one stub page.
+
+Decisions made: no "Migrating from Gemma 3 → 4" page (users swap one model string).
+Visual guides get fully ported pages, not outbound links.
+
+---
+
+## Design system
+
+Standardized components throughout — styled boxes, frames, and animations reused everywhere
+rather than invented per page.
+
+**Official Gemma palette:**
+
+| Token | Hex | Use |
+|---|---|---|
+| `--gemma-blue` | `#3186FF` | Primary — links, active nav, primary buttons |
+| `--gemma-blue-deep` | `#346BF1` | Hover/pressed states, gradient anchor |
+| `--gemma-blue-light` | `#4FA0FF` | Accents, highlights, dark-mode primary |
+| `--gemma-white` | `#FFFFFF` | Light surface |
+| `--gemma-ink` | `#202124` | Dark surface, body text on light |
+
+All new CSS derives from these. No ad-hoc hex values outside the token file.
+
+---
+
+## Phase 0 — Foundation: make the existing content correct
+
+*Nothing here is glamorous. Everything else compounds on top of it, and CI checks stop the
+debt from coming back.*
+
+### Step 0.1 — Repo hygiene and CI guardrails ✅
+- [x] `scripts/lint_docs.py` with 9 checks: `links`, `images`, `internal`, `stubs`,
+      `frontmatter`, `orphans`, `fences`, `admonitions`, `size`.
+- [x] Baseline mechanism (`.lintbaseline.json`) so CI fails only on *new* violations while the
+      backlog is burned down. **Shrinking the baseline to zero is the definition of done for Phase 0.**
+- [x] `.github/workflows/lint.yml` — runs on push, PR, and dispatch. Stdlib-only, no install step.
+- [x] Deleted `docs/stylesheets/custom.css` (dead: not in `extra_css`, fully superseded by
+      `layout.css`, and would have conflicted on `.md-grid` max-width and sidebar width).
+- **Verify:** `uv run python scripts/lint_docs.py --baseline` → exit 0.
+      `uv run python scripts/lint_docs.py` → full 300-finding backlog.
+- **Done when:** ✅ regression test confirmed — adding an upstream link exits 1, reverting exits 0.
+
+**Baseline at 2026-08-03 (the Phase 0 burn-down list):**
+
+| Check | Count | Notes |
+|---|---|---|
+| `frontmatter` | 81 | 71 pages with no block + missing keys elsewhere |
+| `links` | 122 | 5 of these are inside code blocks |
+| `images` | 52 | 18 hotlinked, 34 inline base64 (all in `paligemma/fine-tuning.md`) |
+| `stubs` | 33 | |
+| `fences` | 7 | prompt templates tagged `python` |
+| `orphans` | 4 | |
+| `size` | 1 | `paligemma/fine-tuning.md` at 435 KB |
+| `internal` | 0 | all relative links resolve |
+| `admonitions` | 0 | **converter already handles these correctly** — 36 converted |
+| **Total** | **300** | |
+
+Useful commands:
+
+```bash
+uv run python scripts/lint_docs.py --list
+```
+
+```bash
+uv run python scripts/lint_docs.py --check links,images
+```
+
+```bash
+uv run python scripts/lint_docs.py --write-baseline
+```
+
+### Step 0.2 — Fix the notebook converter ✅
+- [x] **Wrapped list continuations** no longer eaten as code. Devsite wraps long bullets with an
+      indented continuation line; the 4-space rule fenced it as `python`. The guard meant to
+      prevent this (`lines[i-1].strip().startswith("    ")`) could never fire — `.strip()`
+      removes the very indentation it tests. Continuations are now rejoined onto their bullet,
+      which restores the original sentence exactly (markdown renders `foo\n  bar` in a list as
+      `foo bar`, so nothing about the content changes).
+- [x] **Language detection** gained a `text` fallback: `bash` → `python` → `text`. Detection now
+      uses Python *syntax* signals, deliberately not bare English words like "for"/"with"/"from",
+      which is why prompt templates were being classed as code.
+- [x] **Indented bullets no longer split a block.** A bullet may not *start* a block (that would
+      eat indented lists), but once a block is underway an indented bullet is block content —
+      previously one ASR prompt became a fence plus orphaned bullets.
+- [x] **Base64 extraction** via `--assets-dir` / `--assets-url`. Anchored on `![...](...)` so a
+      data URI inside real code is left alone.
+- [x] Golden tests: `scripts/test_convert_notebook.py` + 4 fixtures in `scripts/fixtures/`,
+      each a minimised reproduction of a real defect. 11 tests, wired into CI.
+- **Verify:** `uv run python scripts/test_convert_notebook.py` → 11 passing.
+- **Done when:** ✅ re-converting `core.md.txt` produces the QAT bullets intact with **zero**
+      python fences; `audio.md.txt` yields 8 `python`, 2 `bash`, 2 `text` fences, both ASR
+      prompts correctly `text`.
+
+**Content-fidelity check** (word multiset, source vs. output):
+
+| Source | Dropped | Verdict |
+|---|---|---|
+| `core.md.txt` | `NOTE` ×1 | the `> [!NOTE]` → `!!! Note` conversion. Additions are cookbook-card markup. |
+| `audio.md.txt` | 111, all badge/metadata | `colab_logo`, `notebook-site-button.png`, `View`, `Run`, `<br>` — the intended stripping. |
+
+**Zero prose lost in either.** Regenerate goldens with `--update`, then review the diff —
+those files are the specification.
+
+```bash
+uv run python scripts/test_convert_notebook.py
+```
+
+### Step 0.3 — Re-port Models, targeted repair for Get Started
+
+Two different strategies, because the two sections have different histories:
+
+- **Models is a straight port.** No hand-authoring, so re-converting is safe and correct. ✅ Done.
+- **Get Started is hand-edited and reviewed.** `quickstart.md` has a hand-authored model table,
+  the `npx skills add` admonition, and `hide: toc` frontmatter — none of it in upstream's
+  `get_started.md.txt`. Re-converting would destroy that. Repair defects in place.
+
+#### 0.3a — Porting infrastructure ✅
+- [x] `scripts/sources.toml` — manifest mapping each ported page to its upstream source.
+      **A page absent from the manifest must never be re-converted.** Also the origin of the
+      `source_url` frontmatter in Step 3.1.
+- [x] `scripts/check_fidelity.py` — word-multiset comparison proving a conversion changed markup
+      and nothing else. Compares *source minus what the converter is designed to drop* vs. output,
+      reusing the converter's own `is_badge_line` rather than a parallel word blocklist, so the
+      two can't drift apart.
+- [x] `scripts/port_docs.py` — batch porter. **Fidelity-gated: a page that drops prose is never
+      written.** Caches sources under `.sources/` (gitignored).
+- [x] New converter fix: **consecutive blank lines no longer split a code block.** Notebook
+      exports routinely leave two blank lines mid-cell; the old lookahead checked only one line
+      ahead, so one function became two fences (found at `paligemma/inference.md`, `binary_mask`).
+- [x] New linter check: two same-language fences separated only by blank lines — the signature of
+      a split block. Found **9 across 7 files**, well beyond the one reported.
+
+#### 0.3b — Models re-ported ✅
+- [x] All **37** manifest pages re-ported. Fidelity: 37 faithful, 0 with dropped prose.
+      Re-running is idempotent (all "unchanged").
+- [x] `paligemma/fine-tuning.md`: **435 KB → 51 KB**, 34 images extracted to `docs/assets/paligemma/`.
+- [x] `models/releases.md`: 58-byte stub → full port (+4.2 KB).
+- **Verify:** `uv run python scripts/port_docs.py --filter models/ --dry-run` → 37 ok, 0 failed.
+
+#### 0.3c — Get Started targeted repair (remaining)
+- [ ] `capabilities/audio.md:90` and `:173` — ASR prompt templates tagged `python`, should be
+      `text`. Change the fence language only.
+- [ ] `capabilities/image.md:315`, `:402`, `:484` — three split code blocks. Rejoin them; the
+      converter now handles this correctly, so its output is the reference.
+- **Verify:** `uv run python scripts/lint_docs.py --check fences` → zero.
+- **Done when:** zero fence findings anywhere.
+
+**Backlog after the Models port — 300 → 290:**
+
+| Check | Was | Now | |
+|---|---|---|---|
+| `images` | 52 | **18** | 34 base64 extracted |
+| `size` | 1 | **0** | cleared |
+| `fences` | 7 | **5** | model-card fences fixed; 5 left, all in Get Started |
+| `stubs` | 33 | **32** | `releases.md` now real content |
+| `links` | 122 | **150** | +28 from `releases.md` gaining content — Step 0.5 rewrites them |
+| `frontmatter` / `orphans` / `internal` / `admonitions` | 81 / 4 / 0 / 0 | unchanged | later steps |
+
+```bash
+uv run python scripts/port_docs.py --filter models/ --dry-run
+```
+
+```bash
+uv run python scripts/check_fidelity.py --manifest --cache-dir .sources
+```
+
+### Step 0.4 — Vendor all images
+- [ ] Download the 18 hotlinked images into `docs/assets/` organized by section.
+- [ ] Rewrite all image references to local paths.
+- [ ] Add dark-mode variants for diagrams where the source is light-only.
+- **Verify:** `uv run zensical build` then grep the `site/` output for `ai.google.dev/static` — zero hits.
+- **Done when:** the site renders fully with no network access to Google.
+
+### Step 0.5 — Rewrite internal links
+- [ ] Map all 122 `ai.google.dev/gemma/docs/*` links to local paths.
+- [ ] For pages not yet ported, point at the eventual local path and let the linter flag it as
+      a known-pending target (don't leave live external links).
+- **Verify:** `uv run python scripts/lint_docs.py --check links`
+- **Done when:** zero outbound links to the site we're replacing.
+
+### Step 0.6 — Model capability matrix as single source of truth
+- [ ] Create `data/models.yml`: every Gemma 4 size with parameters, modalities in/out, context
+      window, memory at each quantization, MTP support, target platform.
+- [ ] Resolve the modality contradiction (`quickstart.md` vs `models/index.md:14` vs
+      `models/gemma-4/index.md:45`) — confirm 26B A4B and 31B are text+image only.
+- [ ] Generate the model tables in `quickstart.md`, `models/index.md`, and
+      `models/gemma-4/index.md` from this file via the macros plugin.
+- **Verify:** change one value in `data/models.yml`, rebuild, confirm all three tables update.
+- **Done when:** no hand-maintained model spec table remains anywhere.
+
+---
+
+## Phase 1 — Design system
+
+### Step 1.1 — Token layer
+
+**Stylesheets stay modular — many small files, not one monolith.** Each file owns one concern,
+opens with a comment saying what it owns and what it must not touch, and stays small enough for
+an agent to read end-to-end before editing. Adding a seventh or eighth stylesheet is the right
+move whenever a new concern appears; growing `custom.css` into a 900-line catch-all is not.
+
+Target layout (`docs/stylesheets/`):
+
+| File | Owns |
+|---|---|
+| `gemma.css` | Palette + spacing/radius/shadow scale. **The only file with raw hex values.** |
+| `layout.css` | Page grid, sidebars, content width |
+| `components.css` | Cards, callouts, badges, figures, walkthroughs |
+| `code.css` | Code blocks, copy buttons, tabs |
+| `tokens.css` | Chat-token badges (`<start_of_turn>` etc.) |
+| `home.css` | Landing page only |
+| `motion.css` | Animations + `prefers-reduced-motion` |
+
+- [ ] Create `gemma.css` with the palette above as CSS custom properties, light and dark
+      variants, and the spacing/radius/shadow scale.
+- [ ] Rebind Zensical's `--md-*` primary/accent variables to the Gemma tokens.
+- [ ] Refactor the existing sheets to consume tokens — remove hardcoded hex values.
+- [ ] Resolve `custom.css` (currently not in `extra_css`): fold into `layout.css` or delete.
+- [ ] Keep `extra_css` in `zensical.toml` in the same order as the table above — token layer first.
+- **Verify:** visual pass on 3 pages in both light and dark mode.
+- **Done when:** `grep -E '#[0-9a-fA-F]{6}' docs/stylesheets/*.css` only matches `gemma.css`,
+  and no stylesheet exceeds ~250 lines.
+
+### Step 1.2 — Standard component library
+- [ ] Card grid (generalize the existing `cookbook-card`).
+- [ ] Callout/box variants: note, tip, warning, "requires GPU", "Gemma 4 only".
+- [ ] Model badge chips (size, modality, quantization) driven by `data/models.yml`.
+- [ ] Framed figure component with caption and light/dark image swap.
+- [ ] Step-by-step / numbered walkthrough component.
+- [ ] Document every component in `docs/contributing/components.md` with live examples.
+- **Verify:** the components page renders all variants correctly in both themes.
+- **Done when:** new pages can be built from components without writing new CSS.
+
+### Step 1.3 — Motion
+- [ ] Standardized entrance/hover animations, `prefers-reduced-motion` respected throughout.
+- [ ] Apply consistently to cards, callouts, and nav.
+- **Verify:** enable reduced-motion in the OS; confirm animations disable.
+
+---
+
+## Phase 2 — Navigation & content completion
+
+### Step 2.1 — Restructure nav
+- [ ] Rewrite the `nav` block in `zensical.toml` to the target structure above.
+- [ ] Resolve the 4 orphans: fold `get-started/index.md` into `quickstart.md`,
+      make `capabilities/vision.md` a real parent page, add `models/index.md` and
+      `tools/index.md` to nav.
+- **Verify:** `uv run python scripts/lint_docs.py --check orphans`
+- **Done when:** zero orphans, zero broken nav entries, 8 top-level tabs.
+
+### Step 2.2 — Local engine pages (the differentiator)
+
+These are *written*, not ported — upstream's versions are three paragraphs each. One step per
+engine so they can be tested and handed over independently.
+
+Template each page must follow: one-line install → one-line run → model tags for every
+size × quant → server mode with OpenAI-compatible endpoint → multimodal examples (gated by
+actual model support) → context/KV-cache flags → troubleshooting → measured tok/s on named hardware.
+
+- [ ] 2.2a Ollama
+- [ ] 2.2b llama.cpp
+- [ ] 2.2c LM Studio
+- [ ] 2.2d MLX
+- [ ] 2.2e Transformers
+- [ ] 2.2f vLLM
+- [ ] 2.2g SGLang
+- [ ] 2.2h LiteRT / mobile / WebGPU
+- [ ] 2.2i Google Cloud (GKE, Cloud Run)
+- **Verify per page:** every command runs as written on a clean machine.
+- **Done when:** the page is genuinely more useful than the upstream equivalent.
+
+### Step 2.3 — Fill remaining stubs
+- [ ] Fine-tune pages (QLoRA, full FT, vision QLoRA, Keras LoRA, distributed).
+- [ ] Quantization & QAT page — move the QAT content currently buried in
+      `models/gemma-4/index.md` into its own page under Run.
+- [ ] Multi-Token Prediction page — port `mtp/overview` + `mtp/mtp`. **Biggest content gap:**
+      MTP is a flagship Gemma 4 feature with zero documentation today.
+- [ ] Ecosystem pages — replace the `example.com` placeholders in `gemmaverse.md` with real
+      data-driven cards.
+- **Verify:** `uv run python scripts/lint_docs.py --check stubs` → zero.
+
+### Step 2.4 — Port remaining upstream pages
+- [ ] CodeGemma (5 pages, currently absent from nav entirely).
+- [ ] `setup`, `run`, `gemma_chat`, `gemma_library`, `keras_inference`, `pytorch_gemma`,
+      `huggingface_inference`, `lora_tuning`, `prompt-structure`.
+- [ ] Application tutorials → Learn: `business-email-assistant`, `personal-code-assistant`,
+      `meme-generator`.
+- [ ] Decide case-by-case on: `gemma-aps`, `spoken-language/task-specific-tuning`,
+      `conversions/hf-to-mediapipe-task`, `mobile-actions`.
+
+---
+
+## Phase 3 — Agent-first infrastructure
+
+*The headline requirement from the original plan, currently ~11% built.*
+
+### Step 3.1 — Frontmatter schema
+- [ ] Define and document the schema: `name`, `title`, `description` (richer than llms.txt),
+      `estimated_tokens`, `prerequisites`, `frameworks`, `code_languages`, `models`, `last_updated`,
+      plus `source_url` and `ported_at` on every ported page (see *Content fidelity*) so any page
+      can be diffed against upstream later.
+- [ ] Backfill all 80 pages.
+- [ ] Enforce in `scripts/lint_docs.py`.
+- **Verify:** linter passes with the frontmatter check enabled.
+
+### Step 3.2 — Computed metadata
+- [ ] Build hook computing `estimated_tokens`, `code_languages`, and `last_updated`
+      automatically so they can't drift.
+- **Verify:** add a code block to a page, rebuild, confirm metadata updates.
+
+### Step 3.3 — Machine-readable surfaces
+- [ ] Generate `/llms.txt` (index with descriptions) and `/llms-full.txt` (everything).
+- [ ] Serve raw markdown at `<page>.md` for every page.
+- **Verify:** `curl` the built site for `llms.txt` and one `.md` endpoint.
+
+### Step 3.4 — Copy-for-agent affordances
+- [ ] "Copy page as Markdown" button in the page header.
+- [ ] "Open in Claude / ChatGPT" links.
+- **Verify:** click the button, paste, confirm clean markdown with frontmatter.
+
+### Step 3.5 — Docs as a skill
+- [ ] Package the docs as an installable skill (`npx skills add ...`) and/or MCP server so
+      agents can query them directly.
+
+---
+
+## Phase 4 — Interactive tools
+
+### Step 4.1 — VRAM & context calculator
+Replace the static table in `docs/tools/vram-calculator.md` with a real calculator.
+
+- [ ] Inputs: model, quantization, context length, batch size, KV-cache dtype, GPU preset
+      (4090, 5090, A100, H100, M-series unified memory, …).
+- [ ] Outputs: fits / doesn't fit with headroom, estimated tok/s, recommended engine,
+      **and the exact copy-paste command for that engine**.
+- [ ] URL-encoded state (`?model=12b&quant=q4_0&gpu=rtx4090&ctx=32k`) so results are shareable.
+- [ ] Reads from `data/models.yml`.
+- **Verify:** results match the memory table in `models/gemma-4/index.md` at matching settings.
+- **Done when:** a shared URL reproduces the exact same state.
+
+### Step 4.2 — Global engine + model switcher
+
+The highest-leverage UX win: pick "MLX" once and the entire site shows MLX; pick "E4B" and
+every snippet on every page rewrites its model ID.
+
+- [ ] Header control for engine and model, persisted in `localStorage`.
+- [ ] Extend Zensical's `content.tabs.link` to sync site-wide.
+- [ ] **Capability-aware gating, driven by `data/models.yml`:** audio examples must not offer
+      26B A4B or 31B (text+image only). When the selected model can't do what the page shows,
+      show an inline explanation and the nearest valid model — never a broken snippet, and
+      never silently swap the user's choice.
+- [ ] Graceful fallback when a model/engine pair has no published weights.
+- **Verify:** select 31B, navigate to `capabilities/audio.md`, confirm the gating message
+      appears and no invalid command is shown.
+- **Done when:** no combination of switcher state produces a command that would fail.
+
+### Step 4.3 — "Choose your model" decision flow
+- [ ] Four questions → recommendation + copy-paste command for the user's engine.
+- [ ] Embeds the VRAM calculator.
+
+### Step 4.4 — Tokenizer & chat-template playground
+- [ ] Paste a conversation → exact `<start_of_turn>` token stream, token count.
+- [ ] Image token budget for a given resolution and aspect ratio.
+- [ ] Reuses the existing `tokens.css` badge styling.
+- Pairs directly with `get-started/prompt-formatting.md`.
+
+---
+
+## Phase 5 — Demos & embeds
+
+### Step 5.1 — First HF Space embed (spike)
+- [ ] Embed <https://huggingface.co/spaces/google/gemma4_vision_token_budget> on a Demos page.
+- [ ] Lazy-load (Spaces are heavy) and design a fallback for sleeping Spaces — a dead iframe
+      looks worse than no iframe.
+- [ ] Evaluate: sizing, theme match, mobile behavior, load time.
+- **Verify:** view on mobile and desktop, light and dark. Test with the Space asleep.
+- **Done when:** we know whether to standardize on iframe or `<gradio-app>`, documented here.
+
+### Step 5.2 — Demos section build-out
+- [ ] Concurrent inference demo (currently a stub).
+- [ ] Additional Spaces once 5.1 establishes the pattern.
+
+---
+
+## Phase 6 — Learn
+
+### Step 6.1 — Visual guides, fully ported
+Not outbound links — complete pages hosted here. Maarten provides raw SVG for all images.
+
+- [ ] 6.1a A Visual Guide to Gemma 4
+- [ ] 6.1b A Visual Guide to Gemma 4 12B
+- [ ] 6.1c A Visual Guide to MTP for Gemma 4
+- [ ] 6.1d A Visual Guide to DiffusionGemma
+- [ ] Establish the reading experience: wide layout, framed figures, dark-mode SVG handling,
+      canonical link back to the newsletter.
+- [ ] **Contextual cross-linking** — link the MoE guide from the 26B A4B section, the
+      Quantization guide from QAT, the Reasoning LLMs guide from `capabilities/thinking.md`.
+      Related guides worth linking (not porting): MoE, Quantization, Reasoning LLMs, LLM Agents.
+- **Verify:** SVGs render crisply at all widths, legible in both themes.
+
+### Step 6.2 — Notebooks as source of truth
+- [ ] Keep `.ipynb` canonical, render to pages at build time.
+- [ ] Colab / Kaggle / local badges on every tutorial.
+- [ ] Execute notebooks in CI — a "snippets are tested" badge is a real credibility marker.
+
+### Step 6.3 — Task-first entry points
+- [ ] "I want to…" layer: build a RAG app · extract structured JSON · caption images ·
+      classify content · build a tool-using agent. Each is a curated path across existing pages.
+
+---
+
+## Phase 7 — The wow tier
+
+Only after Phases 0–2 are clean.
+
+- [ ] **In-browser playground** — E2B q4 via WebGPU/transformers.js, running in the docs page.
+      ~1.5–2 GB download, so explicit opt-in and clear progress UI. "Try Gemma 4 with zero
+      install, right here" is unmatched for an open-weights model.
+- [ ] **Ask Gemma about Gemma** — docs chatbot on transformers.js or an HF Space endpoint.
+      Doubles as a live demo and a search upgrade.
+- [ ] **Interactive Gemma 4 architecture explainer** — clickable/scrollytelling coverage of PLE
+      (why E2B's memory exceeds its effective params), MoE routing on 26B A4B, MTP draft heads,
+      and the 12B's encoder-free linear projections. These four concepts are exactly what
+      confuses people, and prose isn't fixing it.
+- [ ] **MTP animated explainer** — draft-vs-verify animation with an acceptance-rate slider
+      showing the resulting speedup.
+- [ ] **DiffusionGemma denoising animation** — text unmasking step by step.
+- [ ] **Community throughput leaderboard** — measured tok/s per (model × quant × engine ×
+      hardware), PR-based submissions. Exists nowhere authoritative; high maintenance, high payoff.
+- [ ] **Cost calculator** — GPU $/hr → $/1M tokens, self-host vs. API.
+
+---
+
+## Phase 8 — Maintainer skill & handover
+
+*Finalized once the docs are done, but **drafted incrementally** — every phase that establishes
+a convention adds its section immediately, while the reasoning is fresh. A skill written from
+scratch at the end will miss the things that were obvious in the moment.*
+
+### Step 8.1 — `gemma-docs` authoring skill
+A skill that lets any agent update these docs correctly without rediscovering the conventions.
+Must cover:
+
+- [ ] **Content fidelity rules** — the non-negotiable section above, stated first and plainly.
+- [ ] **How to port a page** — fetch `.md.txt`, run `scripts/convert_notebook.py`, what the
+      converter handles vs. what needs manual repair, required frontmatter including `source_url`.
+- [ ] **How to add a page** — where it goes in nav, frontmatter schema, which components to use.
+- [ ] **The design system** — which stylesheet owns what, the token layer, the rule that raw hex
+      only lives in `gemma.css`, when to add a new stylesheet instead of growing one.
+- [ ] **Component catalog** — every card/callout/badge/figure with copy-paste examples.
+- [ ] **`data/models.yml`** — that it's the single source of truth, what reads from it
+      (tables, VRAM calculator, switcher gating), and why hand-editing a model table is a bug.
+- [ ] **Verification** — `scripts/lint_docs.py` checks, how to run the build, what CI enforces.
+- [ ] **Gotchas** — Zensical version quirks, features in `zensical.toml` that aren't implemented,
+      converter edge cases, anything accumulated in *Notes* below.
+- [ ] **What not to do** — don't reword ported prose, don't hotlink images, don't add hex outside
+      the token file, don't commit or push (that's Maarten's).
+- **Verify:** hand a fresh agent the skill and a page to port. It should succeed with no
+  additional context and pass the linter first try.
+- **Done when:** that test passes.
+
+### Step 8.2 — Contributor docs
+- [ ] `docs/contributing/` covering the same ground for humans, linking the components page
+      from Step 1.2.
+
+---
+
+## Open decisions
+
+- Whether visual-guide diagrams can also appear inline in the reference docs (with attribution),
+  which would be a much stronger integration than standalone guide pages alone.
+- Whether the throughput leaderboard is worth its ongoing maintenance cost.
+
+---
+
+## Notes
+
+*Agents: record surprises, deviations, and anything that changes the plan here.*
+
+- Zensical version pinned via `uv.lock` (`zensical>=0.0.51`). Some Material features listed in
+  `zensical.toml` may not be implemented yet — verify before relying on them.
+
+**2026-08-03 (Step 0.1)**
+
+- ⚠️ **Two workflows both deploy to GitHub Pages on push to `main`** — `docs.yml` and
+  `deploy.yml`. They will race; `deploy.yml` has a `concurrency: pages` group, `docs.yml`
+  doesn't. They also install zensical differently (`docs.yml` → plain PyPI, `deploy.yml` → uv +
+  `pypi.zensical.org`). **Needs a decision from Maarten** — `deploy.yml` looks like the intended
+  one. Left both in place rather than deleting unilaterally.
+- **Correction to the original audit:** devsite callouts are *not* an issue. The converter
+  already converts them (36 admonitions present, 0 unconverted). The earlier "~16" figure came
+  from a combined grep pattern that also matched `.md.txt` and `colab.research` strings.
+  Step 0.2's admonition work is therefore already done — verify with a fixture rather than rewrite.
+- `check_images` and `check_internal` scan **outside code blocks only**. Markdown-like syntax
+  inside snippets is code, not a reference (e.g. an f-string `{render_inline(image, resize=(64,64))}`
+  in `paligemma/fine-tuning.md:515` was a false positive).
+- `check_links` scans **everywhere**, including code blocks — 5 upstream links live inside
+  snippets. Some may be intentional (e.g. downloading a sample asset); review case by case in Step 0.5.
+- `Finding.key()` deliberately excludes line numbers so edits above a known finding don't
+  resurrect it as "new".
+- The 34 base64 images are all in `paligemma/fine-tuning.md`. Fixing that one file clears the
+  `size` check and 34 of the 52 `images` findings at once — good first target for Step 0.3/0.4.
+
+**2026-08-03 (Step 0.2)**
+
+- ⚠️ **Pages have been hand-edited since their port.** There is currently no way to tell a
+  straight port from a hand-edited page automatically — hence `--divergence` in Step 0.3 and
+  `source_url` frontmatter in Step 3.1. Until one of those lands, **assume any page may contain
+  hand-work and never re-convert in bulk.**
+- **Get Started is reviewed and approved by Maarten.** Repair defects in place; do not re-convert.
+- Only 6 files have converter-class defects. The other 294 backlog findings are links, images,
+  frontmatter, and nav — all outside the converter's remit.
+- The converter's `text` fallback matters more than it looks: an early version of the Python
+  detector matched `\bfor\b` inside "for formatting the answer", which is exactly how prose got
+  classified as code. Keep the detector on *syntax* signals, never bare English words.
+
+**2026-08-03 (Step 0.3)**
+
+- **`scripts/sources.toml` is the authority on what may be re-converted.** Absent from the
+  manifest = hand-authored or hand-edited = never re-convert. Add a page to it only after
+  confirming it is a straight port.
+- Fidelity checking must compare against *source minus designed drops*, not raw source.
+  A naive comparison flags every Colab/Kaggle badge as lost prose — that produced 10 false
+  failures before `strip_designed_drops` reused the converter's own `is_badge_line`.
+- `check_fidelity.py` and `port_docs.py` **share `cache_name()`**. They must: keying the source
+  cache on the bare filename collided every `index.md` across all model directories onto one
+  entry, making the two tools disagree (0 failures vs 26).
+- `--dry-run` extracts images to a temp directory. Without that it wrote 34 files to
+  `docs/assets/` while claiming to change nothing.
+- Two blank lines mid-cell is common in notebook exports — worth checking for whenever a new
+  converter bug is suspected.
diff --git a/docs/assets/paligemma/fine-tuning-01.jpg b/docs/assets/paligemma/fine-tuning-01.jpg
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literal 0
HcmV?d00001

diff --git a/docs/models/diffusiongemma/inference.md b/docs/models/diffusiongemma/inference.md
index a37a63b..c37222d 100644
--- a/docs/models/diffusiongemma/inference.md
+++ b/docs/models/diffusiongemma/inference.md
@@ -1,4 +1,3 @@
-# Generate Output
 
 DiffusionGemma is an experimental open model that explores text diffusion, an exceptionally fast approach to text generation. Based on the 26B (4B active) Mixture-of-Experts (MoE) Gemma 4 architecture, DiffusionGemma generates tokens using discrete diffusion.
 
diff --git a/docs/models/functiongemma/fine-tuning.md b/docs/models/functiongemma/fine-tuning.md
index 816d8c2..3d403d3 100644
--- a/docs/models/functiongemma/fine-tuning.md
+++ b/docs/models/functiongemma/fine-tuning.md
@@ -152,10 +152,8 @@ def search_google(query: str) -> str:
         query: query string
     """
     return "Public Result"
-```
 
 
-```python
 TOOLS = [get_json_schema(search_knowledge_base), get_json_schema(search_google)]
 
 DEFAULT_SYSTEM_MSG = "You are a model that can do function calling with the following functions"
diff --git a/docs/models/functiongemma/formatting.md b/docs/models/functiongemma/formatting.md
index f04f6e0..9ea0639 100644
--- a/docs/models/functiongemma/formatting.md
+++ b/docs/models/functiongemma/formatting.md
@@ -157,7 +157,7 @@ You are a model that can do function calling with the following functions<start_
 
 Next, the user asks a question that requires the tool you just defined.
 
-```python
+```text
 <start_of_turn>user
 Hey, what's the weather in Tokyo right now?<end_of_turn>
 ```
@@ -167,7 +167,7 @@ Hey, what's the weather in Tokyo right now?<end_of_turn>
 The model processes the user's request and the tool definition. It determines it
 needs to call the `get_current_weather` function and outputs the following turn.
 
-```python
+```text
 <start_function_call>call:get_current_weather{location:<escape>Tokyo, Japan<escape>}<end_function_call>
 ```
 
@@ -178,7 +178,7 @@ needs to call the `get_current_weather` function and outputs the following turn.
 You now send the return value from your function back to the model. This turn
 uses the function response tokens.
 
-```python
+```text
 <start_function_response>response:get_current_weather{temperature:15,weather:<escape>sunny<escape>}<end_function_response>
 ```
 
@@ -188,7 +188,7 @@ The model receives the function's result from Turn 4. It now has all the
 information needed to answer the user's original question from Turn 2. It
 processes this new context and generates the final, natural language response.
 
-```python
+```text
 The current weather in Tokyo is sunny with a temperature of 15 degrees Celsius.<end_of_turn>
 ```
 
diff --git a/docs/models/gemma-3n/model-card.md b/docs/models/gemma-3n/model-card.md
index f4875fd..e883d81 100644
--- a/docs/models/gemma-3n/model-card.md
+++ b/docs/models/gemma-3n/model-card.md
@@ -239,10 +239,7 @@ training and development.
     - **Image Data Extraction**: Extract, interpret, and summarize visual data for text communications.
     - **Audio Data Extraction**: Transcribe spoken language, translate speech to text in other languages, and analyze sound-based data.
 - Research and Education
-    - **Natural Language Processing (NLP) and generative model
-```python
-Research**: These models can serve as a foundation for researchers to experiment with generative models and NLP techniques, develop algorithms, and contribute to the advancement of the field.
-```
+    - **Natural Language Processing (NLP) and generative model Research**: These models can serve as a foundation for researchers to experiment with generative models and NLP techniques, develop algorithms, and contribute to the advancement of the field.
     - **Language Learning Tools**: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
     - **Knowledge Exploration**: Assist researchers in exploring large bodies of data by generating summaries or answering questions about specific topics.
 
diff --git a/docs/models/gemma-4/index.md b/docs/models/gemma-4/index.md
index 80918d6..31fb99b 100644
--- a/docs/models/gemma-4/index.md
+++ b/docs/models/gemma-4/index.md
@@ -1,5 +1,3 @@
-# Gemma 4 Overview
-
 [Video](https://www.youtube.com/watch?v=jZVBoFOJK-Q)
 
 Gemma is a family of generative artificial intelligence models and you can
@@ -113,14 +111,8 @@ nearly identically to their high-precision baselines.
 Official QAT collections on Hugging Face
 
 - **[collections/google/gemma-4-qat-q4-0](https://huggingface.co/collections/google/gemma-4-qat-q4-0)**
-    - **Unquantized QAT Checkpoints (`-unquantized` / `-assistant`):** Half-precision weights extracted directly from the QAT pipeline. These are ideal for custom downstream compilation, research, or running speculative decoding using the assistant draft models. *Available for
-```python
-Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.*
-```
-    - **GGUF (`-gguf`):** Checkpoints available for immediate drop-in compatibility across the local LLM ecosystem. *Available for Gemma 4
-```python
-E2B, E4B, 12B, 26B A4B, and 31B.*
-```
+    - **Unquantized QAT Checkpoints (`-unquantized` / `-assistant`):** Half-precision weights extracted directly from the QAT pipeline. These are ideal for custom downstream compilation, research, or running speculative decoding using the assistant draft models. *Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.*
+    - **GGUF (`-gguf`):** Checkpoints available for immediate drop-in compatibility across the local LLM ecosystem. *Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.*
     - **Compressed Tensors (`-w4a16-ct`):** Serialized natively in the `compressed-tensors` standard for optimized, high-concurrency cloud serving. *Available for Gemma 4 E2B, E4B, 12B, and 31B.*
 - **[collections/google/gemma-4-qat-mobile](https://huggingface.co/collections/google/gemma-4-qat-mobile)**
     - **Mobile-Optimized (`-mobile-transformers` / `-mobile-ct`):** Built on a custom `wNa8o8` schema engineered specifically for mobile hardware limits. It utilizes targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize on-device RAM savings without choking edge processors. *Available for Gemma 4 E2B and E4B.*
diff --git a/docs/models/gemma-4/model-card.md b/docs/models/gemma-4/model-card.md
index 65003c9..a0a4f62 100644
--- a/docs/models/gemma-4/model-card.md
+++ b/docs/models/gemma-4/model-card.md
@@ -1,5 +1,3 @@
-# Gemma 4 Model Card
-
 ![Gemma 4 Banner](https://ai.google.dev/static/gemma/images/gemma4_banner.png)
 
 
@@ -214,17 +212,17 @@ Use the following prompt structures for audio processing:
 
 - **Audio Speech Recognition (ASR)**
 
-```python
+```text
 Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
 
 Follow these specific instructions for formatting the answer:
+*   Only output the transcription, with no newlines.
+*   When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.
 ```
-    *   Only output the transcription, with no newlines.
-    *   When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.
 
 - **Automatic Speech Translation (AST)**
 
-```python
+```text
 Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.
 When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.
 ```
diff --git a/docs/models/paligemma/fine-tuning.md b/docs/models/paligemma/fine-tuning.md
index eea47e4..20d7396 100644
--- a/docs/models/paligemma/fine-tuning.md
+++ b/docs/models/paligemma/fine-tuning.md
@@ -427,10 +427,8 @@ train_dataset = big_vision.datasets.jsonl.DataSource(
 val_dataset = big_vision.datasets.jsonl.DataSource(
     os.path.join(DATA_DIR, "data_val10.jsonl"),
     fopen_keys={"image": DATA_DIR})
-```
 
 
-```python
 def train_data_iterator():
   """Never ending iterator over training examples."""
   # Shuffle examples and repeat so one can train for many epochs.
@@ -449,10 +447,8 @@ def train_data_iterator():
         "mask_ar": np.asarray(mask_ar),
         "mask_loss": np.asarray(mask_loss),
     }
-```
 
 
-```python
 def validation_data_iterator():
   """Single iterator over validation examples."""
   for example in val_dataset.get_tfdata(ordered=True).as_numpy_iterator():
@@ -512,28 +508,28 @@ display(HTML(html_out))
 ``` { .no-copy .output }
 Training examples
 ```
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-01.jpg)
 
 a car is parked in front of a building with a green umbrella. the building has a green and white sign, a green and white umbrella, and a white sign with black lettering. there is a tall palm tree and a tall tree. the car is parked next to a yellow car and a black car. the road is grey and the sky is white.
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)
+![](../../assets/paligemma/fine-tuning-02.jpg)
 
 a blue bicycle is parked next to awooden fence. the bike has a black seat, a black kickstand, and a black tire. the fence is brown and the grass is green. there is a small green bush and a small green tree in the background. the bike has a light on the front and a light on the back. the front tire of the bike is on the ground and the back tire is on the fence. the bike has a black pedal and a black pedal on the bike. the bike has a black seat and a black seat on the bike. the bike has a black kickstand and a
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)
+![](../../assets/paligemma/fine-tuning-03.jpg)
 
 a plate of waffles with blueberries and syrup. the waffle is brown. the blueberries are black and are on top of the waffle. the syrup is pouring onto the plate. the plate is white and has a brown rim. the table is white and has a brown wooden fence in the background.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-04.jpg)
 
 a person holds a badminton racket with a shuttlecock attached to it. the racket has a black design, with a white handle and a black net. the shuttlecock is white and red. the person's hand is holding the shuttlecock, and their fingers are gripping the handle. the racket is in the person's hand, and the shuttlecock is in their racket. the person is wearing blue shorts and a red shirt. the background is blurry.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-05.jpg)
 
 a field of sunflowers with a cloudy sky in the background. the sky is blue with white clouds. the flowers are yellow and green, with green leaves on the stems. the sunflower is large and has a yellow center.the flower is in bloom and the petals are yellow.
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)
+![](../../assets/paligemma/fine-tuning-06.jpg)
 
 a purple building with a row of chairs outside. the building has a purple door and a purple window. there is a potted plant in the window and a flower pot on the window sill. the chairs are brown and the table is made of wood. there is a flower box on the wall and a flower pot on the window sill. the street is wet and there is a light on the wall. the sign on the building is orange and the sign on the window is purple. the flowers in the pot are pink and yellow.
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)
+![](../../assets/paligemma/fine-tuning-07.jpg)
 
 a plate of colorful desserts and a cup of coffee. the plate features a variety of sweet treats, including a macaron with a bite taken out, a green macaron, a pink macaron, and a white macaron. the plate is adorned with a white flower on a tree branch and a green leaf on a plant. the coffee cup has a white handle. the table is covered in a white and gold tablecloth.
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)
+![](../../assets/paligemma/fine-tuning-08.jpg)
 
 cinque terre city perched on the side of a towering cliff, overlooking the serene ocean. the sky above is adorned with fluffy white clouds, while the water below mirrors the sky in its tranquility. the buildings below are painted in vibrant hues, ranging from yellow to orange to pink. the hillside is adorned with lush greenery, and the rocky shore provides a picturesque backdrop. the overall atmosphere is serene and captivating, offering a glimpse into the heart of this idyllic town.
 
@@ -705,16 +701,16 @@ step: 15/64   lr: 0.02831   loss: 1.0132
 step: 16/64   lr: 0.02792   loss: 1.2126
 Model predictions at step 16
 ```
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-09.jpg)
 
 a woman's hand rests on a white wall, while her hand is on a white wall. the woman's hand is on the wall, and her hand is on the wall. the woman's hand is on the wall, and her hand is on the wall. the woman's hand is on the wall, and her hand is on the wall. the woman's hand is on the wall, and her hand is on the wall. the woman's hand is on the wall, and her hand is on the wall. the woman's hand is on the wall, and her hand is on the wall.
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)
+![](../../assets/paligemma/fine-tuning-10.jpg)
 
 a woman in a white dress with a red rose on it stands on a white wall with a white bag on the wall. the woman is wearing a white bag on the wall and a white bag on the wall. the woman is wearing a white bag on the wall and a white bag on the wall. the woman is wearing a white bag on the wall and a white bag on the wall. the woman is wearing a white bag on the wall and a white bag on the wall. the woman is wearing a white bag on the wall and a white bag on the wall. the woman is wearing a white bag on the wall and a white
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+![](../../assets/paligemma/fine-tuning-11.jpg)
 
 a person wearing a red blazer with a black belt and a black skirt. the person is wearing a black shirt with a white zipper and a black belt with a white zipper. the person is also wearing a black pants with a white zipper and a black belt with a white zipper. the person is wearing a black shirt with a white zipper and a black belt with a white zipper. the person is wearing a black pants with a white zipper and a black belt with a white zipper. the person is wearing a black shirt with a white zipper and a black belt with a white zipper. the person is wearing a black shirt with a white zipper and
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)
+![](../../assets/paligemma/fine-tuning-12.jpg)
 
 a woman wearing a pink shirt and a pink bag with a white belt. the woman is standing on a gray concrete wall with a gray concrete wall behind her. the woman is wearing a white belt and a white belt on her jeans. the woman is wearing a white belt on her jeans and a white belt on her shirt. the woman is wearing a white belt on her jeans and a white belt on her shirt. the woman is wearing a white belt on her jeans and a white belt on her shirt. the woman is wearing a white belt on her jeans and a white belt on her shirt. the woman is wearing a white belt on her
 
@@ -737,16 +733,16 @@ step: 31/64   lr: 0.01856   loss: 0.6902
 step: 32/64   lr: 0.01778   loss: 0.7054
 Model predictions at step 32
 ```
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-13.jpg)
 
 a woman in a pink dress leans against a white wall, her hand on the wall. the dress is flowing and the sleeves are gathered. the woman's hand is on the wall. the dress is pink and the sleeves are gathered. the woman is wearing a bracelet and a watch. the dress is long and the sleeves are long. the woman is standing next to the wall.
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)
+![](../../assets/paligemma/fine-tuning-14.jpg)
 
 a woman in a white dress with red flowers stands on a pier overlooking the ocean. the dress is long and has a slit in the front. the woman is wearing a straw hat and has a white bag on her shoulder. the dress has a floral pattern and a tie on the front. the woman is standing on the pier and is holding her dress. the dress is flowing in the wind. the woman is wearing a belt on her dress and has a belt on her bag. the dress has a tie on the front and a floral pattern on the front. the woman is wearing a white bag on her shoulder and a white belt on her
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-15.jpg)
 
 a person wears a red blazer with a black belt and a black bag. the bag has a zipper and a silver zipper pull. the person wears a black bra and a black belt. the blazer has a button and a single button. the person wears a black pants and a black bag. the bag has a zipper and a silver zipper pull. the person wears a black belt and a silver zipper pull. the blazer has a single button and a single button. the person wears a black bra and a black belt. the bag has a zipper and a silver zipper pull. the person wears a black pants and a black bag. the bag has
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)
+![](../../assets/paligemma/fine-tuning-16.jpg)
 
 a woman stands on a stone staircase, her hand on the railing. her jeans are blue, and her shirt is pink. her bag is pink and has a strap. her bracelet is silver and has a silver buckle. her shoes are silver and have a silver buckle.
 
@@ -769,16 +765,16 @@ step: 47/64   lr: 0.00638   loss: 0.1287
 step: 48/64   lr: 0.00574   loss: 0.1711
 Model predictions at step 48
 ```
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-17.jpg)
 
 a woman in a pink dress leans against a white wall, her hand on the wall. the dress is flowing and the fabric is sheer. her hand is gripping the wall. the wall is white and has a shadow on it. the woman is wearing a bracelet and a watch.
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)
+![](../../assets/paligemma/fine-tuning-18.jpg)
 
 a woman in a long white floral dress stands on a pier overlooking the ocean. the dress is long and flowing, with a deep v neckline and a slit in the fabric. the woman holds a white woven bag and wears a straw hat. the sky is clear and blue, with a few clouds. the water is calm and blue, with a few waves. the woman's hair is blonde and her nails are painted white.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-19.jpg)
 
 a person wears a red blazer with a black belt and a black skirt. the blazer has a single button on the front. the person wears a white tank top and has their hand in their pocket. the belt is black and has a silver zipper. the bag is a fanny pack and has a zipper on the front. the bag is black and has a zipper on the front. the person is standing next to a green plant.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-20.jpg)
 
 a woman stands on a stone staircase, her hand on her purse. the jeans are blue, and the shirt is pink. the bag is pink and has a strap. the stairs are made of stone, and the wall is made of concrete. the woman is wearing a white cardigan and has a bracelet on her arm. the bag is on her shoulder, and the strap is on her arm. the jeans are ripped, and the knee is on the bag. the woman is standing on a stone staircase.
 
@@ -801,16 +797,16 @@ step: 63/64   lr: 0.00008   loss: 0.1053
 step: 64/64   lr: 0.00002   loss: 0.0730
 Model predictions at step 64
 ```
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-21.jpg)
 
 a woman in a pink dress leans against a white wall, her hand gripping the edge. the dress is flowing and sheer, with long, puffy sleeves. her hand is gripping the wall, and her fingers are curled. the wall is white and has a shadow on it. the woman is standing outside, and the sun is shining on her.
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)
+![](../../assets/paligemma/fine-tuning-22.jpg)
 
 a woman in a long white floral dress stands on a pier overlooking the ocean. the dress is long and flowing, with a deep v neckline and a slit in the fabric. the woman holds a white woven bag and wears a bracelet on her wrist. the sky is clear and blue, with a few clouds. the water is calm and blue, with a few waves. the woman's hair is blonde and her nails are painted white.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-23.jpg)
 
 a person wears a red blazer with a black belt and a black skirt. the blazer has a single button on the front. the person wears a white tank top and has their hand in their pocket. the belt is black and has a silver zipper. the bag is a fanny pack and has a zipper on the front. the bag is black and has a zipper on the front. the person is standing next to a green plant.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-24.jpg)
 
 a woman stands on a stone staircase, her hand on her purse. the jeans are blue, and the fabric is ripped. the shirt is pink, and the buttons are silver. the bag is pink and has a strap. the woman is wearing a white cardigan and a silver bracelet on her left arm. the stairs are made of stone, and the wall is made of concrete. the woman is standing on a step, and the bag is on her arm.
 
@@ -840,34 +836,34 @@ display(HTML(html_out))
 ``` { .no-copy .output }
 Model predictions
 ```
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-25.jpg)
 
 a woman in a pink dress leans against a white wall, her hand gripping the edge. the dress is flowing and sheer, with long, puffy sleeves. her hand is gripping the wall, and her fingers are curled. the wall is white and has a shadow on it. the woman is standing outside, and the sun is shining on her.
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AEhNMJpvmCmlxQA4mmE00uKTeNpNAATTCaa0nNMMlFgPFNbs47O2YwsCvmKRj6Vj/a2n8sOxIQYAPYVsahY3EOlypcNnDqV/KsJLeU8qhPNcOHn7rXmdlVao6jwHM03iq1tJrp1tTuYJnhmAyBXd+ItRutP1m3WG2lmhVg0mwdPQH69q5vwB4MbWphqLStEtu4KbeDuHNeqPFGVgfAy1zub/AIAhI/UVhiZ3mjswnu03ZakWiXE48TTLbAbZrZG+b+EZzn8q7XawUB23MOpxjNeXeCNcVvFVz9tGCsLxxOOc7X/wr0bTnmn86edGR2O0IQcBR0x+dOkmlqTiGnN2JmlWLBc7QSBk1U1YZRdv+swSp+naqU8GqvcSSokSxSrgxuxJGP5cVWM95cWlvIjKGtJxG+8El+zfofxNaXZjYia6u7ySW0twyzoxBZSAVXyx37ZJqpY2NuykOreahKk5wyn0zWb4XvodTvdavRcyxl7pztR+qqSFwfT2rZto/LmlcMxDgH5jk5qXrsU4ODtLc6PRZ1aRoucqoPPcf5Fc745t0stY0/Uw2wXCtaSHHBONy5/I1Y0uUW+qwXJkCrJ+5bnPOePp6fjVj4iQpd+EbyFQTcRKLiMAcjack/lmjeLTHFuM00ePXieTq7tcRhhgsAec8HH1rqdNY2ywOvBUA8Vx9zfLcQJdz52oACo7d/yyKjh8bzG3Jhto5JVbaIQeTkjH5V00GlFJk16U5tzWx7B56soYdCM00y1lWVw8lpGSCpwMg9qn8w7q7Ejzmy55h9aN5xVTec9aernZQ0JMnLn1pMmmbs1JGoOd3AApNpFJXGE0hNBZSMg0wsKBWFJppNBcU0tQAjGhTmN6YXHShGGH57UN6AlqMJNMzkUjTL6ioXnQD7wp6knkUuoNNBFBMxbnqfapoTsxGqBsnOcVqX+jWt54pFvGfLhEgXIPbFaV1plnY3AMLg7Bg/N1rzKThKPMuup3z5ouzNXwPfXVnN9jjjISVt3piu/1CNbeC2CqFOyWV8epXr+tef8AhPU5LrxFBBbWzOrHYWA4X3Jr1XWLaJoGDrkJA/8ASsK0LvQ2pS5dzyHw6klj43spGkZYbmaSMMP4M4/qwr3VQQgDEE45IGK8R1aH7BHY3ETfcuN49QT/APsiva7aZbm1inX7siBx+IzV0vhHWd5XMrVLi+iRkS3BVsgOpLcfTqDXG61rE8KPChYCV1ZXhON3GGYE+mB+dejzBfLbcMjHIrzfVktZb6ZLa3kjS3BBZzwzNgnA7dqcnypsdKPPJKxxWhLL4Z/4mFtNHNpc0/lXO5+YCf73p7etepxtHLAssLq6OMhlOQa82s5o9O1a5iniSWwvozDdQMOH9D9fStbSJn8PqltFM1zZDIjY/wASZ4I9wOPwpws0aYhuU7nW2tuTcRz7wY1bLRsP4uzD3rd1COLUUnHmBkKiIhD1Vuo/mK5iLUY1gWVWzHJOkY/Egf1qfxFqEml6eb11jVW2qXj4Z8HgEdz71kmnJphUg1CMkeTQ2MBvbmzmlKu48vYf9ktz/OsrTPC5+0pKt4kURf8Adz7hwQeAOea3YLiyWc6hfOIzJIy7pB1zz26Hk1leJbCJLgxwL5UdrEHjiiOM8klvauu6VmKlKTvA9LtLhcMCwLJw2PX1q3FPHLKqBhk1xOmX87wIOArRB2dj/rCOoFbCq6kMpYHqDXoUbTiePXTpzsbk13FFKyFhkHFTNNGmn/aN469K5xo2Ykkkk+tGxymwsdvpnitXT0Wpiqj10NuLWIFzuNQXmtK+RGcAjHFZXkUeQPSk6EG7gq07WLEWsvGNrAnHelbW27IarGEelJ5I9KtQiiOeRK2tTEcLUL6xck8DFMaPJ4FNaAmqUY9hOU+4jandMc5rbtdetINJlhkUtcMDg7awzDxUZgzUTpwmrMcako7FaS8uiTmQ1C01ww5kb86uG3yeaabbBrW6M9TBltLtrrftcMDkkLVm3tnmuoopvOYMwGFGST6V7Imi6XcpvjC896bp3h2yj1kSeUrfZzvBx37V48cVStblPUlQqXvzE3hfwvFoqea0SrJjCKDnaD3PvV/WmItrk4z/AKO38xWrn69axtcY/Y7o/wDTuf5j0ri2R1tuT1PNNctJbzw8zQYM0R3hfUDqPrXfeCdWW98JWMpJJQGI+xHT9K5KxfMYRhnIwe/rTvCFw2m6jqmkM5WPP2mEAZz7DPof5URlZtFNXR6T9oimijjmIDTqSEH92uIuLNYFmVY9gaV225zjn/AVXm167vdciNjPueNfszR4ICuhJbP4Vq3CEwqW5bHJ96mu9Ejqw0bannuuWw3MCPlbg0zw/rbajplxpVyVNzav8mSFIOcZGex7j159a2Nbtt8bcVwDy/2X4kt7zgJL8j5HQjv/ACpU5aFVYXZ19nqjE2+msNs6alEdp6kZH8iKPG2rSy6u9rJIfLtl3RIOhJODn35pbmzhiuIPEFvyLU+bLB3z6j2rndd1BNR1T7ahxBO2EZuMhTz+tGjn6l/8u0+xHqDTRaTHKUbDMXjQoCpIOM8/54q5c6ppd+qicSi6S3R5DGB8ykcr+tN/tqbFjZtGsltA2xxsyADnJz1P/wBer15okM2r2n2aGNI5IsSkkgkAgjj9Pxrtivdszz51Nbx3LWmX2n6ppTTW0LRfZDho5BggY/w5rbBEkUcm3bvUHB7VX0Wwe1eeN7dmgL4Eiplj2G7A/WtiXT5yCFjyM8ciumjVipWucVaDkrmeQtN2j0q/DpV1OZBGqZjAJy4APsD600abeNDLKsQZYRl8MOBXUq0H1OR0proUwvtRs9qYJsmnhya0IGsntSbKczk8Gmk4pXEMKgcYqN+mBU3Xqaayj1p3EVsNTefSrLADmmlc4ouFis2fxqMg4q1sweaa20Ci4rHX21vMjMkbkLgdPpXR6FG32aSaQ5kkfBJ9uKpW8XJOK1NPwsLj0evmYqzPe6Fxm4/z6Vh69/x5XnOV+zc8Z7+grXd+CB6e/pWNrxA0q8Y8fuB1JH/160bEjhbBT5ir1+QHjn1/Gm6gzWGs2GpxDLI2G/2h1x+VWrFflBHzYVe4bt/n/wDXUWrgNZRMSch0/XisZy1TNoI6CeSWHXIJtOij+x3iPcSyKgzwh9vcVccb4fwrP8L3Qks7jTpMbwjNCT6H7y1egYNHj2p1Xex0YZWTRg6lDuRhivN/EFnvWVMcj51/DrXq1/F14rhNftyreYB900qb1sbVFpcl8JX63elhWOZohskB7jsa3bWy0+cSCW3iBEh2qU4GQM15/oskmn6tmM/I3ysPVTXodmFmjaWIhlY7gR34FaLfQ56mif8AXYvQWtnbnMVtbpxjKxKP6VaV13blSMHGMhRmqyLvTawrNcGOVIpNpwxDcffHVSR9a1uzkaR0aszDGePTtTZVZBntVOa3azit4w/lgyKRz19q0YxLcsIlTcTTT7ktCaZZm9uDuYiNMEj19q1rvS4IoVeJCqKxZkRyucjH4/Sp9N09bKMjOXbljVi6ge4DJEwWRUJUsONxBwat6kHD6/p8NvNHPaReXbsi8dMN7D8qyFFdtqekOtvaQtvmXYsbqo4LevtXmkmsR21zJbyxuGjcqfwrvwtVyThLdHNi6Cio1I7P8zVYY5oIFZy65ZEcuQfcVbgukus+UrP9FNdTZxWH5TPWmmRD3yaVoJQCfJcL3O01BlVPShNMTTQ4uDnmlDADrTN689KjLgHqKYh7OD0qNiME0jSKmeQaiadCvJ4oA9OsJnZcSIR0wfWtOzI3SDPU5qvBtMMBwMMMj8qZLG9sWmiboQcGvmtme6XZmCk59D/KsrxE6rpc65I3QcDOPT8avyypLGrhwFPXnp/L1rF8ROrxXC8lobbcABnOSB0p33GlsYgZVtFBOMBcH72TjpjH61S1WB5dIkZQcxOjnHswFRRXDSIgbOR2+gxXUQ2OfDF3I4+aRCR9F5rL4ma/Cjm7eRoJo5kOGU5resp9xb03HH06j9MVgxgGMVf0+TBK5561VRe7c1w8vft3Ne7QPHkVyOtW2+J+O1dgp3xkViX8G9XGKyTs7nby3Vjze2TF2jHqrbT+ddf4aZoLdo3PyK5CjHuc/wBK5mWIw6nNGRjPzCuzsWgi04oeWfEgGPXmt0/fuctZWp2Ncj5dyjJx0rEnRhfwXU7IkcTYYZwcdvrzVDXtQ1LTriOa1V2iMbwsBzh8ZRsfWqGtTy6nqGnojskUkJO5TjLZ+YfXAP51u7bnLSpuTt0OiTUCfEk8N5LmzBj8tGOAPlPIPrkiupTAVbm1k3pnhlPT61wf2GW5uNOhWFzDPGwkuQclFB+UZ9TXZ2skFlEz24QKCVYDoT70/Mzas2jpdPvUvExwJF+8taOAqs3fFcapktYodQt+CDmWP+7nt9K7CznjvLVJozlXGfpVp3RmyDeWu1cOGUDGF6DNeVeK7O30zV2e5sEmlmLOx3kLnPp9CK9c2hZwqKAM5OB7Vzuu+FYNW1C1muVWSNZXdkPcEKAP/HaqE+R8xMouS5TyyPVreJgYNMsUbsSm4/qa63S11y5QNJJb28ZHCrFlsfTtXVxaNaaenlWtn5SdRtxj8am+yvyoHT0xn+dEsQpaJCVFx3ZmhWiUI4aTHG7HWoZLO3uB+8tkx6la1JbSUIGVXcDsOtQtEeN4ZM9u1ZKdupbjc50eGrYysGyFOcfPg/yqrP4WaREEbqjj725ifp2rppbd1wVZSCcAmoWRg207AenLCtFiKi15jN0Kb6HLv4RvVcAzW+D33VG/hK9X5Q0L5/2sV0ryFFLFgCpwck0q3ce0ncjkHGBnNa/W6q6mf1Wn2Dwzqkt3aWsVyMSxx8n14rpQqyKVYZBrlEtUVR5TlXB4xW5a34j8qKbO5uA3YmvOT7nYStZl4AIpWTa3OO9Y0lot7cSLLPLvKGEsmPu5B/oK6WIqykKPmxnHrWbpKh5bh2TBErZzQ47DTMqy8M2q3Mglmby1I8vbwT65rpLiKD7J9kQAAxsqgfSqBt5XZVz1dsn2zxTp7eaJieSyD5WHelH3eg27nBQMdpU9RVyxDPeIidWOMVtXXheRmee1ZQGAYRnrz1FP0Xw/dw6lHcXKhEj5Azkk1ry3VgU7aojifGKiuIw+4irN1D5F7PEOgbI+lQbsA5riejsz2ItSV0cHrtqYtQjlA4J2muo0eHztGiDjkAZOOuAKxNccGfH+1XU6awfTUIAAAA4+granqc2L0RnXsCsmxuS4wT/WuU0i2ZdRk0u9haYRuGQj0Jxu/wA+ldXfziO6jBGQQcY65qrv8iQ3CJl9uPfFbJ6nNCo4xaXUqz3lzc6xbxxIYra1do/LB6sMgH6Yq5o4muXWG4unyreaA2MyZ57cYH51pWqR3ax3CgbsfN65plvYq09xESQijGehGecg9sVo9DBu5aW9e2upm+8gYKynowwMiuj0Cdbe6NqrbredfNgJ/Uf59K4cGSO08pjuA5Vu59cnvzWpoF6zRGPOZbZvPi9x/Gv5U46EM9EKDeW5zTJ0LxZXqpz/AI/pTo5BJGrqcqwBB9qWraurEp2ZUIK9Wp3loWDEc+orP1RpLO4WdyTav8rYONjdvwP86of2rG0hKl8AcDOa5X7rszXdG48bEffP1pjREgjYH+oqnBeK/wA3mcHkrg/jV2C6L3DxmNggXIfIwT6VSd2JqwySBGj2uhx6GqL6XbP/AAlSfRjWlNnAyRj1FV2JBJA57UnuNbGXNo6Y+WWQfjVd9FcD5Zz9CK2GJYe/cGkY5Ue4qbvuFl2OWiuSpyoFXEmMoGTyD0qjEiADIq2kqx8Lgk/pTtYVzVsdQMEghuB8pPyP/Q1owosYfZg7mJOK55n3xc8k1PBcPaMArEg8kE0XCxvKFDdKkdA6FTUVvPHcRB42BzU4GRVoRXtpvPtlcjByQfwNToRnb3xVORjaxy7VyWkGwe5xUsTBb2XLf8swdv0zVRfQTRka0gTU9396MH+lY9w4CmtfWnEmo8HIWMD+tYd0MI59q46nxs9jD/w4nHarN5l6FrsNEO7TnX+7IR+grhbgmTVSPeuz0aZUtb7dwI7gn8OKunoY4p3KepxG2dSCCxzg5554/rWbq+r22mWrSvl2A4ROST0xUdxq09011btCRdxb3jfHDJnGB+dcdaR6jqFysMLbXV937xcgEHvWnUxp0ueDlfY9J0C+JRFePy/MUPtPUE9q6B0AR3UZJHQd64g3ggn2yMoKrksPX2rrtKu/tdijnqRzVRl0MJR6mbcwm3tBGWyQSfzqrply1nfxTj+Bskeo7itHVCCGHcVhl/L+atE9CLHrmlshtfLRgyxnCn/ZPI/Q1cPBx3rmfBc7XNh54+5jyjnuy/8A1jj8K1tce4TT5jahlcrjzF6qCecVfNaNyLa2MbxLqomdtEhCs8oDSvnPloDk/jwMfWsxEJOQeAc4I5rR8O+H7KPS0mkRpbiYkvLIcv19avnRUCkLI4f+Fs9Pw71yy5pO7NVaOhkQblJ3cZOPYVZWeSP7r4Hdj29MVPNpzsyfKfkHUE4b61TmTy7nygMA/dB9aAKOneIb+41Cazuh5YjB/hBwc4Ga121hYk/eK0hA5EQyc/Ss54cTEoimRupHU8d6R4gxxu+Y9QM0m2PQu2uqafeu22R1kzjZKpU/rV9xmMfMODWGUMZBEZPHTjFUTpsXmgo08Z5Y7JmGfwziouPQ/9k=)
+![](../../assets/paligemma/fine-tuning-26.jpg)
 
 a woman in a long white floral dress stands on a pier overlooking the ocean. the dress is long and flowing, with a deep v neckline and a slit in the fabric. the woman holds a white woven bag and wears a bracelet on her wrist. the sky is clear and blue, with a few clouds. the water is calm and blue, with a few waves. the woman's hair is blonde and her nails are painted white.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-27.jpg)
 
 a person wears a red blazer with a black belt and a black skirt. the blazer has a single button on the front. the person wears a white tank top and has their hand in their pocket. the belt is black and has a silver zipper. the bag is a fanny pack and has a zipper on the front. the bag is black and has a zipper on the front. the person is standing next to a green plant.
-![](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADgAOADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDiLjTWFnvztXIre0aILYjHWlkSK70QF+CozirunRoLRQvQVAywFJjx3p8QYAA88c08AEYpdwXimIhvJJY4gYV3OSABUy/OgOPrVKzu555m3xbUBO0/StKM5HNAChiLSY46DFVGy9uRyC3cVdniMmmTqpwWwM1HHEqRqnUKMZpAKm0Iq56cZrmtdiZLsuZchv4PSup8sYz2FcffOLm+lkwQAdoz7UwKcalh0pxj6A1MiZqQpjAxmgDD1ZQJEHtUKrmBMCp9cYRzR57iktjus0LdamRUT174MS40vVIe4mRvzXH9K7TWbkgpbp95685+D8zJqOpxkfK0SN+RP+NeiWsX2zWJZnGVj4X61SfumsV1IdYmGleH9gP72XgVwtnp0uo3GFHy9WY10viBpNY1wWcJPlxfKT6etJqzx6Hpi2tsP38oxkdah6u/RHTBOMUluyzaRQJobQwNuWKVlJ98CqWkt/xOY4u53Y/KrHh20lg0CcTA7nl38+4/+tVTSY3fxfCijgBmY+gxT6I5KitNo66/GIUijHHeud1J/IwrdDxXXS7ACBgGuX1wKwKzJlSeHXqKsxPIbz5b6fPTzG/nVCTmZTWrqsDw6hOrf3yRjuDyKy8Dz1pDK89x5dwsOOq5rTs3CxkH1qhcwB3WQfeHFVr69NnA5HU9KBMd4h1PyYjEjckc4rk1vixD7yJB+tJdXT3QJYkkmqytGq7CnzZzuqNxWPYIZIIhDZBSdwz07e9TWu0b0HGGIpIomiuTJnKFcdOlRWEqzTSsp4LHFUUTNeRwT+VIwDE5H0q75YZAR3rFmsWutXLY5VQRW9bhxAokOXA5xTARFVVEajA708KAacFx0p3PcUAK/Gny5HcU2FP3Y4xxUpG+3cDpkZoTAHWgA28YrlNVgEN+wUcHmutzXOaorPfPuHQYFAGfFF8uTUgjzUwULGM0o68CgDjvFCkXsAB7VLaqUsoSeTR4oyNRhBHatrTLaOXToWZckCplsVE7z4UWpWw1W+ZSCWSJT+BJ/mK9G0+BorSSQD52yRmsHwdax6b4JgeVcec7y7ehbnA/QU7UNS1AWkjBSkTEKgA6fSmnY3pwcolu1gtNJtp7ueYSyk5bZzz6ZqSWazaVZTYGW5wMAIXZf8KxZbeRbGCNllZVYNIYgGOc56Eit23gjiZbr7dcGIJgQldo/LrT5ZLoa88Hd3ux0T3EyTrNbiFdoKgsCT+ArF0dTH4rnwOTBx+YrbgufNmCH+NT1rOthBYa9c6peTJBaQQfO7nAyTx/I0HLU+I6NrH5dzudxrmNZwJDGeQaD8SPDF/KYLbVYmfoOcZ+lU7pluX8yOUOp6YNBnY8+18L/arAfwqKwSo88fjXR65H/wATSUn0H8q55023Ix3BoAZKCBmud8RTFXSMdxXTSIdhJHSuS175tR2+iigRkQxNMyxr1JqO7jEMpVfStrTbURjc33jWfqMQ+0ygdqSQHsoGWxnjHNUNNjEbSBem8/zq8FOCAQPcVn6Zu3OM5+Y8/jTGaRG1949OaswH5MnvTVUYFTZCqeOgoAUMvUHing8VBC3m2oYKUz2NTocj6UASRANbM3oaaAMZpyuEtZCeFzUcMyTJlORQAu3d0OOOtZmowjzVbrkYzWtgAVRvlyyUAZMkeQKdHFyKsmPdwBVmO24BoA898Vj/AImsY9BXVaHaqdPtS/3SBn6Vzfi5NusoP9mu90axL6HasB/yzBqZIaOwbXroW0S2cMH2aHKKhG4qBwPxwOnWmXeoatZWNze3keZBD5kMTkAAkcZA6VU00tZ2VoqRvJM0rzAL6gbAT7YNXphcanbt9oRpgRs2r0/P0rto0/duzmrVW3yroM0xZ7fSbaO5uvPnCDekOcFu5J61LDe6lDOd9qHtMYK7xuX3HPP0q5Y20drA8AEaorfL9MUl3KAuEdM/lWzSkrMyhJwd4hDqMU+o2n2VBJH5m12zyhII5H415p8YtSuyDpMZKwkiVsfxdv0rr/L8vUre6w0Mqyrl0PDDPQ+ornfi9p7Ne291H95QQeOorhrU3A7qdVVGtDyfQ9EuNU+WFCSPSur0+/1fwtOqXDSSWmcEMclR7V2fw206CPQ57gJ87SEEkdKzvFyLI7RkDDHFcbk07nUoqUbMTU50urhbhDlZEU8VhSf8fS/jV9EEUMcS52ooUZ9BVCTPnk+ma6DkJ2AKnNcRr4xq7Y9BXZyuBDnpxXGa5zqjn2FNCJLGTcQDVTUoMTu396n2B/e/SrGoDcM+1AHpYG0HGRVHSidp57nmtEjajA85FZ+kjchx70DNNSyEk8inxXkMk3l7hk9Kr3bNBbSuP4UJFYGjw3E+pRTMSVZvXpSuB15njiYI7AZ6CplwRkVkahZSTajE4JCYxWtBD5UKruLH1pgRXkpjsnHYnGaW0iMdurYwCKneIGBQ4yu7OKi1GZhasyrwnQLQBYwOKqXK7pB7Cp4WHkoSDnb3pHXfJnFAFZIuelXI0ytIkXzHNWFj2igDzDxl/wAh9R/s16pokOPD1pgf8sxXk/jNj/wkuB2UV7DoPGkWSgZHlrTSuwbsatnYFYvOIKh4xEBn0LZP61ZhWYR7UU7fpxVm+f7PaRljgbeajt5ZJ40QcqVzx0ruWi0OF6vUz54ZYv8AWHb6N1H0NUZpJ1zugWVR3Rv6V0E0LnJZ1jXHQ9azZrW3ZiWl59UXBq0xNGM5iYboZ2gcEfJJ0J9OasePbQXllE5HJUH9Kg1e2T7O0cUkjyYyNyg4rU1P/TPDtlKeS0K5+uK5sTqkdGG0kYHgIeXpF5F/dkz+Yrn/ABR818oHdv610vhNPLbUI/of51z2vJv1eFfWUfzrzJ7o9OHwszn4NQPGMqR3qw5G41WmfEiD3rqOMS6jBhIrg9aONUkGfSu+mIKYzXAa6v8AxNJjQhEFs+1we+av3bZjWsyE/OKu3j/u1+lMD1SUgxSEdlJrM0xisa4FPuNTSM3EIjY7IyzN2FV9PuFSFT1yBSGbUy+dCyHBBGCDSWFnFavGEPPSnxFXQ/TJpsJAuFwMc0gNOXATc3anx/dzUTMWwMZB604HbTAWacGNYl5cnOPanKg8shh19aI1jTMxHztxRFIJlY4Iwcc0AOHzHpSxoCTSKMHirNtFuVm96AEWPmpMACniMimutAHk/i/B8TOfQCvZfCu27sLAp93YP5V4r4uP/FTTewr2H4ZmQ+HYpXUkqpCinHdClszrr0q0sceMjqeOtYt54l07R/3fmCWYcbY+g9quanvkt5AWKsU52n2NZHhnwzax7bq7jLzldwDc4r0IpJXkcDu3oTW+qXep4dNIkVDzvkf+WaW7MUBzLAcDltp5X61tX9yYY/LgAVjxkdqxPIuoiXkw+ecnrQrPoVqihd39jcqI7adWJGCpOCK1IFEnhiBSc7Cy/qaxdRhspEYughlPAkXGQfatLQSW8P3MbNu8u4YA+xANZV17hrRfvlXQo/K1C8HZo/61zOrLu16Af9NM11+mxlb25bsI/wCtcjcfvfECn+6xP6V5UviR6kfgZjMfmb61RmlVrhFB5B5rQcD5/XJrOkgCXiuP4q6DkHSg7QRXEayc38/1rupnWNMucCvPr+YS6hO45UtxTQiO1I3VLdnO0VURiJMip52J25pjPQdWvo10qRIwXeb5RtHNQWDHyhlSOMVvWtrAm7eilFJxkVYW3geTCxgDjnFQ7jRUtbjEJXBJxinxSn7SCRVxYLcyEAY29almtoFgV0ByxxxSux2Q1LkIGyc88U9rgZA9RnNU0hXcQ2RgZo8sswG44p8wWNZGzboacpGOKoNIY4UVWzt60jXyW67pchcZzTTE0aaMCK1tPh3W5OO9czbana3D7YZAzdgK6/R/msBxzk8UxAbfjpVWVMVrFTg1nTrgsTQB4x4mTzfFsif3mC1774YsItP0OGFOFCivAtaLP4wcqCzeYuAOp5r6I0pnGnR5TAEa4J6k9/wqofGkKXwNjmjRnUMMD0NJauZmuXixjfsB7AAf/XrG1u/ntZAyscYOTjoa1tKtGs9Ht4nbDEGRyepLcn+dd0lZHFHciuJba2bc5eWQdu1c/fz6reSO1umyMHAVh1reurhIwVSAvjnLCsefU52BVbYAdM5xVRQmcjqct20qQXCADOSp7/T/AD3rq/C2Bpl8mSRvVufp/wDWrB1G4b7R5ag5Ay2RnH4/lWx4Uk3Qakuemz+tRiPgZpR+JF+CFzHcuk7xk4UbQOfzFciqSR6kDMoJLlRInQ/Udq7MIyaZI/8AfLEfyrjIUcalGrMT1NeQ376PWS/dsxZsh3A/vGqMzf6XGp681duHCySezn+dZRlDaquTxg10HIV9eEr2BMQOQe1cK2VJDDmu21XVls5Nm0NkVy7rFdzvLjBY5wKEBRUc1JPkbc+lXlsASCAasnTDPgFTnFMR6Ui/I4xnLGr0LRoM46DiqdpmaEMOA3NWEjJAJYAUhjpPLDDIxu71ZKBoVC9jUDW+4bt3ToKmiKpGAzYx60ARSxDKk+lRGIYIzipZrqKOItIwwKx7a8e+1ArGT5YPWloBpQRbN5I7Y5qrfoRZTEn5cZNaZXgc8jtVS5i86OSBsjepFAFDQ7eC1tTeAAyMPl9q7LwtNJK8288AZxXLRwC2sEgToOprpPCuVacA84FKJTOmYAnFZF+xBKitMFgD61l3I3zH3qiTymwsZdR+J8Fsq7gbhWb/AHRyf0FfQMM8c0LeWcKhKYHtxXl/hXS5NP8AHGoahOoIFs7Rn0PH9K7HwjctdaAZWfczzSNn23sB/KnS/iIdRfuWy5c2ou5VhHALgnPpnmtOZwMg8DtUVpGfPeRhgKMA/WmXUiKCSTiu96uxwLRFOYySEiPJPpWbdtDYI095Km9RlUB7+ppL+41KdTFZqtvH3Y8muM1RJvOKXF6ZVXlt3c+lWkIdc6vDOXEIZ2Y5ZmrX8ET+amrnPRo1/Q/41x9xIiH5MHHJIro/hm/mQaszHhrlRn6KKwxDtE2oq8jvZ0/0FV7KMVxA51Uceort7hw2nF1Iwehri5lMd5uxgK4yfqK8p/Emetb3WjiL6423dwuf+WjfzrGM2L0NnoDV29UzXlwQcZkY/rWTPDJGzMxHTiuk4jJ1SdprtmByOlMSWOM4zzioS/707v71Mlj3OxHSgDW0q7E8kiyOFC9DV6+1AWPltG4fccH2rn7TaoK+tXZbVWkTOSNvSiwj0GOeRgI4XxjAFW45GWXaxJ28Cs/SYWSMtKDv3Vpuof5145FSUWRI2eRTHSR87kOB6GnRtuAAPQ1YBKH1HWnuBUNsssJR0OD1p1naQWROwEZq0z5hLryRzinRSo6fNjOOaVkFyEK7TFxKPoaW5mMVo8pwzIO1TK8OC3XFQXsCm1kdeODRYLmNFrcDoEZWB612Xg2aO5W5kj6AgVx0GnxTR5wM4rr/AAVB5CXYAwMimgZ1MmFPTtWYRvlY+lXpXOapt8jcd6YirGhiu7h+N0kLIoPcnFJ4Du4H0NrFVZLmzAjuFYHh9zGtGO2SVWLjJzxRo1gLe+vGWNQ8+zc6/wAWM8n3q6S/eIKkl7Jo30JS2BPLNzWddv6DOK0bhgowO3ArEunODggD1Nd0e5wsy9RyImkmuCvGQitiuMvmgjO+4kDN94hec1vay2xSkatubqx/P/CuPltHuLlEDHc7BST71ewkrnUaRpVkmlDXNWs5Z4pCfstlH/GBwXc9l/zzW0mp3+k7hb+HbOGFjlktSOR+XJrpZLWFYo7BI1MMESw4x1GMf41moCp2N/CAK8upNzd7nrU4RhG1ild6j53hx57dXjEbHckibSvfGPxri9VvbqTUJY87RtQkD8K7HX5Ui0K6HA3D+lef6xfr/atwWB2G1RsqehzXM1qdKelznbt2t7yRDyQSDWfqNxkD6Vvamsc6rdBQN64OP7w/+sRXM3scjZOOMV0J3RwyVpNGJu3SNn1q0FLxyOOgGKpMCjnPrVh5dtvgd+tMkbbRs94iA8E1uXZEXzf3RWFayGF0c1YvL1pTjkUwPT/MfzPLTBbHSri2Fw8kbKdqjkirsbQqd6w/Me+KlFz/ALJqbBcqC0mhPAyCeop8zFUwRg1dW5GOhqGeaKTgofyoArQpuUgHr0qVLJp85OB7VXyyuWGcKOK0IrlIoRg5J5osBG1k0MY2fMOhqDUt8Vi2AcY5q99rBXkVV1C4STTph320wM61YJEhz1611Xhq5jjScMepBFcZaDdDkk9K2NImKSurHtkUAdpPNC65DgYqg11HuALjg1kzTk55NUHbeeSetAHZfbIoICxOc8irWgXYu5rh8H5AP61xbXzJEEOSBXa+F49miC4K4adi34Dgf1rSmryM6nwl27c4NY0w3tgmtO6kUP8AMetZ9zNDDCzvwMV3I5GczqnBkeWQKOnB6/SuXnvx9shSFcKZFy3frU+rzSPdGRieckL6VgTSMLhXHZgaUpWRUVqe8QJMHuXdSPnABPfvxWRqa3Y1BvImVYz2KZ/rXR4E1pHIecqGA+tYGryCGVG/vJXltHqXucv4luGGh3cc0qyOoyCBjHFcLdwYuB5jbg9uy/l/+utfxRcyMk8O4jeVx754qnLAzpYORySwPH0rF7nSnoisLcv4ZuyeZI2WUD2wQf5D8q5O8nx8oPROa9ERUTUp7Zh+7e1GR/wNa841W0ktL66tn6xkqD6jt+lbQ2sclXV3MdY5JwWHIBq4LVxAGxkUtooFo8ZOCTnNWoFk4CtnB71ZkRWVn9oVWYFdp6VLqFosZRQPmPOa17XKycqPwretrK2upEEsY9jQB6FiPH7tVGPamOg/uA/SqVt4l0i5Risvl7SMhuDWxBardQCVJM56c0ElENEvDRkfhSlrcjlP0rS8kxZDoD9aNsO3DIoBoAzRHayegp62UAGQVqzLY28oyrbSaiGnBRyxYexphdjXto1XgKahljhETBohg9amMFoGCs7hj2NTnTlljxGxP40WC5ji3tdoCxgVasdNjdmYLjirZ0O4/hq5ZaZdQoQcUNAmVGsAowEzVVrEZ4XFdCLO5HXbUT2kx6haQ7mGllvxGEBLHAru/LW0tIoEACxqFH4VkaZYn7cjPjCHd+Vat5Jtz05rooR6mFZ9DG1Bx5h9fWs+9lVbI78HI6H/AD9at3p3MuOQP1rH1aYtBIPLxsiJJz+A/rXYc5xt7Oty7SDuelULeya5v1UjCqNxFTwbYwWJGe1dJ4e0oPatdysN0zcfQVz1ZWibU1eR6XpreZo1oev7lP5CuY8Sz7biFM87SfwzVtdVktLCO3iBbaNpAxkjtg9q5S6u59SvpJCAXPGxf4QO1cEz0YNFLUbRLuIbh8wOQaQQL5cakfcORVjEuCPLJwcVCUmBOQF+tZWZpzpdSlPGXvrtl+8tocH3BBrJ8T6Wl5HbanGvEybX/wB4f/W/lXTWkMRknLEs5hYHj6U6zt7e90m5sVBOz94gI5z7VslY53K9zy/+yQwODioxpMin5XNdn9ntgCBC559KDaxEZELflWhjc5KOxulPEpqwv9pxY2S9DXTC2Xtbmn/Z2yMW1SO5nTaXOgLIRIGXuOas2etXcKJJHPNbkcYbla66Xwzc2sUksaMxjUnYed30rDt4bdo0R42LsSGXH3TQBNH4uvreNDchJ4y33kNXp/HFpG0a3NtJGD3I6VAPCllJcWrqjCNmGeeDVPxLoF3FFII5FliQcK45H40XA6O28R6TdRCVbpY1Yd61o0aQBopUkBGRtNeK2tvEbRkkt5g8ZOWTkVo6fq1zZXFubTUGGRhlc9KY7HrLW0nBaMkg9xTPs74OCUPsay08XTx6cHmhDD++vP51JF4ys55USZFjBXIY96QWNNftyphbo/8AAhmmreaghIMgf8MVTGv6dcTGNJdmCFBzkE1aMqtP5cNzHI5XJAPIpkk5vrknB3Y7nNSCUucfaBk9jWdi5L7mU7AO1Me4KkkhuKYzqtGt2j8+ZpN+cKOelOvjk4zik0RwNHSTHEjknPHHSsLUtUaS5lCgrGjYJPf6V10VoctXR6j7qRYELlsKO5rmtS1R5rOcAZEjBAxHbvU2oEzxFrqYxoPuoDXN3F/HM0cUbfu07eprd6IyuZBk8mFnPLAcV6foMlpeaFZTgAFolyB2I4P615NeFlikU9c4H412fgy5k/sLyweI5Coz+f8AWuOotDopvU7fbar1QVkx6TBDeSSxXOI3J+XbyM9s0i3mOJBioHu0z8vX61jY2uaX2eFVCpFwKheNc/6nisxtRmRjw2361JHqjMp+/wDlmjlFcssihJCIgDt6/jVaAtBdI6xhVPDY9KlS4kmSXOfuenuKzJJ7n+8aVtSm2kmGq21zb3T+TEhQ8isme6u0HMCj6Vv3RnvdFV1J+0QHDf7Qrn5Pto6p1qkTLe5El3OwyVI/CmuLuTgTsPwoNxMhw0JHvioWuWdwNyrn2osK56Hb61OMAMGHo1X7ePTLoZlgEUpJO4etcgpkhYEirsV6+ODipaGmdZHo8UMTGNvMXO5RnpWHqFu0zGAISszYD+nqDUcOqyxrgOw+laCavDNABNEwYHIdB3pWKucVpOnCKe+R16OBgisTUdGtzGxCDJmPIruNN0WQXV0ftilJzuAfrmorrww8VlG0khOJvm4zxnrQFzA0nSL2ITRQXLrHjiNhkGoru1ubXC3VikqJ3j4P5V20FqquCjnOMnjtU72ttPcFZUZjIdqj04oHc8e1TyIr5nieWBSAwVx3qI397C/nwTZYjbuVs13fi/QI4riIQwFg5II9K4W70jYM4aNge3FAzai8Ua1b2UAdzhWySP4hW/pXjyG6nSC6t4yG4MhHINcWLXUTpZdZg8SNwGXNPsruKKeL7XaEhSMvGM/pTEe33d55dtax26gRtEHXHoazWa11IFUkVJlPT3qlpOrRalZ+VDOZWgTYpZcEZPQj8a5TVmuLCaS4hJR8nAzw3Q/1Nb0p8r8grU+eCaLGuWepWsoSTe0bcltvB/GucNhqKzGdbaTyF+YsRgY/GuitvGV62nPFKP3mMbtuSvuKxTHrc2pxPJcC9tGcb8Pgge4Ndbmmjz+VplLVUGFCjGWya67wRawSaJKZFywmOD+ArmNThaPzQxy249O1df4IZk8PYjUuzTkFfQcc1y19DejqbBs1OQkjAY6GqzabHHICMEH1NXZNRijIhmhO5mwpUZ+lXRpckyeYxRIl/ibpXNc6LHPXOmuFVsMSSeENUHhljJYGRQPUV1jeXAOZS49QvFKqRXI4kRjjJB4xT5hcpgaY7OZ8kkCPv9RWa1y2DsO4+mK64WSxh2VVO5SBgisi406FgflMLHglR1oUtRuPuozrLUTFcbZlIikG1v8AGq93dw2lw8Lk7lOCKs3Gmxop8uU5PrzVa7tJJ1hlCB3UbGJ4zjoafMris7WIBqEbHiFiPXFVZL2Npl/dLjPcU9wtu3zRuPYEH+VRCa3V1LIQCc/MDVXRFmGieJ4rq5jt7shCxx81dgbWJsmIjnpivIb2weGV0QZx29PpW7o2t3ttao8MpkGMNDIeePQ1maWR32DEcMMVKkoXBGTXNWPi2C5cwXY8twcbW4roYlhmQPbyjJ5wTQBZBBYFh7g1dhv5UKjeGRTnY1ZYkCsUJ3EelTo0ZHQg+9MDqLa9sLlwZIAkgHXHFaQsbWbY0YXKnIYVxsYKKCj8jrUsWoSWxLrIyP7HikBH4nsprjUo7eNwNpyTXB6xaSqkzt823gmvQptbLOj3lsJBj/WJwwrC1GC0urKX7LcBmY58txg07Bc52zt93hKeQ4B7fnWbpUALEsM/MBW39muIvDUiNC4QSctjjGaqaLGTdFkGV3cCgLnU2NrFaXELqvBIJIHXrWVrkguDHEiqWd/mJPA49frW28M81tM9uD5dum5s929B+Fcncz+fGWhXJDbmOeg/rTjqaqVoDBpjrAwiSGZmJz8/IqmIbq2K7uD2VeSafYQ3M0ryR7vl64OM+1aapqLDaYFQHngbj+dd1O0op2POqrlm1e5i6iJRCGmTBIGK63wNqFumjC2b5ZVkLElsA+38qwNat5DZBmByhGSx5q14FsoNRupLedEYIdwJ68//AKqxrrQui9T0WGCA3BndAAq8F8VavbiBfIiVg0arnjpk1ymraVJHeRlQzQFCgAkIC474rM8Rz6tpmnxXVoy3CjEeO2D6VyHUdheCNy5BBGPwFc7Pp8gyVcqntzXBS+NL02ZWaGaIlj90/KeOlaGk+M4grBrkk5zh/ft+dMDrbBZ4XuFLOW8lgufX2qr9v1W2GHiWZR3PWobLxRb3EiZcZYkJj1PrUg16EsxmRSYjhsDofSkD2AX9rqKCO/t3idDkHkD86llgtiu6KRQrjHDcGlN/aXEJEbKRv2lenJpkr2kiRlUAGfTG33piuUbmw8vaGhklY9WQjFZ+vwmx0WS5jgkby8YUn1OKp+KPEl7p2svZW/Cqiurbic5APSql9rNxe2xjus+btUYHCkeuKd9AtqbOs+HpbWYuwJAB3GjRtCiu9JQOo3ZJz3612I1O11KI2d/GI5mGFlA4NWItAOm2qmNt0Y5wOwpWBM8m13Q3ssSjLgvznr+dVdP1u/09S0Tb9p4V+or0bWtP+2oiJjcc4z0rkX8PiaZ45F2SAcEHvQM6Dw1rSX8AW72xXJJ4PG4djW/IYWQskq7gSMV5Tfx3FtIInYyBDhJU4YYqSHXby0bd5plQHk9/xFAWPSzcPGByR71E9xK5BXBHeua0/wAUwXIPmMPlXOD3PpXTQyRT2yyxcZ5xigQqy7+GyaY9tHODuwoFT+TJgng49KeoyAGOMUxWI4o5fJa1WbfB3RhmrWmaXBDIzS2465DRnp+FOR8cqBx3pzXEqKfLK7vekOxsw2ivb+XaTLJHkkqDhvxFcXe+GRFfywCd4nBztJyPWtqO5k3b5E2vnqhp97apqLtOJX88ADOa3oStKxjWjdXOVfQ72EkKxeoxFdRuY5FlT1YMQP8AP+Nb09xc6emJWDA8Z71mSTT3APz4U9jXachTliMlvLGzFgy45Oal+H6M2rXESEg+WGBHqCP8aaSxw2ByOM1N4JYweJLu2GS7xsy4Hbg4/KsK60NqT1O8eZtQdoTDjyz8+e5PpTNW0pG8MX1vIBkwuRjsccYq5YKheTKbHY7i+c7vrVnUI/Ms5lxgtG2CPUiuI7DwBtFuJ4ysEr7lUOEk5BB61iXVjKjN51uh56x16Zf2txDIkiY3D5cAdu9Ys+nLPcSRbiZGU7EHZs9aQzgone1mws0kePu7uxrvL/Tp9f8AD8et6PIGuoRi8tkPDEDlh/Oue1jR5bWaRCpBwPlJ9hR4a1u68Jass6hnt5eJouzD1HuKBrzM9dYmjmV5fNXawJAPANatl4imIWEXCsGcY4569DWn4s0GK5lXXdDKy2lycyR4z5bHv7D+RrG8J6N/afiW3FxF5dvaN59y56KicnP1wBQDWtjU8WWzP41WFfuqsMb+2EFWtY0xLmziWP5ZlfAI5wvessan/a/iy41BM7JJmfnsP4f0xXR2QlnSSTbkowOKYnuf/9k=)
+![](../../assets/paligemma/fine-tuning-28.jpg)
 
 a woman stands on a stone staircase, her hand on her purse. the jeans are blue, and the fabric is ripped. the shirt is pink, and the buttons are silver. the bag is pink and has a strap. the woman is wearing a white cardigan and a silver bracelet on her left arm. the stairs are made of stone, and the wall is made of concrete. the woman is standing on a step, and the bag is on her arm.
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)
+![](../../assets/paligemma/fine-tuning-29.jpg)
 
 a person lies on a white bed, wearing a pink sweater with the words "love will save us" written on the front. the sweater is long-sleeved and has a crew neckline. the person's hand is on the bed, and their shoes are white. the jeans are blue and have a belt loop. the blanket is gray.
-![](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADgAOADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD0AUuKBilzQeoJiilyKTimAUUUZoAKKM4pdwoATNLk+tJxRQIKMUUZoGKKWkpaACijFHNABQKKKACilzSZoAUGnU2lzQIdxSjFMzRQKw/FGKaDS5oCwEUxqeTUbGkMizRk0UUxi5ozRS8UAJRS0lAC0YoooAMUUUtACZopcCgUAAzTqhnuIraMvM4RfU1kXXivS7YECVnf0CkfqaTkluI3M0ZrjX8ZPKxECwqPUnNOXxDd5y0qn/cXIFZutELnWCdS5Q8MOxqQEGuMvtfmEauUzIO69xVvSfEEtyPLlG3A64oVVMZ1PFJkVmpfPIBtIHI7dqvxSCRc8Zq1NPYB9LSYp1UAUUUUALRmiigBaY1PzUbGgCIUopBRzQAuaXNNJpM0AOzRSc0ooAWlpKKAFopMnNOzQAmap6lqtrpVsZrmQL/dXPLH2q3XmfxEu4X1GKFQC0Y/eEnPPYVMnZCbshmqeK7nUnYI7RRE8KpNVLdGu8swd17vIeP1NUrC4AQFY1JH8TdqlutaKgRpiWXPQdBXLJtmd+5qi0gKjYocj/ZAUfjU/wBog06PccsT0VFJFZljdXU1wrLKzngbQcIDXQi9lt4d00u9z0VeAPx5rKUrFxjcwrjUJrnJWIoi8bpBge+PWqiX00MnmJIxCjA4xmrN74rK3DQpGZ5F7YAA/qaxv7c1C+1BLd7W3hEhwCy8/hVK/Yluxu2viqZN3mxkAcYrdsvFCHDcA47nFclDYSyuyOyiVTwmdu4exNaKXaWihJ7BzjuRu/pQ32KTfU7i28RwsB5qkf7S9K2Le5huU3ROGHtXC2trLLAt5DZFYiMgiPt9M1Ys78xPlWTHQMrYxVQrtO0jXl0udzmjrVCzv0niXcw396ug11xkpK6Ieg6lpuaKoB1Man9qY1AEIpSajBp2aAFozSUUALuNLmkFLQAuaXNNp1ABmlpjuI1yT16ViahrFzDcpHDHFsKBy7tjqM+o9DVKLZlUqxhozfrxDxbKF8UX6SZGJSR+VenJrs5O1LYSMQDwhx2/2vrXlnjfzZPEM0rJtY4JA7cVFRWM/a860QkHlvZAKSvrzyaimsDGqlWADdvWs3T5wmVdjjryeBW6l1HcqIgcOvKn1+tczVhxd0W7S5+wxMznn7oUe/QfpW5pNjLqX+lSkkFRt9MEVxUrPNKsBbynMoKuTlc9K9b8NtDDZpAVxtAAPrWE49zppPex5J4usri28TssQZFWNWG3j/PSqFvrLhBHdpuAPyv3Fey+JfCA1uRLq3cQ3SDarEZDD0Irkz8M9YuZP3osVX++WP8ALFaJ6WZlKL5m0zFt9UtZ4g0jOSvVk5Irf0yKe7VXhuAYj90TR8GtTTfh1pmlSCa6bzpF/i24A+grWuoI7UJ9lbcDxt9Kynpsb04N7mfdanc6NaGa4njAHHBwD7CuTju2ub6aS3BIlJcwsMEHrxW5qPhfUtV1iFpLuIaeV+eB03An2Hr71ZGmaVoFuViQRmLOWLZ+nWp0S8xu7l5FPTrm4W1aYKFCckc10unXl1OqsrxhT6Zrj7bxFbC4/d/vIXYo5AyFPf8ACtzSiYdzW0gkgLF1GeV9qScou+wm10OySTdw3DVKK50ak81wzcrx2retpPNiUnG7HNdlGtzaMhqxLTTT8VG1dIivxTqbwaUcUDFoqjPrWl2zFZ9Qto2HUNKM1i3vj/QbQMI55Llh2hQnP407Mlzit2dSDS5rybVviteMGTT7NLcf35gWP5dK5S+8d67fIUm1CTaeqxHZ/Kny9zGWJgttT3PUPEGl6WjNd3sKFeqbst+QqPTPENvqlt9pijdYmOELMMt+GePxr5za6MpLGRmJ67jzW1oXiq70YiHzGNuWzgdU55K1SjHqYSxU3se0X11I8wdmCFGyhz0x/wDrU1n3YjulgMrsVj+U4PBRjxn6HIrEsfFltqMRkBBH91jggjv9MZB9quC+tTEsquCjEhc9P846/nmtWrqxzc75uZnQT3VtYWu4yrFCOpJ4FcrrthZ6uDe200E3y4JSQHmo9ZJ1LTZLVXUykZCZ+8M8c9M+nTd2rziVdR0+WTbLKjg4IAIH41506c4vVnpQrU5R2HXsXkTHa3APSi0vjFJnkmmL5kyszZkPVuKh8vPzIOaq2mpg3Z6GxI7TXNrIp6yKCPXmvc9LjCREEDj2rwzw/Zy3uu2NsMktMp/AHJ/SvbbFbiO9nhfhBypFY1NDpobNmt9oZXAxn6U/zyOoAHuaiSURcNyadJJEy7nIUfzqLmnKQzujjcwz6DNQRCCEmYQB3xk5NRzTZJYD5R096oTXMksO5nSKEdQWwTUtmqVtCe9vo7/MFrGPP9VY/LXMX/gu51K4Mmqak/2UciJeB+PrWzBrJhPl2Fs07HjeV2qK53xJYeK9SczG+hjtgOYlfZ+tJb3Ce22gl1/Y+jxfZrKNN2MZzyTUOi32zUNrNjOTtxjGPX8jXL2+nQ3MrwvvNxDneHfdz9e9XrNpbeSSynfa7oRHI3+P6UnFEObfTQ9EW6jdnaOTKnJAK8j2rb09/Nt0cHn2rkdFzIqJJgumMt6iuu02D7PvA4TORSp/FoDSSNBWyD6ikalVcA80hr047EIw9c1i30HSpb64PC8Ivdm7CvIdX+ImqanG0Jm+zwn+GNCuR7nrXT/Ft3+zadDk+WzMx9yMV5UUcfdOK0SsjjxFSXNyplgyiUlt+4nuDmnKQehx7iqLb+6K3uODSCY9NxU+jf41XMcljQZ5lHB3D35qFmhkOJIwp9cVGlyyff4z+RqQ7JRxjNO9wIZLWM8oSPcGoGDp1+YeoqUlojjn2ppff7H0qHYYQXEkMqyRNgj8R+Iro9P1q2lYbpXsrnjDbiYmIP5r69x7VzBAJ44NMYsOGH4ihNoLHrA1y0lSWS8tXRwpbfAhbPHVccHPcfjxXMHXRrt7cuES3VI8Rh2G9sevTP8AnrWTofiJtOPlzbzCRtDofmUfQ8Ee361k3V2s19JMqFVJOMHn8aJSugjo7nQQ6ks/VQko4dfX3pLi3QRGaJgvcisGS683yzGu2ZeCR39KtSXMnkhJCM9wKy5NdDb2mmpueG/Ecega7HfPB50YBVh3APce9e4aRrumazbC50+4WYEfMP4lPoR2r5oLkmp7O/utPuFns7iSCVejI2KVSkpbFUq7g9dUfTM7cdQKoySbeWPy+teWaR8T7mLEerW/nr082LhvxHQ11a+NvD81t56XuWA/1WCrD8DXHKlNPY74V4SWjNuW5mlyEAjjH8TnArBvtV06xDyT3PmsB35H4CuQ1zxndajI6WKbIl6M3f8ACs2GCS7gNxcMzuAQc0vZtfEDrJ6RNG/8YXks2dNjaFT/AMtC2P0FVGudaukV57+RhJwAtTRWe4oyYKbQGzwKuWszWxWNAuQOCe3v9abaS0RF5PdjbWy/s9U3M2+VwzMTk8dvz/lXT/2al5DFc7dzKcn6GsuO0kv5ASPkUYHP5mu/0nTo4LC2YHJYFdp7isZXbNVZIgsLMQTtEDyACD7V0kMYRATyx61ntEkNwZgOQgxWhCWMKlup5rfDx94hu5LmkJoprV3AcJ8TNM+3+GftCgl7Vw/H908GvE97LxjcK+mbq3jvLOa2lGY5UKMPY18663pkmk6tcWMwKtE5APqOx/KrWxyYqGqkUC6dyR9aQmNhjINIYfxoEeOi09TkI2hP8J49KZueI9CKsAMOi075j94DFKwEYmWZcE81FICpz3FPeBc5jOG+nFMLZ+R/vCk/MBNwcc9aaWI701sq1LncOetIBpIPUUgUscLyakWJ3J2jgdTU6+WF8twB/tKetCQESYg5GDJ69hTGZmOSSane32/dJNREFTggihgN5o3NTtuaNtAApJq1ZW3m3Cuy5RTkjPWq8UbO2EBNbVnZfaFxlowOpArOcrI1pwuy1HCjXlwsY4yAMd66RrJ9P0mJJl/0i4O7ZjlUJ7/WqmmQm0DS28IluP4C4yFPr6ZrZggPmxPcym4u3GXOc4JrhnI76cSvbaeYrcySDKx5zg+gzVrTreC8lVDZAyYznccfjitG6QG1MQYqZDjOPX/6wq1p7xW5YxKdzDapxz0qVL3TRx1KtpE81x5ZUJGjHCKMAV1dk7KoZ+dnCj0rPtLdYbdW27Rgsxbr1q7axvcSKq8ITuY0oQcpaDb0JwHu7rjPlq3zH1rXz6UxESNQqKABTs16FOnyIkXNITS001oBXrz74neHPtunrq9umZrcYlA/iT1/CvQM0yVEmieKRQyOCrKehBpoJxUo2Z8xrvU8HipQ1bfi7w+/h7W5bcKfs8nzwt6qe34VhA1aPJnFxdmPzSg03IpSQBk8CmSKeeBxVWWDB3Ajd7d6m8xn+4vHqeBTcyFsblz6Ck7MCtw4x3pI42dsAdO9XPJTO5/vegprSADCgD6VNu4xwG2EKO361BJjByOacHJQgHkVDk0NgPSZwu3NKZ27gH61D0NB60rgOL88DFPjV3cKqliewqHvXR6G0NrLtJQXDAEM/QZqW7IuKuzb0fw04t1luIUj3DLAhi35A/4V19npBSEfZdOBx/GV6/nwKbofneQk8rgMTtRSu8/8BUdT710ct0yxf6VcNEME/vZFBx9BwK46jcjtglE5q40yWNla5YbgeIYugPuelW7aADL+WASPvY6etMW70+5lIjvBI3X5SW/M1LdLObArZjDynbvc42r61hytuzOhSSVzNkuJLvU22ZKRcIv9T71v6XYmLdcXPCDnmoNJ0220q2Us3mS4zz0J9axfFPjSDT4WgidZJ8cKDx+Pt/OtXHmdomfNyq8ibxn4oSyi+yWxXfwWHp6Cu60Ug6RaPgZaJSfqRXze97LfXYeZizu+WJ7k19IaYAmm2qjoIlH6V2wpqEbGdGftG2aGaM0zNKDVG9h+aaxopGoArClFNzS5plGN4o8OweJNJa2fCzp80Mn91v8AA14Le2dxp95La3MZjmjbaynsa+lBXnHxV0iL7Lb6siBZA3lSMO47ZqkzmxFLmXMtzy8dKgldVPPzN2FPJIBPpVeNSxaTqegobPPH43fNKf8AgIqWNkHAAU1CCAnXPvTQdxwaVwJZORkde9Q5I4NNOVOCTSHPrSbAUNhqVvUUw0mTSAUnmko5pwXuaAADHJqzH5ksilW+foOcGq2eamhmMbBlxketAz0dtan0/wAP6dBZhvMePEsqcsMdge2aoul5e6p5RkY74iMsemRyT9K5IanLtCskZH+7XS+ETPeRX00fzTwrv298YOD9AaycbI3jPmdjrbdNM8O2Q8+UADkgnLOff/Cqdz41gMuy0tx5mMlpeQo9cf0rgtSvZrmZZJGZiOcdcVXtbhBM7uTtbO8Drg0KknqxutbRHQar4ru7okLK23PBPf3P+FcfJK80rPIxZieSavSou2UpKsi/eBB5/Ks4c1rZJWRjKTk9Se2O2VW9DX0vpT+ZpVo/96JT+lfNMPGK+j/D7iTw/YMOhhX+VV0OnCdTUGaXmm0oNI7R4pDRSGkKxBSjFMzS1Qx+a4X4sSsPCKqvRp1B/Wu3zXN+OtMOreFLuGMZkjHmoPcc0EVE3BpHgiSFoCh69jQuRHtHpULkomOhpY5STz19aVzyiTGFA9KZnBBpSTmk4PWgQ9+VBqPOKUNxikzSACQabS9TT0AoAbnaPehW9acyUzGDQBJtB6GmEFTSg4qUEMMUwGK9X9N1G70q9S8sZjFOncdCPQjuKomM9qFLIaPUDVv72yv3aYQvazP96JOY8+q9wPbmswboZQT901INsg54NPGNuxuaaQXuMkTacjowqqD6Vaf/AFPHbIqoEPrSYFiJucYr6A8DXYu/Cdm2OUXYfwr58jUhge31r3j4c3aXHhOFEADRMVbFPodWFfvM66ikzS5pHeOFDUlITxSArg0tNpaoBaq31xHa2U9xNjy442Zs+gFWa5D4lagbHwnLGrYe4cRj6dTQKUuVNnh17KJ7uWVVCq7lgo7AnpUSL3pcZNSEbVxUnkDM0hNKRTaBBkZ5pcim4pcUABbsKQEg0uKMUATo4YYNDJioBkGrCSBhg0wGYpQKfijbQABsdTS78jgAmmEUA7TxRcBfN5+7ipiMorr260xoxKu4fepsMhicq4+U9aYD5TmB8daqAOa0HjwMjlTVILJnHFJoB8anPXJr1b4SzyD7fbtnZ8rD0zXlkZk9R+Ar0P4X3pg1mW3klKrMnyqOhIqktDbDu1RHrpNAYU09aMVJ6ZLkUhpnNNLEUAMopaKYAK8m+LmoiS+s9OU/6pDIw9z0r1nOOfSvnrxXfnVfFF9c5ynmFV+g4oOfFStC3cxUTHJpG5NSEcYphpHnDMUmKdSYpANxSgUuKTpQAuKXFKrDoalChulMCArTMEGrXlU0xcUWAjWU96XeaUwnrSYwelADg/qKXAYcUq7T2pwjGcg0AMRmQ5qfalwvo9J5WetKIcHIODVJMBYi0R8uQfKeh9KqOEErAuRzV8ESDY557Gq1xGI2BK5z1oaAagXPysfzrpvCFwbTxJYynOPMC4B9eK5hHjz939K0tLkjOoW+6Qp+8X5iMY5oiVF2kmfR5IozTIsGCMq24bRhvWn0j1wpjU80xjQAnFFJmjNADhzXz14ntPsPiXUIcYCzMR9Dz/WvoQGvHPilY/ZvEQugPluIgc+44P8ASg58VG8L9jhmYfjTKAM0hpHnC4oxSBsGnBgaAG4pMVLgUuB7UWAhxQCRUu0UbRRYBUmx1qZSrDOagwBSgimmBY2A01oQaRX4qUPVaMRWaBgflzSYkXtVvdRuHcUrDuVQ0g7VIrsR71KSvpSbkz1FOwhVbcMMvNJdf8e+4dc09WQnBIpt0rGHaozzmh7DKkcp6YGasxuoYZBz6Cqig9hVqPGOn40kB9AeFr03/hqymPXYFP4Vr1yPw3naXwwEYcRyFQa640Pc9eDvFMM01qdxTWHFIojpaTNGaRQ6uH+KGnC78OLdqPntnyT/ALJ4rts1na/Zf2noF7aY5kiIX69RTRE480Wj51/hqPknipJFaPKMMEHBFKgCDLdaR44wRsaCmKc03oKYZSe1GgBgjvSc+tJvPpRub0pALlhRuam5b0oyfSgB240obHem8+lAGetAEizAdeaeJ/QVGIwe4p4hGPvCnqA/7R7Un2g9gaACvRgfwp4bP8Ip6gR/aD3U4o3Rv3INSfUigpGaNQI/JY8qwNSxyzRcMu5aaEUHgmng9smmgIZUKyEoTg84pUlI7GkuUIZWVjgimxuysPmJ+tLZgepfDLXyJn0iQHY+Xj9j3r0414/8MRE3iN2fO8QnYO1ewGmz0sO26auJTWbinYpjUjc//9k=)
+![](../../assets/paligemma/fine-tuning-30.jpg)
 
 a man stands against a pink wall, his hand on his face. the man has long blonde hair and a black sweater. the wall is pink.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-31.jpg)
 
 a row of white hangers hang on a white metal bar. the hangers have silver hooks and silver metal bars on them. the bar is white and has a silver metal screw on the end of it. the wall is white and has a gray shadow on it. the hanger on the bar has a silver hook and a silver metal bar on it.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-32.jpg)
 
 a white hoodie and white pants hang on a black metal bar. the hoodie has a white drawstring and a white drawstring on the pants. the pants have a white drawstring on the waistband and a white drawstring on the hem. the hanger on the bar is made of wood. the wall has a black circle on it.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-33.jpg)
 
 a woman stands in front of a gray wall, wearing a pair of blue jeans and a black bag. the bag is black and has a silver chain on the handle. the woman's boots are black and are knee-high. the jeans are blue and have a small hole in them. the woman's hand is on the bag. the wall is gray and the concrete is gray.
-![](data:image/jpeg;base64,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)
+![](../../assets/paligemma/fine-tuning-34.jpg)
 
 a man stands on a path, wearing a denim jacket, a white t-shirt, and a pair of brown pants. the jacket is open, and the shirt is tucked in. his shoes are white and his pants are brown. the man has his hands in his pockets, and his eyes are closed. the jacket is on the tree.
 
diff --git a/docs/models/paligemma/inference.md b/docs/models/paligemma/inference.md
index 88ab4b9..58ff1ae 100644
--- a/docs/models/paligemma/inference.md
+++ b/docs/models/paligemma/inference.md
@@ -181,10 +181,8 @@ def display_segment_output(image, bounding_box, segment_mask, target_image_size)
         mask = mask.resize((x2 - x1, y2 - y1), resample=Image.NEAREST)
         mask = np.array(mask)
         binary_mask = (mask > 0.5).astype(np.uint8)
-```
 
 
-```python
         # Place the binary mask onto the full mask
         full_mask[y1:y2, x1:x2] = np.maximum(full_mask[y1:y2, x1:x2], binary_mask)
     cmap = plt.get_cmap('jet')
@@ -281,12 +279,10 @@ if not os.path.exists("big_vision_repo"):
 # Append big_vision code to python import path
 if "big_vision_repo" not in sys.path:
   sys.path.append("big_vision_repo")
-```
 
 
-```bash
 # Install missing dependencies. Assume jax~=0.4.25 with GPU available.
-pip3 install -q "overrides" "ml_collections" "einops~=0.7" "sentencepiece"
+!pip3 install -q "overrides" "ml_collections" "einops~=0.7" "sentencepiece"
 ```
 
 For this segmentation example, load and prepare a different image that includes a cat.
@@ -304,9 +300,7 @@ reconstruct_masks = segeval.get_reconstruct_masks('oi')
 def parse_segments(detokenized_output: str) -> tuple[np.ndarray, np.ndarray]:
   matches = re.finditer(
       '<loc(?P<y0>\d\d\d\d)><loc(?P<x0>\d\d\d\d)><loc(?P<y1>\d\d\d\d)><loc(?P<x1>\d\d\d\d)>'
-```
-          + ''.join(f'<seg(?P<s{i}>\d\d\d)>' for i in range(16)),
-```python
+        + ''.join(f'<seg(?P<s{i}>\d\d\d)>' for i in range(16)),
       detokenized_output,
   )
   boxes, segs = [], []
diff --git a/docs/models/recurrentgemma/fine-tuning.md b/docs/models/recurrentgemma/fine-tuning.md
index 37869aa..c2569bf 100644
--- a/docs/models/recurrentgemma/fine-tuning.md
+++ b/docs/models/recurrentgemma/fine-tuning.md
@@ -309,10 +309,8 @@ class TrainingInput:
 class DatasetSplit(enum.Enum):
   TRAIN = 'train'
   VALIDATION = 'valid'
-```
 
 
-```python
 class MTNTDatasetBuilder:
   """A data loader for the MTNT dataset."""
 
@@ -385,10 +383,8 @@ class MTNTDatasetBuilder:
     mask = self._pad_up_to_max_len(mask, False)
 
     return TrainingInput(input_tokens=tokens, target_mask=mask)
-```
 
 
-```python
   def get_train_dataset(self, batch_size: int, num_epochs: int):
     """Build the training dataset."""
 
diff --git a/docs/models/recurrentgemma/model-card.md b/docs/models/recurrentgemma/model-card.md
index 0583286..0060775 100644
--- a/docs/models/recurrentgemma/model-card.md
+++ b/docs/models/recurrentgemma/model-card.md
@@ -164,10 +164,7 @@ In creating an open model, we have carefully considered the following:
     - LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card.
 - **Misinformation and misuse**
     - LLMs can be misused to generate text that is false, misleading, or harmful.
-    - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI
-```python
-Toolkit](https://ai.google.dev/responsible).
-```
+    - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](https://ai.google.dev/responsible).
 - **Transparency and accountability**
     - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
     - A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem.
diff --git a/docs/models/releases.md b/docs/models/releases.md
index 2ae9c6d..01255cb 100644
--- a/docs/models/releases.md
+++ b/docs/models/releases.md
@@ -1,3 +1,124 @@
-# Releases
+This page documents releases for the Gemma family of models.
 
-Gemma model release history and changelog.
+## June 3, 2026
+
+- Release of [Gemma 4 12B Unified](https://ai.google.dev/gemma/docs/core).
+
+## April 16, 2026
+
+- Release of [Gemma 4 - MTP](https://ai.google.dev/gemma/docs/mtp/overview) for E2B, E4B, 31B, and 26B A4B.
+
+## March 31, 2026
+
+- Release of [Gemma 4](https://ai.google.dev/gemma/docs/core) in E2B, E4B, 31B and 26B A4B sizes.
+
+## January 15, 2026
+
+- Release of [TranslateGemma](https://www.kaggle.com/models/google/translategemma) in 4B, 12B, and 27B parameter size.
+
+## January 13, 2026
+
+- Release of [MedGemma 1.5](https://developers.google.com/health-ai-developer-foundations/medgemma) in 4B parameter size.
+
+## December 19, 2025
+
+- Release of [Gemma Scope 2](https://ai.google.dev/gemma/docs/gemma_scope), an interpretability suite for Gemma 3 models.
+
+## December 18, 2025
+
+- Release of [FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma) in 270M parameter size.
+- Release of [T5Gemma v2](https://blog.google/technology/developers/t5gemma-2/) in 270M-270M, 1B-1B, and 4B-4B parameter sizes.
+
+## September 13, 2025
+
+- Release of [VaultGemma](https://www.kaggle.com/models/google/vaultgemma) in 1B parameter size.
+
+## September 4, 2025
+
+- Release of [EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma) in 308M parameter size.
+
+## August 14, 2025
+
+- Release of [Gemma 3](https://ai.google.dev/gemma/docs/core/model_card_3) in 270M size.
+
+## July 9, 2025
+
+- Release of [T5Gemma](https://developers.googleblog.com/en/t5gemma/) across different parameter sizes.
+- Release of [MedGemma](https://developers.google.com/health-ai-developer-foundations/medgemma) 27B parameter multimodal model.
+
+## June 26, 2025
+
+- Release of [Gemma 3n](https://ai.google.dev/gemma/docs/3n) in E2B and E4B sizes.
+
+## May 20, 2025
+
+- Release of [MedGemma](https://developers.google.com/health-ai-developer-foundations/medgemma) in 4B and 27B parameter sizes.
+
+## March 10, 2025
+
+- Release of [Gemma 3](https://ai.google.dev/gemma/docs/core/model_card_3) in 1B, 4B, 12B and 27B sizes.
+- Release of [ShieldGemma 2](https://ai.google.dev/gemma/docs/shieldgemma).
+
+## February 19, 2025
+
+- Release of [PaliGemma 2 mix](https://ai.google.dev/gemma/docs/paligemma/model-card-2) in 3B, 10B, and 28B parameter sizes.
+
+## December 5, 2024
+
+- Release of [PaliGemma 2](https://ai.google.dev/gemma/docs/paligemma) in 3B, 10B, and 28B parameter sizes.
+
+## October 16, 2024
+
+- Release of [Personal AI code assistant](https://ai.google.dev/gemma/docs/personal-code-assistant) developer guide.
+
+## October 15, 2024
+
+- Release of [Gemma-APS](https://ai.google.dev/gemma/docs/gemma-aps) in 2B and 7B sizes.
+
+## October 8, 2024
+
+- Release of [Business email assistant](https://ai.google.dev/gemma/docs/business-email-assistant) developer guide.
+
+## October 3, 2024
+
+- Release of [Gemma 2 JPN](https://www.kaggle.com/models/google/gemma-2-2b-jpn-it) in 2B size.
+- Release of [Spoken language tasks](https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning) developer guide.
+
+## September 12, 2024
+
+- Release of [DataGemma](https://ai.google.dev/gemma/docs/datagemma) in 2B size.
+
+## July 31, 2024
+
+- Release of [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2) in 2B size.
+- Initial release of [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma).
+- Initial release of [Gemma Scope](https://ai.google.dev/gemma/docs/gemma_scope).
+
+## June 27, 2024
+
+- Initial release of [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2) in 9B and 27B sizes.
+
+## June 11, 2024
+
+- Release of [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma) 9B variant.
+
+## May 14, 2024
+
+- Initial release of [PaliGemma](https://ai.google.dev/gemma/docs/paligemma).
+
+## May 3, 2024
+
+- Release of [CodeGemma](https://ai.google.dev/gemma/docs/codegemma) v1.1.
+
+## April 9, 2024
+
+- Initial release of [CodeGemma](https://ai.google.dev/gemma/docs/codegemma).
+- Initial release of [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma).
+
+## April 5, 2024
+
+- Release of [Gemma](https://ai.google.dev/gemma/docs/core/model_card) 1.1.
+
+## February 21, 2024
+
+- Initial release of [Gemma](https://ai.google.dev/gemma/docs/core/model_card) in 2B and 7B sizes.
diff --git a/docs/models/shieldgemma/model-card-v1.md b/docs/models/shieldgemma/model-card-v1.md
index 69e2fb5..2cb4570 100644
--- a/docs/models/shieldgemma/model-card-v1.md
+++ b/docs/models/shieldgemma/model-card-v1.md
@@ -39,7 +39,7 @@ The prompt pattern contains the following components, in order:
 
 Here is an example of a prompt used to evaluate the user prompt `[User Prompt]`:
 
-```python
+```text
 You are a policy expert trying to help determine whether a user prompt is in
 violation of the defined safety policies.
 
@@ -48,10 +48,8 @@ Human Question: [User Prompt]
 <end_of_turn>
 
 Our safety principle is defined in the below:
-```
 
-    * "No Hate Speech": The prompt shall not contain or seek generation of content
-```python
+* "No Hate Speech": The prompt shall not contain or seek generation of content
   that expresses, incites, or promotes hate based on race, gender, ethnicity,
   religion, nationality, sexual orientation, disability status, or caste.
 
diff --git a/docs/models/shieldgemma/model-card-v2.md b/docs/models/shieldgemma/model-card-v2.md
index 505817d..1d884a0 100644
--- a/docs/models/shieldgemma/model-card-v2.md
+++ b/docs/models/shieldgemma/model-card-v2.md
@@ -43,7 +43,7 @@ incorporates third party benchmarks.
 
 Here is our prompt instruction `[Prompt Instruction]`:
 
-```python
+```text
 You are a policy expert trying to help determine whether a generated or real
 image is in violation of the safety policy below. Your decision must very
 clearly follow the specific language used in the safety policy and should not
diff --git a/docs/stylesheets/custom.css b/docs/stylesheets/custom.css
deleted file mode 100644
index ceb0c1f..0000000
--- a/docs/stylesheets/custom.css
+++ /dev/null
@@ -1,28 +0,0 @@
-/* custom.css - Full-width modern layout similar to https://ai.google.dev/gemma/docs */
-
-.md-grid {
-  max-width: 96% !important;
-}
-
-.md-main__inner {
-  max-width: 100% !important;
-}
-
-.md-content {
-  max-width: 100% !important;
-}
-
-/* Ensure nice spacing across the wide grid */
-.md-sidebar--primary,
-.md-sidebar--secondary {
-  width: 16rem;
-}
-
-@media screen and (min-width: 76.25em) {
-  .md-sidebar--primary {
-    left: 1.5rem;
-  }
-  .md-sidebar--secondary {
-    right: 1.5rem;
-  }
-}
diff --git a/scripts/check_fidelity.py b/scripts/check_fidelity.py
new file mode 100644
index 0000000..f6b673f
--- /dev/null
+++ b/scripts/check_fidelity.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+"""
+Content-fidelity check: prove a conversion changed markup and nothing else.
+
+PLAN.md makes content fidelity non-negotiable -- ported prose must survive the port intact.
+This compares the *word multiset* of an upstream source against its converted page and reports
+anything dropped. Word-level rather than line-level, because the converter legitimately reflows
+wrapped list items and re-indents code, but must never lose a word.
+
+    python scripts/check_fidelity.py <source.md.txt> <converted.md>
+    python scripts/check_fidelity.py --manifest            # every page in sources.toml
+
+Expected drops are metadata the converter is meant to strip (Colab badges, "View on
+ai.google.dev", `<br>`). Those are filtered by EXPECTED_DROPS; anything else is a finding.
+
+Exit codes: 0 = faithful, 1 = prose lost, 2 = usage error.
+"""
+
+from __future__ import annotations
+
+import argparse
+import re
+import sys
+import tomllib
+from collections import Counter
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DOCS_DIR = REPO_ROOT / "docs"
+MANIFEST = Path(__file__).resolve().parent / "sources.toml"
+
+# Markup tokens that legitimately change form during conversion:
+#   NOTE/WARNING/...  `> [!NOTE]` becomes `!!! Note` (different case, so it reads as a drop)
+#   ---               table separators are regenerated by fix_tables
+EXPECTED_DROPS = {"NOTE", "IMPORTANT", "WARNING", "TIP", "CAUTION", "---"}
+
+WORD = re.compile(r"[A-Za-z0-9_.+-]{2,}")
+BASE64_PAYLOAD = re.compile(r"data:image/[A-Za-z0-9.+-]+;base64,[A-Za-z0-9+/=\s]+")
+NOTEBOOK_PREAMBLE = re.compile(r"^This notebook will run on\b", re.IGNORECASE)
+
+
+def strip_designed_drops(source_text: str) -> str:
+    """Remove the lines convert_notebook is *designed* to discard.
+
+    Comparing raw source against output would flag every Colab/Kaggle badge as lost prose. The
+    honest comparison is "source minus the chrome we deliberately strip" vs. the output, so this
+    reuses the converter's own predicate rather than maintaining a parallel word blocklist --
+    the two can't drift apart.
+    """
+    from convert_notebook import is_badge_line
+
+    lines = source_text.splitlines()
+    kept = []
+    for i, line in enumerate(lines):
+        stripped = line.strip()
+        if is_badge_line(line) or NOTEBOOK_PREAMBLE.match(stripped):
+            continue
+        if stripped in ("<br />", "<br/>", "<br>"):
+            continue
+        if stripped.startswith("|---"):
+            previous = lines[i - 1].strip() if i > 0 else ""
+            following = lines[i + 1].strip() if i + 1 < len(lines) else ""
+            if is_badge_line(previous) or is_badge_line(following):
+                continue
+        kept.append(line)
+    return "\n".join(kept)
+
+
+def words(text: str) -> Counter:
+    """Word multiset, with base64 payloads removed so extraction isn't reported as loss."""
+    return Counter(WORD.findall(BASE64_PAYLOAD.sub(" ", text)))
+
+
+def compare(source_text: str, converted_text: str) -> tuple[Counter, Counter]:
+    """Return (unexpected drops, additions)."""
+    source = words(strip_designed_drops(source_text))
+    converted = words(converted_text)
+    dropped = Counter({w: c for w, c in (source - converted).items() if w not in EXPECTED_DROPS})
+    return dropped, converted - source
+
+
+def report(label: str, dropped: Counter, added: Counter, verbose: bool = False) -> bool:
+    """Print one page's result. Returns True if faithful."""
+    if not dropped:
+        detail = f"  (+{sum(added.values())} markup words)" if verbose else ""
+        print(f"  OK    {label}{detail}")
+        return True
+    print(f"  LOST  {label} -- {sum(dropped.values())} word(s) dropped:")
+    for word, count in dropped.most_common(15):
+        print(f"          {word!r} x{count}")
+    if len(dropped) > 15:
+        print(f"          ... and {len(dropped) - 15} more")
+    return False
+
+
+def cache_name(page_path: str) -> str:
+    """Cache filename for a manifest page. Shared with port_docs.py -- keep the two identical."""
+    return page_path.replace("/", "_") + ".txt"
+
+
+def fetch(source: str) -> str:
+    if source.startswith(("http://", "https://")):
+        import urllib.request
+        req = urllib.request.Request(source, headers={"User-Agent": "Mozilla/5.0"})
+        with urllib.request.urlopen(req) as resp:
+            return resp.read().decode("utf-8")
+    return Path(source).read_text(encoding="utf-8")
+
+
+def check_manifest(cache_dir: Path | None, verbose: bool) -> int:
+    pages = tomllib.loads(MANIFEST.read_text(encoding="utf-8"))["page"]
+    print(f"Checking {len(pages)} page(s) against upstream\n")
+    failures = 0
+    for entry in pages:
+        target = DOCS_DIR / entry["path"]
+        if not target.exists():
+            print(f"  SKIP  {entry['path']} (not yet ported)")
+            continue
+        # Must match port_docs.py's scheme: many pages are named index.md, so keying the
+        # cache on the bare filename collides them all onto one entry.
+        cached = cache_dir / cache_name(entry["path"]) if cache_dir else None
+        if cached and cached.exists():
+            source_text = cached.read_text(encoding="utf-8")
+        else:
+            source_text = fetch(entry["source"])
+            if cached:
+                cached.parent.mkdir(parents=True, exist_ok=True)
+                cached.write_text(source_text, encoding="utf-8")
+        dropped, added = compare(source_text, target.read_text(encoding="utf-8"))
+        if not report(entry["path"], dropped, added, verbose):
+            failures += 1
+    print(f"\n{len(pages) - failures} faithful, {failures} with dropped prose")
+    return 1 if failures else 0
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("source", nargs="?", help="upstream .md.txt path or URL")
+    parser.add_argument("converted", nargs="?", help="converted .md path")
+    parser.add_argument("--manifest", action="store_true", help="check every page in sources.toml")
+    parser.add_argument("--cache-dir", default=None, help="cache fetched sources here")
+    parser.add_argument("--verbose", action="store_true")
+    args = parser.parse_args()
+
+    if args.manifest:
+        return check_manifest(Path(args.cache_dir) if args.cache_dir else None, args.verbose)
+
+    if not (args.source and args.converted):
+        parser.error("provide <source> and <converted>, or use --manifest")
+
+    dropped, added = compare(fetch(args.source), Path(args.converted).read_text(encoding="utf-8"))
+    return 0 if report(Path(args.converted).name, dropped, added, verbose=True) else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/convert_notebook.py b/scripts/convert_notebook.py
index fe9f92b..721c9e9 100644
--- a/scripts/convert_notebook.py
+++ b/scripts/convert_notebook.py
@@ -5,7 +5,7 @@
 Transformations applied:
   1. Strip metadata header (badge table, "<br />" lines, "Run in Google Colab", etc.)
   2. Remove "This notebook will run on ..." lines
-  3. Convert 4-space/tab indented blocks → ```python or ```bash fenced code blocks
+  3. Convert 4-space/tab indented blocks → ```python / ```bash / ```text fenced code blocks
   4. Convert bare ``` fenced blocks (output) → ``` { .no-copy .output }
   5. Convert blockquote-wrapped notes/warnings/important →  !!! admonitions
   6. Fix markdown tables (ensure valid headers and separator line placement)
@@ -13,9 +13,21 @@
   8. Ensure markdown list sub-bullets are indented by 4 spaces (additional tab)
   9. Collapse triple+ blank lines into double blank lines
   10. Detect shell-only code blocks (pip install / gcloud / git) and use ```bash
+  11. Rejoin wrapped list-item continuation lines instead of eating them as code
+  12. Extract base64 data-URI images to a real asset directory
+
+Content fidelity
+----------------
+This is a FORMAT converter, not an editor. It must never reword, reorder, or drop prose --
+see the "Content fidelity" section in PLAN.md. If output differs from the source in anything
+other than markup, that is a bug in this script.
 
 Usage:
     python convert_notebook.py <input.md.txt|url> <output.md> [--title "Default Title"]
+                               [--assets-dir docs/assets/<section>] [--assets-url ../assets/<section>]
+
+Tests:
+    python scripts/test_convert_notebook.py
 """
 
 import re
@@ -94,6 +106,101 @@ def is_sub_bullet(line: str) -> bool:
     return bool(re.match(r"^([-*+]|\d+\.)\s+", s))
 
 
+LIST_ITEM = re.compile(r"^(\s*)([-*+]|\d+\.)\s+")
+
+# Syntax-level Python signals. Deliberately avoids bare English words like "for", "with" and
+# "from" on their own -- prompt templates and prose contain those constantly, and treating them
+# as code is exactly the bug this replaces.
+PY_SIGNAL = re.compile(
+    r"^\s*(?:import|from)\s+\w"                                   # import statements
+    r"|^\s*(?:def|class)\s+\w"                                    # definitions
+    r"|^\s*(?:return|yield|raise|assert|pass|break|continue)\b"    # statement keywords
+    r"|^\s*[#@]"                                                  # comment or decorator
+    r"|\w\s*=\s*\S"                                               # assignment
+    r"|\w\("                                                      # function call
+    r"|\)\s*:"                                                    # block opener
+    r"|\bself\.|\bNone\b|\bTrue\b|\bFalse\b",
+    re.MULTILINE,
+)
+
+# Markdown image whose source is an inline base64 payload. Anchored on the `![...](...)` form so
+# that a data URI appearing inside real code (e.g. `return f"data:image/jpeg;base64,{b64}"`)
+# is left alone.
+DATA_URI_IMAGE = re.compile(
+    r"!\[([^\]]*)\]\(\s*data:image/([A-Za-z0-9.+-]+);base64,([A-Za-z0-9+/=\s]+?)\s*\)"
+)
+
+
+def enclosing_list_indent(lines: list[str], i: int) -> int | None:
+    """Return the indent of the list item that line ``i`` continues, or None.
+
+    Devsite wraps long list items across lines, indenting the continuation. Without this the
+    4-space-indent rule below mistakes that continuation for a code block and fences prose --
+    the defect seen at models/gemma-4/index.md:117.
+
+    A blank line ends a continuation: an indented block after a blank line is a code block.
+    """
+    current = lines[i]
+    current_indent = len(current) - len(current.lstrip())
+    j = i - 1
+    while j >= 0:
+        previous = lines[j]
+        if not previous.strip():
+            return None
+        match = LIST_ITEM.match(previous)
+        if match:
+            indent = len(match.group(1))
+            return indent if indent < current_indent else None
+        previous_indent = len(previous) - len(previous.lstrip())
+        if previous_indent == 0 or previous_indent < current_indent:
+            return None
+        j -= 1  # another continuation line; keep walking back to find the bullet
+    return None
+
+
+def looks_like_python(lines: list[str]) -> bool:
+    """Whether an indented block is Python rather than prose or a prompt template."""
+    code = "\n".join(lines).strip()
+    return bool(code) and bool(PY_SIGNAL.search(code))
+
+
+def detect_language(lines: list[str]) -> str:
+    """Pick a fence language. Falls back to ``text`` rather than mislabelling prose as python."""
+    if is_shell_block(lines):
+        return "bash"
+    if looks_like_python(lines):
+        return "python"
+    return "text"
+
+
+def extract_data_uri_images(
+    text: str, assets_dir: Path | None, assets_url: str, stem: str
+) -> tuple[str, int]:
+    """Write inline base64 images out to ``assets_dir`` and rewrite the references.
+
+    Returns the rewritten text and the number of images extracted. With no ``assets_dir`` the
+    text is returned untouched, so the caller can warn instead of silently inlining megabytes.
+    """
+    if assets_dir is None or not DATA_URI_IMAGE.search(text):
+        return text, 0
+
+    import base64
+
+    assets_dir.mkdir(parents=True, exist_ok=True)
+    count = 0
+
+    def replace(match: re.Match) -> str:
+        nonlocal count
+        count += 1
+        alt, fmt, payload = match.group(1), match.group(2).lower(), match.group(3)
+        suffix = "jpg" if fmt in ("jpeg", "jpg") else fmt
+        filename = f"{stem}-{count:02d}.{suffix}"
+        (assets_dir / filename).write_bytes(base64.b64decode("".join(payload.split())))
+        return f"![{alt}]({assets_url.rstrip('/')}/{filename})"
+
+    return DATA_URI_IMAGE.sub(replace, text), count
+
+
 def fix_list_indentation(lines: list[str]) -> list[str]:
     """Ensure markdown list sub-bullets use 4-space tab indentation."""
     out = []
@@ -155,7 +262,13 @@ def parse_get_it_on_link(line: str):
     return None, None
 
 
-def convert(source_text: str, default_title: str = None) -> str:
+def convert(
+    source_text: str,
+    default_title: str = None,
+    assets_dir: Path | None = None,
+    assets_url: str = "../assets",
+    assets_stem: str = "image",
+) -> str:
     raw_lines = source_text.splitlines()
     lines = []
     for line in raw_lines:
@@ -254,19 +367,56 @@ def convert(source_text: str, default_title: str = None) -> str:
             i = j
             continue
 
+        # --- Rejoin wrapped list-item continuations (must precede the code-block rule) ---
+        # Devsite wraps long bullets; the continuation is indented and would otherwise be
+        # swallowed as a code block. Joining restores the original sentence exactly -- markdown
+        # renders "foo\n  bar" inside a list as "foo bar", so nothing about the content changes.
+        if (
+            line.startswith("    ")
+            and not is_sub_bullet(line)
+            and enclosing_list_indent(lines, i) is not None
+        ):
+            if output and output[-1].strip():
+                output[-1] = output[-1].rstrip() + " " + line.strip()
+            else:
+                output.append(line)
+            i += 1
+            continue
+
         # --- Convert 4-space indented blocks to fenced code blocks ---
+        # NOTE: the previous-line test compares raw indentation. Calling .strip() first (as an
+        # earlier version did) removes the very whitespace being tested, so the guard never fired.
         if line.startswith("    ") and not is_sub_bullet(line) and (
-            i == 0 or not lines[i - 1].strip().startswith("    ") or is_sub_bullet(lines[i - 1])
+            i == 0 or not lines[i - 1].startswith("    ") or is_sub_bullet(lines[i - 1])
         ):
             code_lines = []
             j = i
             while j < n:
-                if lines[j].startswith("    ") and not is_sub_bullet(lines[j]):
+                # A bullet may not *start* a block (that would eat indented lists), but once a
+                # block is underway an indented bullet is block content -- e.g. the formatting
+                # rules inside an ASR prompt template. Ending the block there would split one
+                # prompt into a fence plus orphaned bullets.
+                started = bool(code_lines)
+                if lines[j].startswith("    ") and (started or not is_sub_bullet(lines[j])):
                     code_lines.append(lines[j][4:])
                     j += 1
-                elif lines[j].strip() == "" and j + 1 < n and lines[j + 1].startswith("    ") and not is_sub_bullet(lines[j + 1]):
-                    code_lines.append("")
-                    j += 1
+                elif lines[j].strip() == "":
+                    # Skip the whole run of blank lines, not just one. Notebook exports
+                    # routinely leave two blank lines mid-cell; stopping at the first one
+                    # splits a single function across two fences (seen at
+                    # models/paligemma/inference.md, `binary_mask = ...`).
+                    k = j
+                    while k < n and not lines[k].strip():
+                        k += 1
+                    if (
+                        k < n
+                        and lines[k].startswith("    ")
+                        and (started or not is_sub_bullet(lines[k]))
+                    ):
+                        code_lines.extend([""] * (k - j))  # preserve the original spacing
+                        j = k
+                    else:
+                        break
                 else:
                     break
 
@@ -274,7 +424,7 @@ def convert(source_text: str, default_title: str = None) -> str:
                 code_lines.pop()
 
             if code_lines:
-                lang = "bash" if is_shell_block(code_lines) else "python"
+                lang = detect_language(code_lines)
                 if lang == "bash":
                     code_lines = clean_shell_lines(code_lines)
                 output.append(f"```{lang}")
@@ -308,6 +458,13 @@ def convert(source_text: str, default_title: str = None) -> str:
 
     result = "\n".join(output)
 
+    # Extract inline base64 images before anything else measures the document's size.
+    result, extracted = extract_data_uri_images(result, assets_dir, assets_url, assets_stem)
+    if extracted:
+        print(f"  extracted {extracted} inline image(s) to {assets_dir}")
+    elif assets_dir is None and DATA_URI_IMAGE.search(result):
+        print("  WARNING: inline base64 images left in place -- rerun with --assets-dir")
+
     # Collapse 3+ consecutive blank lines into 2
     result = re.sub(r"\n{4,}", "\n\n\n", result)
 
@@ -326,15 +483,35 @@ def main():
     parser.add_argument("source", help="Input file path or URL (.md.txt)")
     parser.add_argument("output", help="Output Markdown file path")
     parser.add_argument("--title", default=None, help="Default title heading if missing")
+    parser.add_argument(
+        "--assets-dir", default=None,
+        help="directory to write extracted base64 images into (e.g. docs/assets/paligemma)",
+    )
+    parser.add_argument(
+        "--assets-url", default=None,
+        help="URL prefix used to reference extracted images (default: derived from --assets-dir)",
+    )
     args = parser.parse_args()
 
+    out_path = Path(args.output)
+    assets_dir = Path(args.assets_dir) if args.assets_dir else None
+    if assets_dir and not args.assets_url:
+        # Derive a page-relative URL, e.g. docs/models/x.md + docs/assets/y -> ../assets/y
+        import os
+        args.assets_url = os.path.relpath(assets_dir, out_path.parent).replace("\\", "/")
+
     print(f"Reading from: {args.source}")
     raw = fetch_content(args.source)
 
     print("Converting...")
-    converted = convert(raw, default_title=args.title)
+    converted = convert(
+        raw,
+        default_title=args.title,
+        assets_dir=assets_dir,
+        assets_url=args.assets_url or "../assets",
+        assets_stem=out_path.stem,
+    )
 
-    out_path = Path(args.output)
     out_path.parent.mkdir(parents=True, exist_ok=True)
     out_path.write_text(converted, encoding="utf-8")
     print(f"Written to: {out_path}")
diff --git a/scripts/fixtures/admonitions.expected.md b/scripts/fixtures/admonitions.expected.md
new file mode 100644
index 0000000..f1b2b4b
--- /dev/null
+++ b/scripts/fixtures/admonitions.expected.md
@@ -0,0 +1,18 @@
+# Admonitions
+
+Some introductory prose.
+
+!!! Note
+    These numbers may change based on your specific inference tool and environment.
+
+
+More prose between callouts.
+
+!!! Warning
+    Fine-tuning requires drastically more memory than inference.
+
+
+| Parameters | BF16 | Q4_0 |
+|---|---|---|
+| Gemma 4 E2B | 11.4 GB | 2.9 GB |
+| Gemma 4 12B | 26.7 GB | 6.7 GB |
diff --git a/scripts/fixtures/admonitions.md.txt b/scripts/fixtures/admonitions.md.txt
new file mode 100644
index 0000000..1b78ce1
--- /dev/null
+++ b/scripts/fixtures/admonitions.md.txt
@@ -0,0 +1,16 @@
+# Admonitions
+
+Some introductory prose.
+
+> [!NOTE]
+> These numbers may change based on your specific inference tool and environment.
+
+More prose between callouts.
+
+> [!WARNING]
+> Fine-tuning requires drastically more memory than inference.
+
+| Parameters | BF16 | Q4_0 |
+| ---------- | ---- | ---- |
+| Gemma 4 E2B | 11.4 GB | 2.9 GB |
+| Gemma 4 12B | 26.7 GB | 6.7 GB |
diff --git a/scripts/fixtures/base64_image.expected.md b/scripts/fixtures/base64_image.expected.md
new file mode 100644
index 0000000..a3997f7
--- /dev/null
+++ b/scripts/fixtures/base64_image.expected.md
@@ -0,0 +1,16 @@
+# Fine-tuning
+
+The helper below encodes an image for the API:
+
+```python
+def to_data_url(image_b64):
+    return f"data:image/jpeg;base64,{image_b64}"
+```
+
+Running the cell produces:
+
+![](assets/base64_image-01.png)
+
+And a second output with alt text:
+
+![png](assets/base64_image-02.png)
diff --git a/scripts/fixtures/base64_image.md.txt b/scripts/fixtures/base64_image.md.txt
new file mode 100644
index 0000000..ca332ae
--- /dev/null
+++ b/scripts/fixtures/base64_image.md.txt
@@ -0,0 +1,14 @@
+# Fine-tuning
+
+The helper below encodes an image for the API:
+
+    def to_data_url(image_b64):
+        return f"data:image/jpeg;base64,{image_b64}"
+
+Running the cell produces:
+
+![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==)
+
+And a second output with alt text:
+
+![png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==)
diff --git a/scripts/fixtures/list_continuation.expected.md b/scripts/fixtures/list_continuation.expected.md
new file mode 100644
index 0000000..f7a1d37
--- /dev/null
+++ b/scripts/fixtures/list_continuation.expected.md
@@ -0,0 +1,7 @@
+# QAT collections
+
+Official QAT collections on Hugging Face
+
+- **[collections/google/gemma-4-qat-q4-0](https://huggingface.co/collections/google/gemma-4-qat-q4-0)**
+    - **Unquantized QAT Checkpoints (`-unquantized` / `-assistant`):** Half-precision weights extracted directly from the QAT pipeline. *Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.*
+    - **GGUF (`-gguf`):** Checkpoints available for immediate drop-in compatibility across the local LLM ecosystem. *Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.*
diff --git a/scripts/fixtures/list_continuation.md.txt b/scripts/fixtures/list_continuation.md.txt
new file mode 100644
index 0000000..8a177cb
--- /dev/null
+++ b/scripts/fixtures/list_continuation.md.txt
@@ -0,0 +1,9 @@
+# QAT collections
+
+Official QAT collections on Hugging Face
+
+- **[collections/google/gemma-4-qat-q4-0](https://huggingface.co/collections/google/gemma-4-qat-q4-0)**
+  - **Unquantized QAT Checkpoints (`-unquantized` / `-assistant`):** Half-precision weights extracted directly from the QAT pipeline. *Available for
+    Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.*
+  - **GGUF (`-gguf`):** Checkpoints available for immediate drop-in compatibility across the local LLM ecosystem. *Available for Gemma 4
+    E2B, E4B, 12B, 26B A4B, and 31B.*
diff --git a/scripts/fixtures/prompt_template.expected.md b/scripts/fixtures/prompt_template.expected.md
new file mode 100644
index 0000000..c4d4e7c
--- /dev/null
+++ b/scripts/fixtures/prompt_template.expected.md
@@ -0,0 +1,26 @@
+# Audio
+
+Use the following prompt structure for **Audio Speech Recognition (ASR)**.
+
+```text
+Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
+
+Follow these specific instructions for formatting the answer:
+*   Only output the transcription, with no newlines.
+*   When transcribing numbers, write the digits.
+```
+
+The following code example shows how to prompt the model:
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("automatic-speech-recognition", model="google/gemma-4-12b")
+print(pipe("sample.wav"))
+```
+
+Install the dependencies first:
+
+```bash
+pip install --upgrade transformers
+```
diff --git a/scripts/fixtures/prompt_template.md.txt b/scripts/fixtures/prompt_template.md.txt
new file mode 100644
index 0000000..a49d296
--- /dev/null
+++ b/scripts/fixtures/prompt_template.md.txt
@@ -0,0 +1,20 @@
+# Audio
+
+Use the following prompt structure for **Audio Speech Recognition (ASR)**.
+
+    Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
+
+    Follow these specific instructions for formatting the answer:
+    *   Only output the transcription, with no newlines.
+    *   When transcribing numbers, write the digits.
+
+The following code example shows how to prompt the model:
+
+    from transformers import pipeline
+
+    pipe = pipeline("automatic-speech-recognition", model="google/gemma-4-12b")
+    print(pipe("sample.wav"))
+
+Install the dependencies first:
+
+    pip install --upgrade transformers
diff --git a/scripts/fixtures/split_code_block.expected.md b/scripts/fixtures/split_code_block.expected.md
new file mode 100644
index 0000000..6422105
--- /dev/null
+++ b/scripts/fixtures/split_code_block.expected.md
@@ -0,0 +1,17 @@
+# Inference
+
+Render the segmentation masks:
+
+```python
+def visualize(image, masks):
+    for mask, bbox in masks:
+        mask = np.array(mask)
+        binary_mask = (mask > 0.5).astype(np.uint8)
+
+
+        # Place the binary mask onto the full mask
+        full_mask[y1:y2, x1:x2] = np.maximum(full_mask[y1:y2, x1:x2], binary_mask)
+    return full_mask
+```
+
+That produces the blended output.
diff --git a/scripts/fixtures/split_code_block.md.txt b/scripts/fixtures/split_code_block.md.txt
new file mode 100644
index 0000000..f84d19b
--- /dev/null
+++ b/scripts/fixtures/split_code_block.md.txt
@@ -0,0 +1,15 @@
+# Inference
+
+Render the segmentation masks:
+
+    def visualize(image, masks):
+        for mask, bbox in masks:
+            mask = np.array(mask)
+            binary_mask = (mask > 0.5).astype(np.uint8)
+
+
+            # Place the binary mask onto the full mask
+            full_mask[y1:y2, x1:x2] = np.maximum(full_mask[y1:y2, x1:x2], binary_mask)
+        return full_mask
+
+That produces the blended output.
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
new file mode 100644
index 0000000..8628745
--- /dev/null
+++ b/scripts/lint_docs.py
@@ -0,0 +1,570 @@
+#!/usr/bin/env python3
+"""
+Documentation linter for the Gemma docs port.
+
+Guards the invariants in PLAN.md so cleanup work can't silently regress. Every check is a
+small, independent function registered in CHECKS; add a new one by writing the function and
+registering it, nothing else.
+
+    uv run python scripts/lint_docs.py                    # all checks, full report
+    uv run python scripts/lint_docs.py --check links      # one check
+    uv run python scripts/lint_docs.py --check fences,admonitions
+    uv run python scripts/lint_docs.py --list             # show available checks
+    uv run python scripts/lint_docs.py --format json      # machine-readable
+
+Baseline
+--------
+The port starts with a large backlog of known violations. A baseline file records them so CI
+fails only on *new* violations while the backlog is burned down:
+
+    uv run python scripts/lint_docs.py --write-baseline   # snapshot current violations
+    uv run python scripts/lint_docs.py --baseline .lintbaseline.json
+
+Findings in the baseline are reported as "known" and do not affect the exit code. Findings that
+have been fixed since the baseline was written are reported too, as a nudge to shrink it.
+Shrinking the baseline to zero is the definition of done for Phase 0.
+
+Exit codes: 0 = no new findings, 1 = new findings, 2 = usage error.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+import tomllib
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Callable, Iterable, Iterator
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DOCS_DIR = REPO_ROOT / "docs"
+CONFIG_FILE = REPO_ROOT / "zensical.toml"
+DEFAULT_BASELINE = REPO_ROOT / ".lintbaseline.json"
+
+# --- Thresholds -------------------------------------------------------------------------------
+
+# Body text below this is a stub, not a page.
+MIN_BODY_CHARS = 400
+
+# Above this, something went wrong in conversion (usually inlined base64 images).
+MAX_FILE_BYTES = 60_000
+
+# Frontmatter keys required today. Phase 3.1 expands this to the full agent schema
+# (estimated_tokens, prerequisites, frameworks, code_languages, models, last_updated,
+# source_url, ported_at) -- add them here when that step lands.
+REQUIRED_FRONTMATTER = ("title", "description")
+
+# The site being replaced. Any link here is a leak.
+UPSTREAM_DOCS = "ai.google.dev/gemma/docs"
+
+# Devsite callouts that should have become admonitions during conversion.
+DEVSITE_CALLOUTS = ("Note:", "Key Point:", "Caution:", "Warning:", "Important:", "Tip:")
+
+# Signals that a code block really is Python. Absence of all of these in a block tagged
+# `python` means prose or a prompt template got captured by the converter.
+PY_SIGNALS = re.compile(
+    r"\b(import|from|def|class|return|print|lambda|None|True|False|self|async|await|yield)\b"
+    r"|[=<>]=?\s"
+    r"|\w\("
+    r"|\)\s*[:.]"
+    r"|^\s*[#@]"
+    r"|\[[\"'\d]",
+    re.MULTILINE,
+)
+
+# Two or more consecutive word-like tokens -- i.e. it reads as a sentence.
+PROSE_SIGNAL = re.compile(r"[A-Za-z]{2,}\s+[A-Za-z]{2,}")
+
+
+# --- Model ------------------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class Finding:
+    """One violation, anchored to a file and (where meaningful) a line."""
+
+    check: str
+    path: str  # repo-relative, forward slashes
+    line: int  # 1-indexed; 0 when the finding is about the file as a whole
+    message: str
+
+    def key(self) -> str:
+        """Stable identity for baseline matching.
+
+        Deliberately excludes the line number so that unrelated edits above a known finding
+        don't resurrect it as "new".
+        """
+        return f"{self.check}|{self.path}|{self.message}"
+
+    def location(self) -> str:
+        return f"{self.path}:{self.line}" if self.line else self.path
+
+
+@dataclass
+class Page:
+    """A markdown page with its frontmatter split out."""
+
+    path: Path
+    rel: str  # relative to docs/, forward slashes
+    text: str
+    frontmatter: dict = field(default_factory=dict)
+    has_frontmatter: bool = False
+    body: str = ""
+    body_offset: int = 0  # lines consumed by the frontmatter block
+
+    @property
+    def repo_rel(self) -> str:
+        return f"docs/{self.rel}"
+
+    def body_lines(self) -> Iterator[tuple[int, str]]:
+        """Yield (1-indexed file line number, line) for the body only."""
+        for i, line in enumerate(self.body.splitlines(), start=self.body_offset + 1):
+            yield i, line
+
+
+@dataclass
+class Context:
+    """Everything the checks need, loaded once."""
+
+    pages: list[Page]
+    nav_entries: list[str]  # docs-relative paths referenced by zensical.toml nav
+    config: dict
+
+    @property
+    def page_paths(self) -> set[str]:
+        return {p.rel for p in self.pages}
+
+
+# --- Loading ----------------------------------------------------------------------------------
+
+
+def parse_frontmatter(text: str) -> tuple[dict, bool, str, int]:
+    """Split a YAML frontmatter block off the top of a document.
+
+    Returns (mapping, had_frontmatter, body, lines_consumed). Parsing is intentionally shallow:
+    we only need top-level scalar keys, so a full YAML dependency isn't warranted here.
+    """
+    if not text.startswith("---"):
+        return {}, False, text, 0
+
+    lines = text.splitlines()
+    for idx in range(1, len(lines)):
+        if lines[idx].strip() == "---":
+            block = lines[1:idx]
+            mapping: dict = {}
+            for raw in block:
+                # Top-level keys only -- indented lines belong to nested structures.
+                if not raw or raw[0].isspace() or raw.lstrip().startswith("#"):
+                    continue
+                if ":" in raw:
+                    key, _, value = raw.partition(":")
+                    mapping[key.strip()] = value.strip()
+            body = "\n".join(lines[idx + 1 :])
+            return mapping, True, body, idx + 1
+
+    # Unterminated block: treat the whole file as body.
+    return {}, False, text, 0
+
+
+def load_pages() -> list[Page]:
+    pages = []
+    for path in sorted(DOCS_DIR.rglob("*.md")):
+        text = path.read_text(encoding="utf-8", errors="replace")
+        fm, has_fm, body, offset = parse_frontmatter(text)
+        pages.append(
+            Page(
+                path=path,
+                rel=path.relative_to(DOCS_DIR).as_posix(),
+                text=text,
+                frontmatter=fm,
+                has_frontmatter=has_fm,
+                body=body,
+                body_offset=offset,
+            )
+        )
+    return pages
+
+
+def walk_nav(node) -> Iterator[str]:
+    """Yield every markdown path in zensical.toml's nested nav structure."""
+    if isinstance(node, str):
+        if node.endswith(".md"):
+            yield node
+    elif isinstance(node, list):
+        for item in node:
+            yield from walk_nav(item)
+    elif isinstance(node, dict):
+        for value in node.values():
+            yield from walk_nav(value)
+
+
+def load_context() -> Context:
+    config = tomllib.loads(CONFIG_FILE.read_text(encoding="utf-8"))
+    nav = config.get("project", {}).get("nav", [])
+    return Context(pages=load_pages(), nav_entries=list(walk_nav(nav)), config=config)
+
+
+def iter_code_blocks(page: Page) -> Iterator[tuple[int, str, str]]:
+    """Yield (start line, info string, block content) for each fenced code block."""
+    fence = re.compile(r"^(\s*)```(.*)$")
+    open_at: int | None = None
+    info = ""
+    buf: list[str] = []
+    for lineno, line in page.body_lines():
+        m = fence.match(line)
+        if not m:
+            if open_at is not None:
+                buf.append(line)
+            continue
+        if open_at is None:
+            open_at, info, buf = lineno, m.group(2).strip(), []
+        else:
+            yield open_at, info, "\n".join(buf)
+            open_at = None
+
+
+def outside_code(page: Page) -> Iterator[tuple[int, str]]:
+    """Yield body lines that are not inside a fenced code block."""
+    in_fence = False
+    for lineno, line in page.body_lines():
+        if line.lstrip().startswith("```"):
+            in_fence = not in_fence
+            continue
+        if not in_fence:
+            yield lineno, line
+
+
+# --- Checks -----------------------------------------------------------------------------------
+
+MD_LINK = re.compile(r"!?\[[^\]]*\]\(([^)\s]+)(?:\s+\"[^\"]*\")?\)")
+MD_IMAGE = re.compile(r"!\[[^\]]*\]\(([^)\s]+)")
+HTML_SRC = re.compile(r"<img[^>]+src=[\"']([^\"']+)[\"']")
+
+
+def check_links(ctx: Context) -> Iterable[Finding]:
+    """No links back to the documentation site we're replacing."""
+    for page in ctx.pages:
+        for lineno, line in page.body_lines():
+            if UPSTREAM_DOCS in line:
+                for match in re.finditer(rf"https?://{re.escape(UPSTREAM_DOCS)}[^\s)\"'>]*", line):
+                    yield Finding(
+                        "links", page.repo_rel, lineno,
+                        f"link to upstream docs: {match.group(0)}",
+                    )
+
+
+def check_images(ctx: Context) -> Iterable[Finding]:
+    """Images must be vendored into docs/assets/, not hotlinked.
+
+    Scans outside code blocks only -- markdown image syntax inside a snippet is code, not
+    an image reference (e.g. an f-string like `{render_inline(image, resize=(64,64))}`).
+    """
+    for page in ctx.pages:
+        for lineno, line in outside_code(page):
+            for pattern in (MD_IMAGE, HTML_SRC):
+                for match in pattern.finditer(line):
+                    src = match.group(1)
+                    if src.startswith("data:"):
+                        yield Finding(
+                            "images", page.repo_rel, lineno,
+                            "inline base64 image -- extract to docs/assets/",
+                        )
+                    elif src.startswith(("http://", "https://")):
+                        yield Finding(
+                            "images", page.repo_rel, lineno,
+                            f"hotlinked image: {src}",
+                        )
+                    else:
+                        target = (page.path.parent / src.split("#")[0]).resolve()
+                        if not target.exists():
+                            yield Finding(
+                                "images", page.repo_rel, lineno,
+                                f"missing local image: {src}",
+                            )
+
+
+def check_internal(ctx: Context) -> Iterable[Finding]:
+    """Relative links must resolve to a file that exists. Prose only -- see check_images."""
+    for page in ctx.pages:
+        for lineno, line in outside_code(page):
+            for match in MD_LINK.finditer(line):
+                target = match.group(1)
+                if target.startswith(("http://", "https://", "#", "mailto:", "data:", "/")):
+                    continue
+                if line[match.start()] == "!":  # images handled by check_images
+                    continue
+                resolved = (page.path.parent / target.split("#")[0]).resolve()
+                if not resolved.exists():
+                    yield Finding(
+                        "internal", page.repo_rel, lineno,
+                        f"broken internal link: {target}",
+                    )
+
+
+def check_stubs(ctx: Context) -> Iterable[Finding]:
+    """Every page in the nav must have real content."""
+    for page in ctx.pages:
+        if "Content coming soon" in page.body:
+            yield Finding("stubs", page.repo_rel, 0, "placeholder text: 'Content coming soon'")
+            continue
+        if "example.com" in page.body:
+            yield Finding("stubs", page.repo_rel, 0, "placeholder link: example.com")
+        # Strip headings and blank lines before measuring.
+        prose = "\n".join(
+            line for line in page.body.splitlines()
+            if line.strip() and not line.lstrip().startswith("#")
+        )
+        if len(prose) < MIN_BODY_CHARS:
+            yield Finding(
+                "stubs", page.repo_rel, 0,
+                f"stub: {len(prose)} chars of content (minimum {MIN_BODY_CHARS})",
+            )
+
+
+def check_frontmatter(ctx: Context) -> Iterable[Finding]:
+    """Every page carries agent-readable frontmatter (see PLAN.md Phase 3.1)."""
+    for page in ctx.pages:
+        if not page.has_frontmatter:
+            yield Finding("frontmatter", page.repo_rel, 0, "no frontmatter block")
+            continue
+        for key in REQUIRED_FRONTMATTER:
+            if key not in page.frontmatter:
+                yield Finding("frontmatter", page.repo_rel, 0, f"missing required key: {key}")
+
+
+def check_orphans(ctx: Context) -> Iterable[Finding]:
+    """Nav and filesystem must agree."""
+    nav = set(ctx.nav_entries)
+    for rel in sorted(ctx.page_paths - nav):
+        yield Finding("orphans", f"docs/{rel}", 0, "page exists but is not in nav")
+    for rel in sorted(nav - ctx.page_paths):
+        yield Finding("orphans", "zensical.toml", 0, f"nav entry has no file: {rel}")
+    seen: set[str] = set()
+    for rel in ctx.nav_entries:
+        if rel in seen:
+            yield Finding("orphans", "zensical.toml", 0, f"page listed twice in nav: {rel}")
+        seen.add(rel)
+
+
+def check_fences(ctx: Context) -> Iterable[Finding]:
+    """Catch converter damage: prose captured as Python, and code blocks split in half."""
+    for page in ctx.pages:
+        if page.body.count("```") % 2:
+            yield Finding("fences", page.repo_rel, 0, "unbalanced code fences")
+
+        # Two same-language blocks separated by nothing but blank lines is the signature of a
+        # block that got split -- usually by blank lines inside a notebook cell.
+        blocks = list(iter_code_blocks(page))
+        body = page.body.splitlines()
+        for (start_a, info_a, code_a), (start_b, info_b, _) in zip(blocks, blocks[1:]):
+            if info_a != info_b or not info_a:
+                continue
+            end_a = start_a + len(code_a.splitlines()) + 1  # closing fence
+            between = body[end_a - page.body_offset : start_b - page.body_offset - 1]
+            if all(not line.strip() for line in between):
+                yield Finding(
+                    "fences", page.repo_rel, start_b,
+                    f"`{info_a}` block split in two -- likely blank lines inside one block",
+                )
+
+        for lineno, info, code in blocks:
+            lang = info.split()[0].lower() if info else ""
+            if lang not in ("python", "py"):
+                continue
+            stripped = code.strip()
+            if not stripped or PY_SIGNALS.search(stripped) or not PROSE_SIGNAL.search(stripped):
+                continue
+            preview = " ".join(stripped.split())[:60]
+            kind = "prompt template" if "{" in stripped else "prose"
+            yield Finding(
+                "fences", page.repo_rel, lineno,
+                f"{kind} tagged as python: '{preview}...'",
+            )
+
+
+def check_admonitions(ctx: Context) -> Iterable[Finding]:
+    """Devsite callouts should be Zensical admonitions."""
+    for page in ctx.pages:
+        for lineno, line in outside_code(page):
+            stripped = line.lstrip("> ").lstrip()
+            for callout in DEVSITE_CALLOUTS:
+                if stripped.startswith(callout):
+                    yield Finding(
+                        "admonitions", page.repo_rel, lineno,
+                        f"unconverted devsite callout: '{callout}' -- use !!! admonition",
+                    )
+                    break
+
+
+def check_size(ctx: Context) -> Iterable[Finding]:
+    """Oversized files mean conversion inlined something it shouldn't have."""
+    for page in ctx.pages:
+        size = page.path.stat().st_size
+        if size > MAX_FILE_BYTES:
+            yield Finding(
+                "size", page.repo_rel, 0,
+                f"{size:,} bytes exceeds {MAX_FILE_BYTES:,} -- check for inlined assets",
+            )
+
+
+CHECKS: dict[str, Callable[[Context], Iterable[Finding]]] = {
+    "links": check_links,
+    "images": check_images,
+    "internal": check_internal,
+    "stubs": check_stubs,
+    "frontmatter": check_frontmatter,
+    "orphans": check_orphans,
+    "fences": check_fences,
+    "admonitions": check_admonitions,
+    "size": check_size,
+}
+
+CHECK_HELP = {
+    "links": "links back to ai.google.dev/gemma/docs",
+    "images": "hotlinked, inlined, or missing images",
+    "internal": "relative links that don't resolve",
+    "stubs": "placeholder pages and leftover example.com",
+    "frontmatter": "missing or incomplete agent frontmatter",
+    "orphans": "pages missing from nav, nav entries missing files",
+    "fences": "prose or prompts captured as python code blocks",
+    "admonitions": "unconverted devsite Note:/Caution: callouts",
+    "size": "files large enough to indicate inlined assets",
+}
+
+
+# --- Reporting --------------------------------------------------------------------------------
+
+
+def load_baseline(path: Path) -> set[str]:
+    if not path.exists():
+        return set()
+    return set(json.loads(path.read_text(encoding="utf-8")).get("known", []))
+
+
+def report_text(new: list[Finding], known: list[Finding], fixed: set[str], selected: list[str]) -> None:
+    if new:
+        print("NEW FINDINGS\n" + "=" * 78)
+        by_check: dict[str, list[Finding]] = {}
+        for f in new:
+            by_check.setdefault(f.check, []).append(f)
+        for check in selected:
+            items = by_check.get(check)
+            if not items:
+                continue
+            print(f"\n{check}  ({len(items)}) -- {CHECK_HELP[check]}")
+            for f in items:
+                print(f"  {f.location()}\n      {f.message}")
+        print()
+
+    print("SUMMARY\n" + "=" * 78)
+    width = max(len(c) for c in selected)
+    new_by_check: dict[str, int] = {}
+    known_by_check: dict[str, int] = {}
+    for f in new:
+        new_by_check[f.check] = new_by_check.get(f.check, 0) + 1
+    for f in known:
+        known_by_check[f.check] = known_by_check.get(f.check, 0) + 1
+
+    print(f"  {'check':<{width}}   new    known")
+    for check in selected:
+        n, k = new_by_check.get(check, 0), known_by_check.get(check, 0)
+        flag = "" if not n else "  <--"
+        print(f"  {check:<{width}}  {n:>4}  {k:>7}{flag}")
+    print(f"  {'-' * (width + 16)}")
+    print(f"  {'total':<{width}}  {len(new):>4}  {len(known):>7}")
+
+    if fixed:
+        print(f"\n  {len(fixed)} baseline finding(s) no longer present -- rerun with "
+              f"--write-baseline to shrink the baseline.")
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="Lint the Gemma docs against the invariants in PLAN.md.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "--check", default="all",
+        help="comma-separated check names, or 'all' (default). See --list.",
+    )
+    parser.add_argument("--list", action="store_true", help="list available checks and exit")
+    parser.add_argument("--format", choices=("text", "json"), default="text")
+    parser.add_argument(
+        "--baseline", nargs="?", const=str(DEFAULT_BASELINE), default=None,
+        help=f"treat findings in this file as known (default file: {DEFAULT_BASELINE.name})",
+    )
+    parser.add_argument(
+        "--write-baseline", action="store_true",
+        help="write current findings to the baseline file and exit 0",
+    )
+    args = parser.parse_args(argv)
+
+    if args.list:
+        width = max(len(c) for c in CHECKS)
+        for name in CHECKS:
+            print(f"  {name:<{width}}  {CHECK_HELP[name]}")
+        return 0
+
+    if args.check == "all":
+        selected = list(CHECKS)
+    else:
+        selected = [c.strip() for c in args.check.split(",") if c.strip()]
+        unknown = [c for c in selected if c not in CHECKS]
+        if unknown:
+            parser.error(f"unknown check(s): {', '.join(unknown)}. Use --list to see options.")
+
+    if not CONFIG_FILE.exists():
+        parser.error(f"config not found: {CONFIG_FILE}")
+
+    ctx = load_context()
+    findings = [f for name in selected for f in CHECKS[name](ctx)]
+    findings.sort(key=lambda f: (f.check, f.path, f.line))
+
+    if args.write_baseline:
+        # A partial run would drop every unselected check's entries from the baseline.
+        if set(selected) != set(CHECKS):
+            parser.error("--write-baseline requires all checks; drop --check and rerun.")
+        DEFAULT_BASELINE.write_text(
+            json.dumps(
+                {
+                    "_comment": "Known violations at baseline time. Shrink to zero; never grow. "
+                                "Regenerate with: uv run python scripts/lint_docs.py --write-baseline",
+                    "checks": selected,
+                    "count": len(findings),
+                    "known": sorted({f.key() for f in findings}),
+                },
+                indent=2,
+            ) + "\n",
+            encoding="utf-8",
+        )
+        print(f"Wrote baseline with {len(findings)} finding(s) to {DEFAULT_BASELINE.name}")
+        return 0
+
+    baseline = load_baseline(Path(args.baseline)) if args.baseline else set()
+    new = [f for f in findings if f.key() not in baseline]
+    known = [f for f in findings if f.key() in baseline]
+
+    # Only baseline entries belonging to the checks we actually ran can be judged fixed --
+    # otherwise `--check links` would report every unrelated baseline entry as resolved.
+    scoped_baseline = {k for k in baseline if k.split("|", 1)[0] in selected}
+    fixed = scoped_baseline - {f.key() for f in findings}
+
+    if args.format == "json":
+        print(json.dumps(
+            {
+                "new": [f.__dict__ for f in new],
+                "known_count": len(known),
+                "fixed_count": len(fixed),
+            },
+            indent=2,
+        ))
+    else:
+        report_text(new, known, fixed, selected)
+
+    return 1 if new else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/port_docs.py b/scripts/port_docs.py
new file mode 100644
index 0000000..ddf25dd
--- /dev/null
+++ b/scripts/port_docs.py
@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+"""
+Batch-port upstream pages listed in scripts/sources.toml.
+
+Only pages in the manifest are touched. A page absent from the manifest is hand-authored or
+hand-edited and must never be re-converted -- see the "Content fidelity" section in PLAN.md.
+
+    python scripts/port_docs.py --dry-run              # show what would change
+    python scripts/port_docs.py --filter models/       # port one section
+    python scripts/port_docs.py                        # port everything in the manifest
+
+Every page is fidelity-checked after conversion. A page that drops prose is reported and, unless
+--force is given, left unwritten -- a bad conversion never lands on disk.
+
+Sources are cached under .sources/ so repeated runs don't re-fetch.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import tempfile
+import tomllib
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from check_fidelity import cache_name, compare, fetch  # noqa: E402
+from convert_notebook import convert  # noqa: E402
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DOCS_DIR = REPO_ROOT / "docs"
+ASSETS_DIR = DOCS_DIR / "assets"
+MANIFEST = Path(__file__).resolve().parent / "sources.toml"
+CACHE_DIR = REPO_ROOT / ".sources"
+
+
+def load_source(url: str, name: str, refresh: bool) -> str:
+    cached = CACHE_DIR / name
+    if cached.exists() and not refresh:
+        return cached.read_text(encoding="utf-8")
+    text = fetch(url)
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    cached.write_text(text, encoding="utf-8")
+    return text
+
+
+def relative_assets_url(assets_dir: Path, page_path: Path) -> str:
+    import os
+    return os.path.relpath(assets_dir, page_path.parent).replace("\\", "/")
+
+
+def port(entry: dict, refresh: bool, dry_run: bool, force: bool) -> tuple[bool, str]:
+    """Convert one page. Returns (ok, status)."""
+    page_path = DOCS_DIR / entry["path"]
+    source_text = load_source(entry["source"], cache_name(entry["path"]), refresh)
+
+    assets_dir = ASSETS_DIR / entry["assets"] if entry.get("assets") else None
+    assets_url = relative_assets_url(assets_dir, page_path) if assets_dir else "../assets"
+
+    # A dry run must not touch the filesystem, so extraction goes to a throwaway directory.
+    # The URL still points at the real location, keeping the diff honest.
+    with tempfile.TemporaryDirectory() as scratch:
+        converted = convert(
+            source_text,
+            assets_dir=Path(scratch) if (dry_run and assets_dir) else assets_dir,
+            assets_url=assets_url,
+            assets_stem=page_path.stem,
+        )
+
+    dropped, _ = compare(source_text, converted)
+    if dropped and not force:
+        top = ", ".join(f"{w!r}x{c}" for w, c in dropped.most_common(5))
+        return False, f"LOST {sum(dropped.values())} word(s): {top}"
+
+    if dry_run:
+        before = page_path.read_text(encoding="utf-8") if page_path.exists() else ""
+        if before == converted:
+            return True, "unchanged"
+        delta = len(converted) - len(before)
+        return True, f"would rewrite ({delta:+,} bytes)"
+
+    page_path.parent.mkdir(parents=True, exist_ok=True)
+    page_path.write_text(converted, encoding="utf-8")
+    return True, "ported"
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("--filter", default="", help="only pages whose path starts with this")
+    parser.add_argument("--dry-run", action="store_true", help="report changes without writing")
+    parser.add_argument("--refresh", action="store_true", help="re-fetch sources, ignoring cache")
+    parser.add_argument("--force", action="store_true", help="write even if prose was dropped")
+    args = parser.parse_args()
+
+    pages = tomllib.loads(MANIFEST.read_text(encoding="utf-8"))["page"]
+    selected = [p for p in pages if p["path"].startswith(args.filter)]
+    if not selected:
+        print(f"No manifest pages match {args.filter!r}")
+        return 2
+
+    mode = "DRY RUN" if args.dry_run else "PORTING"
+    print(f"{mode}: {len(selected)} page(s)\n")
+
+    failures = []
+    for entry in selected:
+        ok, status = port(entry, args.refresh, args.dry_run, args.force)
+        marker = "  " if ok else "!!"
+        print(f"{marker} {entry['path']:<45} {status}")
+        if not ok:
+            failures.append(entry["path"])
+
+    print(f"\n{len(selected) - len(failures)} ok, {len(failures)} failed")
+    if failures:
+        print("\nFidelity failures (not written):")
+        for path in failures:
+            print(f"  {path}")
+        print("\nInvestigate before using --force; dropped prose is a converter bug.")
+    return 1 if failures else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/sources.toml b/scripts/sources.toml
new file mode 100644
index 0000000..3485c86
--- /dev/null
+++ b/scripts/sources.toml
@@ -0,0 +1,181 @@
+# Upstream source for every ported page.
+#
+# Consumed by scripts/port_docs.py, and the origin of the `source_url` frontmatter added in
+# PLAN.md Step 3.1. A page listed here is a straight port and may be re-converted freely.
+# A page NOT listed here is either hand-authored or hand-edited -- never re-convert it.
+#
+# Deliberately absent:
+#   models/index.md          hand-authored hub page
+#   get-started/*            reviewed and hand-edited by Maarten (Step 0.3 = targeted repair)
+#   cookbook.md, index.md    authored for this site
+#
+# `assets` is the subdirectory under docs/assets/ that inline base64 images are extracted to.
+
+# --- Core -------------------------------------------------------------------------------------
+
+[[page]]
+path = "models/gemma-4/index.md"
+source = "https://ai.google.dev/gemma/docs/core.md.txt"
+
+[[page]]
+path = "models/gemma-4/model-card.md"
+source = "https://ai.google.dev/gemma/docs/core/model_card_4.md.txt"
+
+[[page]]
+path = "models/gemma-3/index.md"
+source = "https://ai.google.dev/gemma/docs/core/model_card_3.md.txt"
+
+[[page]]
+path = "models/gemma-2/index.md"
+source = "https://ai.google.dev/gemma/docs/core/model_card_2.md.txt"
+
+[[page]]
+path = "models/gemma-1/index.md"
+source = "https://ai.google.dev/gemma/docs/core/model_card.md.txt"
+
+[[page]]
+path = "models/releases.md"
+source = "https://ai.google.dev/gemma/docs/releases.md.txt"
+
+# --- Gemma 3n ---------------------------------------------------------------------------------
+
+[[page]]
+path = "models/gemma-3n/index.md"
+source = "https://ai.google.dev/gemma/docs/gemma-3n.md.txt"
+
+[[page]]
+path = "models/gemma-3n/model-card.md"
+source = "https://ai.google.dev/gemma/docs/gemma-3n/model_card.md.txt"
+
+# --- DiffusionGemma ---------------------------------------------------------------------------
+
+[[page]]
+path = "models/diffusiongemma/index.md"
+source = "https://ai.google.dev/gemma/docs/diffusiongemma.md.txt"
+
+[[page]]
+path = "models/diffusiongemma/model-card.md"
+source = "https://ai.google.dev/gemma/docs/diffusiongemma/model_card.md.txt"
+
+[[page]]
+path = "models/diffusiongemma/explained.md"
+source = "https://ai.google.dev/gemma/docs/diffusiongemma/explained.md.txt"
+
+[[page]]
+path = "models/diffusiongemma/inference.md"
+source = "https://ai.google.dev/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf.md.txt"
+assets = "diffusiongemma"
+
+# --- FunctionGemma ----------------------------------------------------------------------------
+
+[[page]]
+path = "models/functiongemma/index.md"
+source = "https://ai.google.dev/gemma/docs/functiongemma.md.txt"
+
+[[page]]
+path = "models/functiongemma/model-card.md"
+source = "https://ai.google.dev/gemma/docs/functiongemma/model_card.md.txt"
+
+[[page]]
+path = "models/functiongemma/formatting.md"
+source = "https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices.md.txt"
+
+[[page]]
+path = "models/functiongemma/inference.md"
+source = "https://ai.google.dev/gemma/docs/functiongemma/function-calling-with-hf.md.txt"
+
+[[page]]
+path = "models/functiongemma/full-sequence.md"
+source = "https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma.md.txt"
+
+[[page]]
+path = "models/functiongemma/fine-tuning.md"
+source = "https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma.md.txt"
+assets = "functiongemma"
+
+# --- EmbeddingGemma ---------------------------------------------------------------------------
+
+[[page]]
+path = "models/embeddinggemma/index.md"
+source = "https://ai.google.dev/gemma/docs/embeddinggemma.md.txt"
+
+[[page]]
+path = "models/embeddinggemma/model-card.md"
+source = "https://ai.google.dev/gemma/docs/embeddinggemma/model_card.md.txt"
+
+[[page]]
+path = "models/embeddinggemma/inference.md"
+source = "https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.md.txt"
+
+[[page]]
+path = "models/embeddinggemma/fine-tuning.md"
+source = "https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers.md.txt"
+
+# --- PaliGemma --------------------------------------------------------------------------------
+
+[[page]]
+path = "models/paligemma/index.md"
+source = "https://ai.google.dev/gemma/docs/paligemma.md.txt"
+
+[[page]]
+path = "models/paligemma/model-card-v1.md"
+source = "https://ai.google.dev/gemma/docs/paligemma/model-card.md.txt"
+
+[[page]]
+path = "models/paligemma/model-card-v2.md"
+source = "https://ai.google.dev/gemma/docs/paligemma/model-card-2.md.txt"
+
+[[page]]
+path = "models/paligemma/prompting.md"
+source = "https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions.md.txt"
+
+[[page]]
+path = "models/paligemma/inference.md"
+source = "https://ai.google.dev/gemma/docs/paligemma/inference-with-keras.md.txt"
+assets = "paligemma"
+
+[[page]]
+path = "models/paligemma/fine-tuning.md"
+source = "https://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma.md.txt"
+assets = "paligemma"
+
+# --- ShieldGemma ------------------------------------------------------------------------------
+
+[[page]]
+path = "models/shieldgemma/index.md"
+source = "https://ai.google.dev/gemma/docs/shieldgemma.md.txt"
+
+[[page]]
+path = "models/shieldgemma/model-card-v1.md"
+source = "https://ai.google.dev/gemma/docs/shieldgemma/model_card.md.txt"
+
+[[page]]
+path = "models/shieldgemma/model-card-v2.md"
+source = "https://ai.google.dev/gemma/docs/shieldgemma/model_card_2.md.txt"
+
+# --- Research ---------------------------------------------------------------------------------
+
+[[page]]
+path = "models/recurrentgemma/index.md"
+source = "https://ai.google.dev/gemma/docs/recurrentgemma.md.txt"
+
+[[page]]
+path = "models/recurrentgemma/model-card.md"
+source = "https://ai.google.dev/gemma/docs/recurrentgemma/model_card.md.txt"
+
+[[page]]
+path = "models/recurrentgemma/inference.md"
+source = "https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.md.txt"
+
+[[page]]
+path = "models/recurrentgemma/fine-tuning.md"
+source = "https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.md.txt"
+assets = "recurrentgemma"
+
+[[page]]
+path = "models/datagemma/index.md"
+source = "https://ai.google.dev/gemma/docs/datagemma.md.txt"
+
+[[page]]
+path = "models/gemma-scope/index.md"
+source = "https://ai.google.dev/gemma/docs/gemma_scope.md.txt"
diff --git a/scripts/test_convert_notebook.py b/scripts/test_convert_notebook.py
new file mode 100644
index 0000000..717fd33
--- /dev/null
+++ b/scripts/test_convert_notebook.py
@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+"""
+Golden tests for convert_notebook.py.
+
+Each fixture in scripts/fixtures/ pairs a `.md.txt` source with the `.expected.md` output the
+converter must produce. Fixtures are minimised reproductions of real defects found in the port
+-- see PLAN.md Step 0.2. Named regression tests sit alongside the golden comparison so a failure
+says *which* defect came back rather than just "output differs".
+
+    python scripts/test_convert_notebook.py            # run
+    python scripts/test_convert_notebook.py --update   # regenerate .expected.md, then REVIEW the diff
+
+Stdlib only, so CI needs no install step.
+"""
+
+from __future__ import annotations
+
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from convert_notebook import convert  # noqa: E402
+
+FIXTURES = Path(__file__).resolve().parent / "fixtures"
+
+
+def render(source: Path, assets_dir: Path | None = None) -> str:
+    """Convert a fixture the way the test harness always does, so results are comparable."""
+    return convert(
+        source.read_text(encoding="utf-8"),
+        assets_dir=assets_dir,
+        assets_url="assets",
+        assets_stem=source.name.removesuffix(".md.txt"),
+    )
+
+
+def fixtures() -> list[Path]:
+    return sorted(FIXTURES.glob("*.md.txt"))
+
+
+class GoldenTests(unittest.TestCase):
+    """Every fixture must convert to exactly its .expected.md."""
+
+    def test_fixtures_exist(self):
+        self.assertTrue(fixtures(), f"no fixtures found in {FIXTURES}")
+
+    def test_golden_output(self):
+        for source in fixtures():
+            with self.subTest(fixture=source.name):
+                expected_path = source.with_suffix("").with_suffix(".expected.md")
+                self.assertTrue(
+                    expected_path.exists(),
+                    f"missing {expected_path.name} -- run with --update and review the result",
+                )
+                with tempfile.TemporaryDirectory() as tmp:
+                    actual = render(source, Path(tmp))
+                self.assertEqual(
+                    expected_path.read_text(encoding="utf-8"),
+                    actual,
+                    f"\n{source.name} output drifted from {expected_path.name}",
+                )
+
+
+class RegressionTests(unittest.TestCase):
+    """Named guards for the specific defects Step 0.2 fixed."""
+
+    def test_wrapped_list_item_is_not_fenced_as_code(self):
+        """Defect: models/gemma-4/index.md:117 fenced a wrapped bullet as python."""
+        output = render(FIXTURES / "list_continuation.md.txt")
+        self.assertNotIn("```", output, "wrapped list continuation was fenced as code")
+        self.assertIn(
+            "*Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.*",
+            output,
+            "wrapped sentence was not rejoined; the italic span is still broken",
+        )
+
+    def test_prompt_template_is_tagged_text_not_python(self):
+        """Defect: ASR prompt templates were tagged `python` because bash detection failed."""
+        output = render(FIXTURES / "prompt_template.md.txt")
+        self.assertIn("```text\nTranscribe the following speech segment", output)
+        self.assertNotIn("```python\nTranscribe", output)
+
+    def test_prompt_template_keeps_its_bullets_in_one_block(self):
+        """Defect: an indented bullet ended the block, splitting one prompt in two."""
+        output = render(FIXTURES / "prompt_template.md.txt")
+        block = output.split("```text\n", 1)[1].split("```", 1)[0]
+        self.assertIn("Only output the transcription", block)
+        self.assertIn("When transcribing numbers", block)
+
+    def test_blank_lines_do_not_split_a_code_block(self):
+        """Defect: two blank lines mid-cell split one function across two fences."""
+        output = render(FIXTURES / "split_code_block.md.txt")
+        self.assertEqual(
+            output.count("```python"), 1,
+            "code block was split -- consecutive blank lines ended it early",
+        )
+        block = output.split("```python\n", 1)[1].split("```", 1)[0]
+        self.assertIn("binary_mask = (mask > 0.5).astype(np.uint8)", block)
+        self.assertIn("full_mask[y1:y2, x1:x2] = np.maximum", block)
+        self.assertIn("return full_mask", block)
+
+    def test_real_code_still_detected(self):
+        """The text fallback must not swallow genuine python and bash blocks."""
+        output = render(FIXTURES / "prompt_template.md.txt")
+        self.assertIn("```python\nfrom transformers import pipeline", output)
+        self.assertIn("```bash\npip install --upgrade transformers", output)
+
+    def test_data_uri_inside_code_is_preserved(self):
+        """Only markdown images are extracted -- a data URI in a snippet is code."""
+        with tempfile.TemporaryDirectory() as tmp:
+            output = render(FIXTURES / "base64_image.md.txt", Path(tmp))
+            written = sorted(p.name for p in Path(tmp).iterdir())
+        self.assertIn('return f"data:image/jpeg;base64,{image_b64}"', output)
+        self.assertNotIn("](data:image", output, "markdown data URI was not extracted")
+        self.assertEqual(
+            written,
+            ["base64_image-01.png", "base64_image-02.png"],
+            "expected exactly the two markdown images to be extracted",
+        )
+
+    def test_extracted_images_are_valid_files(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            render(FIXTURES / "base64_image.md.txt", Path(tmp))
+            for path in Path(tmp).iterdir():
+                self.assertTrue(path.stat().st_size > 0, f"{path.name} is empty")
+                self.assertEqual(
+                    path.read_bytes()[:8],
+                    b"\x89PNG\r\n\x1a\n",
+                    f"{path.name} is not a valid PNG",
+                )
+
+    def test_images_left_alone_without_assets_dir(self):
+        """No assets dir means no silent data loss -- the payload stays put."""
+        output = render(FIXTURES / "base64_image.md.txt", None)
+        self.assertIn("](data:image/png;base64,", output)
+
+    def test_devsite_admonitions_converted(self):
+        """Already working before Step 0.2; pinned so it stays that way."""
+        output = render(FIXTURES / "admonitions.md.txt")
+        self.assertIn("!!! Note", output)
+        self.assertIn("!!! Warning", output)
+        self.assertNotIn("> [!", output)
+
+    def test_tables_get_separator_rows(self):
+        output = render(FIXTURES / "admonitions.md.txt")
+        self.assertIn("|---|---|---|", output)
+
+
+def update_expected() -> int:
+    for source in fixtures():
+        expected_path = source.with_suffix("").with_suffix(".expected.md")
+        with tempfile.TemporaryDirectory() as tmp:
+            expected_path.write_text(render(source, Path(tmp)), encoding="utf-8")
+        print(f"  wrote {expected_path.relative_to(FIXTURES.parent.parent)}")
+    print("\nReview the diff before committing -- these files are the specification.")
+    return 0
+
+
+if __name__ == "__main__":
+    if "--update" in sys.argv:
+        sys.exit(update_expected())
+    unittest.main(verbosity=2)

From cd94fe73414296673de31a33337b005922ef4d08 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 11:23:10 +0200
Subject: [PATCH 02/20] Minor fixes to the get started pages

---
 .lintbaseline.json                     |  5 +--
 PLAN.md                                | 44 ++++++++++++++++++++++----
 docs/get-started/capabilities/audio.md | 10 +++---
 docs/get-started/capabilities/image.md |  6 ----
 scripts/lint_docs.py                   | 13 +++++++-
 5 files changed, 55 insertions(+), 23 deletions(-)

diff --git a/.lintbaseline.json b/.lintbaseline.json
index e015dc5..353332d 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -11,11 +11,8 @@
     "admonitions",
     "size"
   ],
-  "count": 290,
+  "count": 285,
   "known": [
-    "fences|docs/get-started/capabilities/audio.md|prompt template tagged as python: 'Transcribe the following speech segment in {LANGUAGE} into {...'",
-    "fences|docs/get-started/capabilities/audio.md|prompt template tagged as python: 'Transcribe the following speech segment in {SOURCE_LANGUAGE}...'",
-    "fences|docs/get-started/capabilities/image.md|`python` block split in two -- likely blank lines inside one block",
     "frontmatter|docs/community/discord.md|no frontmatter block",
     "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
     "frontmatter|docs/community/gemmaverse.md|no frontmatter block",
diff --git a/PLAN.md b/PLAN.md
index b6982f5..581ed52 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -253,13 +253,43 @@ Two different strategies, because the two sections have different histories:
 - [x] `models/releases.md`: 58-byte stub → full port (+4.2 KB).
 - **Verify:** `uv run python scripts/port_docs.py --filter models/ --dry-run` → 37 ok, 0 failed.
 
-#### 0.3c — Get Started targeted repair (remaining)
-- [ ] `capabilities/audio.md:90` and `:173` — ASR prompt templates tagged `python`, should be
-      `text`. Change the fence language only.
-- [ ] `capabilities/image.md:315`, `:402`, `:484` — three split code blocks. Rejoin them; the
-      converter now handles this correctly, so its output is the reference.
-- **Verify:** `uv run python scripts/lint_docs.py --check fences` → zero.
-- **Done when:** zero fence findings anywhere.
+#### 0.3c — Get Started targeted repair ✅
+
+Diffing each page against a fresh conversion first confirmed both carry real hand-edits, so
+repair-in-place was the correct call:
+
+| Page | Hand-edits preserved |
+|---|---|
+| `capabilities/image.md` | title recased, `## Summary` edit, `<!-- TODO: Port over -->` block |
+| `capabilities/audio.md` | hand-added `# Audio Understanding`, `## Summary` edit, TODO block |
+
+- [x] `capabilities/audio.md` — two ASR prompt templates `python` → `text`.
+- [x] `capabilities/audio.md:81` — unconverted `> > [!NOTE]` callout → `!!! Note`.
+- [x] `capabilities/image.md` — three split blocks rejoined. Diff is exactly 6 lines
+      (the fence pairs); no content touched.
+- [x] New linter coverage: GitHub-style `> [!NOTE]` callouts. The `admonitions` check only
+      knew about devsite `Note:` prefixes, so this defect was invisible.
+- **Verify:** `uv run python scripts/lint_docs.py --check fences,admonitions` → zero.
+- **Done when:** ✅ zero fence and admonition findings site-wide; remaining diff vs. a fresh
+      conversion is hand-edits only.
+
+**Technique worth reusing:** diffing a page against a fresh conversion cleanly separates
+*bugs* from *hand-edits*. Do this before touching any page whose history is unclear — it is the
+manual form of the `--divergence` check and it is what proved re-porting would have been wrong here.
+
+### Phase 0 progress: 300 → 285
+
+| Check | Start | Now | |
+|---|---|---|---|
+| `fences` | 7 | **0** | ✅ cleared |
+| `size` | 1 | **0** | ✅ cleared |
+| `admonitions` | 0 | **0** | ✅ 1 found and fixed |
+| `internal` | 0 | **0** | ✅ |
+| `images` | 52 | **18** | base64 done; 18 hotlinked remain → Step 0.4 |
+| `stubs` | 33 | **32** | → Steps 2.2–2.4 |
+| `orphans` | 4 | **4** | → Step 2.1 |
+| `frontmatter` | 81 | **81** | → Step 3.1 |
+| `links` | 122 | **150** | +28 from `releases.md` gaining content → Step 0.5 |
 
 **Backlog after the Models port — 300 → 290:**
 
diff --git a/docs/get-started/capabilities/audio.md b/docs/get-started/capabilities/audio.md
index a22865f..fa783c0 100644
--- a/docs/get-started/capabilities/audio.md
+++ b/docs/get-started/capabilities/audio.md
@@ -78,16 +78,16 @@ signed 16-bit PCM integer WAV files at 44.1 kHz, follow these steps:
 - Downmix from stereo to mono by averaging the 2 channels
 - Convert from int16 to float32, and divide by 32768.0 to scale to the range \[-1, 1\]
 
-> > [!NOTE]
-> > **Note:** When resampling audio to 16 kHz, you should use a Fourier method for best results, such as `scipy.signal.resample` or `librosa.sample(res_type ='scipy')`.
->
+!!! Note
+    When resampling audio to 16 kHz, you should use a Fourier method for best results, such as `scipy.signal.resample` or `librosa.sample(res_type ='scipy')`.
+
 ## Speech to text
 
 Gemma 4 E2B, E4B, and 12B Unified are trained for multilingual speech recognition, allowing you to transcribe audio input in various languages into text.
 
 Use the following prompt structure for **Audio Speech Recognition (ASR)**.
 
-```python
+```text
 Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
 
 Follow these specific instructions for formatting the answer:
@@ -170,7 +170,7 @@ Gemma 4 E2B, E4B and 12B Unified are trained for multilingual speech translation
 
 Use the following prompt structure for **Automatic Speech Translation (AST)**.
 
-```python
+```text
 Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.
 When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.
 ```
diff --git a/docs/get-started/capabilities/image.md b/docs/get-started/capabilities/image.md
index 61dc95a..6dc8d37 100644
--- a/docs/get-started/capabilities/image.md
+++ b/docs/get-started/capabilities/image.md
@@ -309,10 +309,8 @@ def draw_results(text_content):
     return output_image
   else:
     print("No JSON code block found.")
-```
 
 
-```python
 messages = [
     {
         "role": "user", "content": [
@@ -396,10 +394,8 @@ def draw_results(text_content):
         output_img = draw_bounding_box(output_img, item["box_2d"], item["label"], label_colors, w, h)
 
     return Image.fromarray(output_img)
-```
 
 
-```python
 # Detect person, card, and traffic light
 messages = [
     {
@@ -478,10 +474,8 @@ messages = [
         ]
      }
 ]
-```
 
 
-```python
 # Run for different budget sizes
 budget_sizes = [70, 140, 280, 560]
 # 1120 won't fit on T4, but works on L4 or highger
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index 8628745..24f0383 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -62,6 +62,10 @@
 # Devsite callouts that should have become admonitions during conversion.
 DEVSITE_CALLOUTS = ("Note:", "Key Point:", "Caution:", "Warning:", "Important:", "Tip:")
 
+# GitHub-style callouts, which devsite also emits: `> [!NOTE]`, sometimes double-quoted as
+# `> > [!NOTE]`. These must become `!!! note` too.
+GITHUB_CALLOUT = re.compile(r"^\s*(?:>\s*)+\[!(NOTE|IMPORTANT|WARNING|TIP|CAUTION)\]", re.IGNORECASE)
+
 # Signals that a code block really is Python. Absence of all of these in a block tagged
 # `python` means prose or a prompt template got captured by the converter.
 PY_SIGNALS = re.compile(
@@ -385,9 +389,16 @@ def check_fences(ctx: Context) -> Iterable[Finding]:
 
 
 def check_admonitions(ctx: Context) -> Iterable[Finding]:
-    """Devsite callouts should be Zensical admonitions."""
+    """Devsite and GitHub callouts should both be Zensical admonitions."""
     for page in ctx.pages:
         for lineno, line in outside_code(page):
+            github = GITHUB_CALLOUT.match(line)
+            if github:
+                yield Finding(
+                    "admonitions", page.repo_rel, lineno,
+                    f"unconverted callout: '[!{github.group(1).upper()}]' -- use !!! admonition",
+                )
+                continue
             stripped = line.lstrip("> ").lstrip()
             for callout in DEVSITE_CALLOUTS:
                 if stripped.startswith(callout):

From 72c733c0103bd906b18d262443b8837bb0cb7fa8 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 11:31:11 +0200
Subject: [PATCH 03/20] Port images over

---
 .lintbaseline.json                            |  21 +--
 PLAN.md                                       |  53 ++++++-
 .../assets/capabilities/output_0f334997_1.png | Bin 0 -> 299654 bytes
 .../assets/capabilities/output_6b3b62e7_0.png | Bin 0 -> 732596 bytes
 .../assets/capabilities/output_6b3b62e7_1.png | Bin 0 -> 611323 bytes
 .../assets/capabilities/output_76f17518_3.png | Bin 0 -> 493628 bytes
 .../assets/capabilities/output_a6927bd0_0.png | Bin 0 -> 945574 bytes
 .../assets/capabilities/output_b041486b_1.png | Bin 0 -> 1117218 bytes
 .../assets/capabilities/output_bc2920e6_1.png | Bin 0 -> 887206 bytes
 .../assets/capabilities/output_ebb5fd08_0.png | Bin 0 -> 602400 bytes
 .../assets/diffusiongemma/ar_vs_diffusion.png | Bin 0 -> 83190 bytes
 .../assets/diffusiongemma/cond_overview-1.png | Bin 0 -> 304977 bytes
 .../md_forward_reverse_diffusion.png          | Bin 0 -> 120694 bytes
 .../diffusiongemma/output_Gsxfo7TuN0Vu_0.png  | Bin 0 -> 945574 bytes
 docs/assets/diffusiongemma/patch_encoder.png  | Bin 0 -> 217061 bytes
 .../usd_forward_reverse_diffusion.png         | Bin 0 -> 129942 bytes
 .../functiongemma/functiongemma_flow.png      | Bin 0 -> 131519 bytes
 .../functiongemma/output_6JBty2rq05Jz_0.png   | Bin 0 -> 26708 bytes
 docs/assets/gemma-3n/gemma-3n-parameters.png  | Bin 0 -> 246645 bytes
 docs/assets/gemma-4/gemma4_banner.png         | Bin 0 -> 185664 bytes
 docs/get-started/capabilities/image.md        |  14 +-
 docs/get-started/capabilities/thinking.md     |   2 +-
 docs/models/diffusiongemma/explained.md       |  10 +-
 docs/models/diffusiongemma/inference.md       |   2 +-
 docs/models/functiongemma/fine-tuning.md      |   2 +-
 docs/models/functiongemma/formatting.md       |   2 +-
 docs/models/gemma-3n/index.md                 |   2 +-
 docs/models/gemma-4/model-card.md             |   2 +-
 scripts/check_fidelity.py                     |  14 +-
 scripts/port_docs.py                          |   7 +
 scripts/vendor_images.py                      | 136 ++++++++++++++++++
 31 files changed, 221 insertions(+), 46 deletions(-)
 create mode 100644 docs/assets/capabilities/output_0f334997_1.png
 create mode 100644 docs/assets/capabilities/output_6b3b62e7_0.png
 create mode 100644 docs/assets/capabilities/output_6b3b62e7_1.png
 create mode 100644 docs/assets/capabilities/output_76f17518_3.png
 create mode 100644 docs/assets/capabilities/output_a6927bd0_0.png
 create mode 100644 docs/assets/capabilities/output_b041486b_1.png
 create mode 100644 docs/assets/capabilities/output_bc2920e6_1.png
 create mode 100644 docs/assets/capabilities/output_ebb5fd08_0.png
 create mode 100644 docs/assets/diffusiongemma/ar_vs_diffusion.png
 create mode 100644 docs/assets/diffusiongemma/cond_overview-1.png
 create mode 100644 docs/assets/diffusiongemma/md_forward_reverse_diffusion.png
 create mode 100644 docs/assets/diffusiongemma/output_Gsxfo7TuN0Vu_0.png
 create mode 100644 docs/assets/diffusiongemma/patch_encoder.png
 create mode 100644 docs/assets/diffusiongemma/usd_forward_reverse_diffusion.png
 create mode 100644 docs/assets/functiongemma/functiongemma_flow.png
 create mode 100644 docs/assets/functiongemma/output_6JBty2rq05Jz_0.png
 create mode 100644 docs/assets/gemma-3n/gemma-3n-parameters.png
 create mode 100644 docs/assets/gemma-4/gemma4_banner.png
 create mode 100644 scripts/vendor_images.py

diff --git a/.lintbaseline.json b/.lintbaseline.json
index 353332d..543e6cf 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -11,7 +11,7 @@
     "admonitions",
     "size"
   ],
-  "count": 285,
+  "count": 266,
   "known": [
     "frontmatter|docs/community/discord.md|no frontmatter block",
     "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
@@ -94,24 +94,6 @@
     "frontmatter|docs/tutorials/visual-guides/gemma-4-12b.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/gemma-4.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/mtp.md|no frontmatter block",
-    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_6b3b62e7_0.png",
-    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_6b3b62e7_1.png",
-    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_76f17518_3.png",
-    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_a6927bd0_0.png",
-    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_b041486b_1.png",
-    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_bc2920e6_1.png",
-    "images|docs/get-started/capabilities/image.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_ebb5fd08_0.png",
-    "images|docs/get-started/capabilities/thinking.md|hotlinked image: https://ai.google.dev/static/gemma/docs/capabilities/thinking_files/output_0f334997_1.png",
-    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/ar_vs_diffusion.png",
-    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/cond_overview-1.png",
-    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/md_forward_reverse_diffusion.png",
-    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/patch_encoder.png",
-    "images|docs/models/diffusiongemma/explained.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/images/usd_forward_reverse_diffusion.png",
-    "images|docs/models/diffusiongemma/inference.md|hotlinked image: https://ai.google.dev/static/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf_files/output_Gsxfo7TuN0Vu_0.png",
-    "images|docs/models/functiongemma/fine-tuning.md|hotlinked image: https://ai.google.dev/static/gemma/docs/functiongemma/finetuning-with-functiongemma_files/output_6JBty2rq05Jz_0.png",
-    "images|docs/models/functiongemma/formatting.md|hotlinked image: https://ai.google.dev/gemma/docs/images/functiongemma_flow.png",
-    "images|docs/models/gemma-3n/index.md|hotlinked image: https://ai.google.dev/static/gemma/docs/images/gemma-3n-parameters.png",
-    "images|docs/models/gemma-4/model-card.md|hotlinked image: https://ai.google.dev/static/gemma/images/gemma4_banner.png",
     "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/audio/roses-are.wav",
     "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/thinking",
     "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision",
@@ -157,7 +139,6 @@
     "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure",
     "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
     "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma",
-    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/images/functiongemma_flow.png",
     "links|docs/models/functiongemma/full-sequence.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
     "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
     "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices",
diff --git a/PLAN.md b/PLAN.md
index 581ed52..fa05880 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -310,12 +310,30 @@ uv run python scripts/port_docs.py --filter models/ --dry-run
 uv run python scripts/check_fidelity.py --manifest --cache-dir .sources
 ```
 
-### Step 0.4 — Vendor all images
-- [ ] Download the 18 hotlinked images into `docs/assets/` organized by section.
-- [ ] Rewrite all image references to local paths.
-- [ ] Add dark-mode variants for diagrams where the source is light-only.
-- **Verify:** `uv run zensical build` then grep the `site/` output for `ai.google.dev/static` — zero hits.
-- **Done when:** the site renders fully with no network access to Google.
+### Step 0.4 — Vendor all images ✅
+- [x] `scripts/vendor_images.py` — downloads remote images into `docs/assets/<section>/` and
+      rewrites the references. Section = the page's parent directory, matching the layout
+      `port_docs.py` already uses. Original filenames are kept so any asset traces back to its
+      upstream URL. A failed download leaves the reference untouched (a broken local path is
+      worse than a working remote one) and the script is idempotent, so re-running retries.
+- [x] All **18** hotlinked images vendored, 18/18 succeeded. `docs/assets/` is now 8.2 MB.
+- [x] Fidelity checker taught to ignore image *targets* on both sides — only alt text is prose.
+      Without this, vendoring read as 6 pages losing prose and would have **blocked those pages
+      from ever being re-ported**, since `port_docs.py` is fidelity-gated.
+- [ ] ~~Dark-mode variants~~ — **deferred to Phase 1** with the rest of the design system.
+- **Verify:** ✅ zero `ai.google.dev/static` references in the built `site/`; all 214 local
+      `<img>` sources resolve on disk.
+- **Done when:** ✅ the site renders fully with no network access to Google.
+
+!!! warning "Ordering: port, then vendor"
+    Conversion restores the upstream image URLs, so **re-porting a page undoes its vendoring**
+    (confirmed: re-porting `models/gemma-4/model-card.md` would revert one image, +21 bytes).
+    Always run `vendor_images.py` after `port_docs.py`. `port_docs.py` prints this reminder, and
+    since the `images` check now sits at zero, a missed run fails CI.
+
+```bash
+uv run python scripts/vendor_images.py --dry-run
+```
 
 ### Step 0.5 — Rewrite internal links
 - [ ] Map all 122 `ai.google.dev/gemma/docs/*` links to local paths.
@@ -619,6 +637,16 @@ Must cover:
 
 ---
 
+## Pipeline order
+
+Steps that rewrite pages must run in this order, because each stage can undo the previous one:
+
+```
+port_docs.py  ->  vendor_images.py  ->  lint_docs.py --baseline
+```
+
+`check_fidelity.py --manifest` can run at any point; it ignores image targets by design.
+
 ## Notes
 
 *Agents: record surprises, deviations, and anything that changes the plan here.*
@@ -675,3 +703,16 @@ Must cover:
   `docs/assets/` while claiming to change nothing.
 - Two blank lines mid-cell is common in notebook exports — worth checking for whenever a new
   converter bug is suspected.
+
+**2026-08-03 (Step 0.4)**
+
+- **Conversion and vendoring fight each other.** Converting restores upstream image URLs;
+  vendoring replaces them with local paths. Always port first, then vendor. See *Pipeline order*.
+- Any check that gates writes (as `port_docs.py` does on fidelity) must be **precise about what
+  is allowed to change**, or it locks pages out of future edits. Vendoring tripped the fidelity
+  check on 6 pages until image targets were excluded from the word comparison.
+- `docs/assets/` is 8.2 MB, most of it `capabilities/` (7 notebook-output PNGs, 5.5 MB). If repo
+  size becomes a concern, those are the candidates for recompression — not the diagrams.
+- The site's `404.html` uses root-absolute `/assets/` paths. That is correct (a 404 can be served
+  from any depth) and will look like a broken link to any checker resolving paths relative to the
+  file. Don't "fix" it.
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literal 0
HcmV?d00001

diff --git a/docs/get-started/capabilities/image.md b/docs/get-started/capabilities/image.md
index 6dc8d37..5635f30 100644
--- a/docs/get-started/capabilities/image.md
+++ b/docs/get-started/capabilities/image.md
@@ -76,7 +76,7 @@ output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)
 print(output[0]['generated_text'])
 ```
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_a6927bd0_0.png)
+![png](../../assets/capabilities/output_a6927bd0_0.png)
 
 ``` { .no-copy .output }
 This image shows the **Golden Gate Bridge** in San Francisco, California.
@@ -127,9 +127,9 @@ output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)
 print(output[0]['generated_text'])
 ```
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_6b3b62e7_0.png)
+![png](../../assets/capabilities/output_6b3b62e7_0.png)
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_6b3b62e7_1.png)
+![png](../../assets/capabilities/output_6b3b62e7_1.png)
 
 ``` { .no-copy .output }
 Here are captions for the two images you provided:
@@ -217,7 +217,7 @@ output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)
 print(output[0]['generated_text'])
 ```
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_ebb5fd08_0.png)
+![png](../../assets/capabilities/output_ebb5fd08_0.png)
 
 ``` { .no-copy .output }
 The sign in the image says:
@@ -335,7 +335,7 @@ draw_results(output[0]['generated_text'])
 ```<turn|>
 ````
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_b041486b_1.png)
+![png](../../assets/capabilities/output_b041486b_1.png)
 
 ## Variable Resolution (Token Budget)
 
@@ -422,7 +422,7 @@ draw_results(output[0]['generated_text'])
 ```<turn|>
 ````
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_bc2920e6_1.png)
+![png](../../assets/capabilities/output_bc2920e6_1.png)
 
 It does alright but it is clear that the image is being compressed quite a bit as it does not detect all cars and persons. A higher token budget should resolve this!
 
@@ -557,7 +557,7 @@ Budget Size: 560
 ```<turn|>
 ````
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/vision/image_files/output_76f17518_3.png)
+![png](../../assets/capabilities/output_76f17518_3.png)
 
 ## Summary
 
diff --git a/docs/get-started/capabilities/thinking.md b/docs/get-started/capabilities/thinking.md
index 4a00afd..d12da8b 100644
--- a/docs/get-started/capabilities/thinking.md
+++ b/docs/get-started/capabilities/thinking.md
@@ -434,7 +434,7 @@ for key, value in result.items():
 === Downloaded image ===
 ```
 
-![png](https://ai.google.dev/static/gemma/docs/capabilities/thinking_files/output_0f334997_1.png)
+![png](../../assets/capabilities/output_0f334997_1.png)
 
 ``` { .no-copy .output }
 Role: assistant
diff --git a/docs/models/diffusiongemma/explained.md b/docs/models/diffusiongemma/explained.md
index 747cffa..41acf79 100644
--- a/docs/models/diffusiongemma/explained.md
+++ b/docs/models/diffusiongemma/explained.md
@@ -5,7 +5,7 @@ standard language models and how text-based diffusion differs.
 
 ## The Problem with Autoregressive Models
 
-![autoregressive versus diffusion](https://ai.google.dev/static/gemma/docs/diffusiongemma/images/ar_vs_diffusion.png)
+![autoregressive versus diffusion](../../assets/diffusiongemma/ar_vs_diffusion.png)
 
 Many Large Language Models (LLMs) are **autoregressive**, meaning they generate
 text one single token at a time. While this approach works well for serving many
@@ -22,7 +22,7 @@ amount of time for 1 user as it does for 256 users grouped together.
 Consequently, an individual user sees no latency advantage; the hardware's
 computational capacity sits idle while waiting on memory transfers.
 
-![DiffusionGemma overview](https://ai.google.dev/static/gemma/docs/diffusiongemma/images/cond_overview-1.png)
+![DiffusionGemma overview](../../assets/diffusiongemma/cond_overview-1.png)
 
 **DiffusionGemma** utilizes this idle compute time for the individual user.
 Instead of generating 1 token for 256 separate users, it generates **256 tokens
@@ -56,7 +56,7 @@ specialized methodologies:
 
 ### 1. Masked Diffusion
 
-![Masked Diffusion](https://ai.google.dev/static/gemma/docs/diffusiongemma/images/md_forward_reverse_diffusion.png)
+![Masked Diffusion](../../assets/diffusiongemma/md_forward_reverse_diffusion.png)
 
 Early text diffusion relied on masking, similar to BERT training. Random tokens
 in a sequence are replaced with a `[MASK]` token (representing noise). During
@@ -69,7 +69,7 @@ the surrounding context changes.
 
 ### 2. Uniform State Diffusion
 
-![Uniform State Diffusion](https://ai.google.dev/static/gemma/docs/diffusiongemma/images/usd_forward_reverse_diffusion.png)
+![Uniform State Diffusion](../../assets/diffusiongemma/usd_forward_reverse_diffusion.png)
 
 To resolve the limitations of masking, DiffusionGemma uses **Uniform State
 Diffusion** . Instead of an explicit `[MASK]` token, noise is introduced by
@@ -84,7 +84,7 @@ refinement.
 
 ## Architecture: The incremental prefill and denoising
 
-![Block Autoregressive Denoising](https://ai.google.dev/static/gemma/docs/diffusiongemma/images/patch_encoder.png)
+![Block Autoregressive Denoising](../../assets/diffusiongemma/patch_encoder.png)
 
 DiffusionGemma implements Uniform State Diffusion efficiently by alternating
 between **Incremental Prefill** and **Denoising**. Gemma 4 26B A4B model isn't
diff --git a/docs/models/diffusiongemma/inference.md b/docs/models/diffusiongemma/inference.md
index c37222d..e2f6204 100644
--- a/docs/models/diffusiongemma/inference.md
+++ b/docs/models/diffusiongemma/inference.md
@@ -568,7 +568,7 @@ text = processor.decode(output[0], skip_special_tokens=False)
 print(text[0])
 ```
 
-![png](https://ai.google.dev/static/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf_files/output_Gsxfo7TuN0Vu_0.png)
+![png](../../assets/diffusiongemma/output_Gsxfo7TuN0Vu_0.png)
 
 ``` { .no-copy .output }
 <bos><|turn>user
diff --git a/docs/models/functiongemma/fine-tuning.md b/docs/models/functiongemma/fine-tuning.md
index 3d403d3..02e5a06 100644
--- a/docs/models/functiongemma/fine-tuning.md
+++ b/docs/models/functiongemma/fine-tuning.md
@@ -515,7 +515,7 @@ plt.grid(True)
 plt.show()
 ```
 
-![png](https://ai.google.dev/static/gemma/docs/functiongemma/finetuning-with-functiongemma_files/output_6JBty2rq05Jz_0.png)
+![png](../../assets/functiongemma/output_6JBty2rq05Jz_0.png)
 
 ## Test Model Inference
 
diff --git a/docs/models/functiongemma/formatting.md b/docs/models/functiongemma/formatting.md
index 9ea0639..e51fe48 100644
--- a/docs/models/functiongemma/formatting.md
+++ b/docs/models/functiongemma/formatting.md
@@ -134,7 +134,7 @@ successfully makes the connection:
 Here is a complete, step-by-step formatted example demonstrating the flow for
 using the `get_current_weather` tool.
 
-![A diagram of Weather Tool Flow](https://ai.google.dev/gemma/docs/images/functiongemma_flow.png)
+![A diagram of Weather Tool Flow](../../assets/functiongemma/functiongemma_flow.png)
 
 ### Turn 1: Tool Definition (Developer)
 
diff --git a/docs/models/gemma-3n/index.md b/docs/models/gemma-3n/index.md
index ae28500..f68ab1b 100644
--- a/docs/models/gemma-3n/index.md
+++ b/docs/models/gemma-3n/index.md
@@ -56,7 +56,7 @@ However, using parameter skipping and PLE caching techniques, this model can be
 operated with an effective memory load of just under 2 billion (1.91B)
 parameters, as illustrated in Figure 1.
 
-![Gemma 3n diagram of parameter usage](https://ai.google.dev/static/gemma/docs/images/gemma-3n-parameters.png)
+![Gemma 3n diagram of parameter usage](../../assets/gemma-3n/gemma-3n-parameters.png)
 
 **Figure 1.** Gemma 3n E2B model parameters running in standard execution
 versus an effectively lower parameter load using PLE caching and parameter
diff --git a/docs/models/gemma-4/model-card.md b/docs/models/gemma-4/model-card.md
index a0a4f62..559433c 100644
--- a/docs/models/gemma-4/model-card.md
+++ b/docs/models/gemma-4/model-card.md
@@ -1,4 +1,4 @@
-![Gemma 4 Banner](https://ai.google.dev/static/gemma/images/gemma4_banner.png)
+![Gemma 4 Banner](../../assets/gemma-4/gemma4_banner.png)
 
 
 [Hugging Face](https://huggingface.co/collections/google/gemma-4) \|
diff --git a/scripts/check_fidelity.py b/scripts/check_fidelity.py
index f6b673f..8d9db17 100644
--- a/scripts/check_fidelity.py
+++ b/scripts/check_fidelity.py
@@ -38,6 +38,13 @@
 BASE64_PAYLOAD = re.compile(r"data:image/[A-Za-z0-9.+-]+;base64,[A-Za-z0-9+/=\s]+")
 NOTEBOOK_PREAMBLE = re.compile(r"^This notebook will run on\b", re.IGNORECASE)
 
+# Image *locations* legitimately change during the port -- base64 payloads become files
+# (port_docs.py) and hotlinks become local paths (vendor_images.py). Only the alt text is prose,
+# so the target is blanked on both sides before comparing. Without this, vendoring an image
+# reads as six dropped words and would block the page from ever being re-ported.
+MD_IMAGE_TARGET = re.compile(r"(!\[[^\]]*\]\()[^)]*(\))")
+HTML_IMAGE_TARGET = re.compile(r"(<img[^>]+src=[\"'])[^\"']*([\"'])")
+
 
 def strip_designed_drops(source_text: str) -> str:
     """Remove the lines convert_notebook is *designed* to discard.
@@ -67,8 +74,11 @@ def strip_designed_drops(source_text: str) -> str:
 
 
 def words(text: str) -> Counter:
-    """Word multiset, with base64 payloads removed so extraction isn't reported as loss."""
-    return Counter(WORD.findall(BASE64_PAYLOAD.sub(" ", text)))
+    """Word multiset, ignoring image targets and base64 payloads (see the regexes above)."""
+    text = BASE64_PAYLOAD.sub(" ", text)
+    text = MD_IMAGE_TARGET.sub(r"\1\2", text)
+    text = HTML_IMAGE_TARGET.sub(r"\1\2", text)
+    return Counter(WORD.findall(text))
 
 
 def compare(source_text: str, converted_text: str) -> tuple[Counter, Counter]:
diff --git a/scripts/port_docs.py b/scripts/port_docs.py
index ddf25dd..c62a0bf 100644
--- a/scripts/port_docs.py
+++ b/scripts/port_docs.py
@@ -111,6 +111,13 @@ def main() -> int:
             failures.append(entry["path"])
 
     print(f"\n{len(selected) - len(failures)} ok, {len(failures)} failed")
+
+    if not args.dry_run and len(selected) > len(failures):
+        # Conversion restores the upstream image URLs, undoing any previous vendoring.
+        # The linter's `images` check is at zero, so a missed run fails CI -- but say it here
+        # rather than making someone discover it from a red build.
+        print("\nNext: re-vendor images, which this run reverted to upstream URLs:")
+        print("  uv run python scripts/vendor_images.py")
     if failures:
         print("\nFidelity failures (not written):")
         for path in failures:
diff --git a/scripts/vendor_images.py b/scripts/vendor_images.py
new file mode 100644
index 0000000..b8a2210
--- /dev/null
+++ b/scripts/vendor_images.py
@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+"""
+Download hotlinked images into docs/assets/ and rewrite the references.
+
+The port inherited images pointing at ai.google.dev/static. That makes the site depend on
+Google's CDN staying put, blocks offline builds, and leaves no way to add dark-mode variants.
+This vendors them.
+
+    python scripts/vendor_images.py --dry-run    # show what would be downloaded
+    python scripts/vendor_images.py              # download and rewrite
+
+Assets are grouped by the page's parent directory, matching the layout port_docs.py already
+uses (docs/assets/paligemma/, docs/assets/diffusiongemma/, ...). Original filenames are kept
+so an asset can always be traced back to its upstream URL.
+
+A download failure leaves the reference untouched -- a broken local path is worse than a
+working remote one. Re-run after fixing; the script is idempotent.
+
+Dark-mode variants are deliberately out of scope; see PLAN.md Phase 1.
+"""
+
+from __future__ import annotations
+
+import argparse
+import re
+import sys
+import urllib.error
+import urllib.request
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from lint_docs import load_pages, outside_code  # noqa: E402
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DOCS_DIR = REPO_ROOT / "docs"
+ASSETS_DIR = DOCS_DIR / "assets"
+
+REMOTE_IMAGE = re.compile(r"!\[([^\]]*)\]\((https?://[^)\s]+)\)")
+HTML_IMAGE = re.compile(r"(<img[^>]+src=[\"'])(https?://[^\"']+)([\"'])")
+UNSAFE = re.compile(r"[^A-Za-z0-9._-]")
+
+
+def asset_section(page_rel: str) -> str:
+    """Directory under docs/assets/ for a page's images: its parent directory name."""
+    parent = Path(page_rel).parent
+    return parent.name or "shared"
+
+
+def safe_filename(url: str) -> str:
+    name = url.split("?")[0].rstrip("/").rsplit("/", 1)[-1]
+    return UNSAFE.sub("-", name) or "image.png"
+
+
+def download(url: str, target: Path) -> tuple[bool, str]:
+    if target.exists():
+        return True, "cached"
+    try:
+        req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+        with urllib.request.urlopen(req, timeout=30) as resp:
+            data = resp.read()
+    except (urllib.error.URLError, urllib.error.HTTPError, TimeoutError) as exc:
+        return False, f"{type(exc).__name__}: {exc}"
+    if not data:
+        return False, "empty response"
+    target.parent.mkdir(parents=True, exist_ok=True)
+    target.write_bytes(data)
+    return True, f"{len(data):,} bytes"
+
+
+def collect(page) -> list[tuple[str, str]]:
+    """Remote image URLs on a page, as (url, filename), outside code blocks, deduplicated."""
+    found: dict[str, str] = {}
+    for _, line in outside_code(page):
+        for match in REMOTE_IMAGE.finditer(line):
+            found.setdefault(match.group(2), safe_filename(match.group(2)))
+        for match in HTML_IMAGE.finditer(line):
+            found.setdefault(match.group(2), safe_filename(match.group(2)))
+    return list(found.items())
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("--dry-run", action="store_true")
+    args = parser.parse_args()
+
+    import os
+
+    total = failed = 0
+    for page in load_pages():
+        images = collect(page)
+        if not images:
+            continue
+
+        section = asset_section(page.rel)
+        print(f"\n{page.repo_rel}  ->  docs/assets/{section}/")
+
+        text = page.text
+        rewritten = 0
+        for url, filename in images:
+            total += 1
+            target = ASSETS_DIR / section / filename
+            if args.dry_run:
+                print(f"    would fetch {filename:<45} <- {url}")
+                continue
+
+            ok, detail = download(url, target)
+            if not ok:
+                failed += 1
+                print(f"  !! FAILED   {filename:<45} {detail}")
+                continue
+
+            relative = os.path.relpath(target, page.path.parent).replace("\\", "/")
+            before = text
+            text = text.replace(f"]({url})", f"]({relative})")
+            text = HTML_IMAGE.sub(
+                lambda m: m.group(1) + relative + m.group(3) if m.group(2) == url else m.group(0),
+                text,
+            )
+            if text != before:
+                rewritten += 1
+            print(f"     ok       {filename:<45} {detail}")
+
+        if not args.dry_run and text != page.text:
+            page.path.write_text(text, encoding="utf-8")
+            print(f"     rewrote {rewritten} reference(s)")
+
+    verb = "would vendor" if args.dry_run else "vendored"
+    print(f"\n{verb} {total - failed}/{total} image(s)")
+    if failed:
+        print(f"{failed} failed -- references left pointing at the original URL; re-run to retry")
+    return 1 if failed else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

From 305e0b5417b3a4707894818f2ca0dad168b2ca99 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 11:45:04 +0200
Subject: [PATCH 04/20] Fix zizmor flags

---
 .github/workflows/deploy.yml | 21 ++++++++++++++------
 .github/workflows/lint.yml   | 13 +++++++++++--
 PLAN.md                      | 37 ++++++++++++++++++++++++++++++++++++
 3 files changed, 63 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 3cc8944..1b09b24 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -6,10 +6,10 @@ on:
       - main
   workflow_dispatch:
 
+# Least privilege at the workflow level; the deploy job widens this for itself.
+# Granting pages/id-token here would hand them to the build job too, which only needs to read.
 permissions:
   contents: read
-  pages: write
-  id-token: write
 
 # Only allow one deployment at a time
 concurrency:
@@ -20,10 +20,15 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      # Actions are pinned to commit SHAs (org policy: zizmor unpinned-uses).
+      # Update a pin by resolving the tag, e.g.:
+      #   gh api repos/actions/checkout/git/ref/tags/v4 --jq '.object.sha'
+      - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 # v4
+        with:
+          persist-credentials: false
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
 
       - name: Build docs
         run: |
@@ -34,17 +39,21 @@ jobs:
             zensical build
 
       - name: Upload Pages artifact
-        uses: actions/upload-pages-artifact@v3
+        uses: actions/upload-pages-artifact@56afc609e74202658d3ffba0e8f6dda462b719fa # v3
         with:
           path: site
 
   deploy:
     needs: build
     runs-on: ubuntu-latest
+    # Scoped to this job only -- these are what actions/deploy-pages needs.
+    permissions:
+      pages: write
+      id-token: write
     environment:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
     steps:
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index c0cf166..7eaa9fb 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -6,13 +6,22 @@ on:
   pull_request:
   workflow_dispatch:
 
+# Least privilege: these jobs only read the repo.
+permissions:
+  contents: read
+
 jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      # Actions are pinned to commit SHAs (org policy: zizmor unpinned-uses).
+      # Update a pin by resolving the tag, e.g.:
+      #   gh api repos/actions/checkout/git/ref/tags/v4 --jq '.object.sha'
+      - uses: actions/checkout@11d5960a326750d5838078e36cf38b85af677262 # v4
+        with:
+          persist-credentials: false
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5
         with:
           python-version: "3.12"
 
diff --git a/PLAN.md b/PLAN.md
index fa05880..e62546e 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -335,6 +335,29 @@ uv run python scripts/check_fidelity.py --manifest --cache-dir .sources
 uv run python scripts/vendor_images.py --dry-run
 ```
 
+### Step 0.4b — CI hardening (zizmor / org policy) ✅
+
+The org runs [zizmor](https://docs.zizmor.sh/) on workflows and **fails on any Medium/High
+finding**, not just the mandatory-check list. Both workflows now pass clean.
+
+- [x] All 6 action references pinned to commit SHAs in `lint.yml` and `deploy.yml`
+      (`unpinned-uses`, High — this is what broke the build).
+- [x] `lint.yml` given an explicit `permissions: contents: read` block.
+- [x] `deploy.yml` permissions narrowed: `contents: read` at workflow level, with
+      `pages: write` / `id-token: write` scoped to the `deploy` job alone. At workflow level they
+      were also being handed to the `build` job, which only needs to read (`excessive-permissions`, High).
+- [x] `persist-credentials: false` on both checkouts (`artipacked`).
+- **Verify:** `uvx zizmor --persona=regular .github/workflows/` → "No findings to report."
+
+!!! note "Updating a pin"
+    Pins carry a `# v4` comment so the intended version stays readable. To move one:
+
+    ```bash
+    gh api repos/actions/checkout/git/ref/tags/v4 --jq '.object.sha'
+    ```
+
+    For annotated tags, dereference: `gh api repos/<repo>/git/tags/<sha> --jq '.object.sha'`.
+
 ### Step 0.5 — Rewrite internal links
 - [ ] Map all 122 `ai.google.dev/gemma/docs/*` links to local paths.
 - [ ] For pages not yet ported, point at the eventual local path and let the linter flag it as
@@ -716,3 +739,17 @@ port_docs.py  ->  vendor_images.py  ->  lint_docs.py --baseline
 - The site's `404.html` uses root-absolute `/assets/` paths. That is correct (a 404 can be served
   from any depth) and will look like a broken link to any checker resolving paths relative to the
   file. Don't "fix" it.
+- ⚠️ **Any new workflow must be zizmor-clean before it is pushed.** Pin every action to a SHA,
+  set an explicit `permissions:` block, and scope write permissions to the job that needs them.
+  Check locally with `uvx zizmor --persona=regular .github/workflows/` — it catches everything
+  CI does and takes seconds. The org fails on Medium/High, which is broader than the published
+  mandatory-check list.
+- **Why zizmor appeared only now, on this branch.** The scanner is "GitHub Actions Scan"
+  (`.github/workflows/github_actions_scan.yml`), injected org-wide by `google-gemma` — it is not
+  in this repo. It triggers on **`pull_request_target`**, and every commit before this branch went
+  *straight to main via push*, so it had never run. `foundation` is the repo's first pull request.
+  The pre-existing `docs.yml` and `deploy.yml` did not pass the policy; they were never scanned.
+- **The scan is diff-scoped.** It flagged only `lint.yml` while `deploy.yml` sat unmodified on the
+  same branch with four unpinned actions. Pre-existing workflow debt therefore stays invisible
+  until a PR touches that file — worth auditing a workflow *before* you need to edit it under
+  time pressure.

From 5311dfa83bc3fd29657a38974e9cb9f228bb2fd3 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 12:20:15 +0200
Subject: [PATCH 05/20] First pass at links

---
 .lintbaseline.json                            | 151 +----
 PLAN.md                                       | 102 +++-
 ...lery-mobile-actions-finetune-challenge.png | Bin 0 -> 200487 bytes
 .../deployment/gallery-mobile-actions.png     | Bin 0 -> 136153 bytes
 docs/assets/learn/bite-webapp.png             | Bin 0 -> 624166 bytes
 .../learn/pipet-2-ext-dev-host-settings.png   | Bin 0 -> 298234 bytes
 docs/assets/learn/pipet-2.png                 | Bin 0 -> 294731 bytes
 docs/assets/tuning/k-mail-webapp.png          | Bin 0 -> 167191 bytes
 docs/deployment/huggingface.md                | 202 ++++++-
 docs/deployment/index.md                      | 124 ++++
 docs/deployment/mobile-actions.md             | 101 ++++
 docs/get-started/capabilities/audio.md        |  10 +-
 .../capabilities/function-calling.md          |   8 +-
 docs/get-started/capabilities/image.md        |   6 +-
 docs/get-started/capabilities/mtp.md          |  43 ++
 docs/get-started/capabilities/text.md         |   6 +-
 docs/get-started/capabilities/thinking.md     |   6 +-
 docs/get-started/capabilities/video.md        |   6 +-
 docs/get-started/prompt-formatting.md         |   2 +-
 docs/get-started/prompt-structure.md          |  63 ++
 docs/get-started/quickstart.md                |  10 +-
 docs/get-started/setup.md                     |  58 ++
 docs/learn/business-email-assistant.md        | 464 +++++++++++++++
 docs/learn/personal-code-assistant.md         | 551 ++++++++++++++++++
 docs/models/datagemma/index.md                |   2 +-
 docs/models/diffusiongemma/index.md           |   4 +-
 docs/models/diffusiongemma/inference.md       |   2 +-
 docs/models/diffusiongemma/model-card.md      |   2 +-
 docs/models/embeddinggemma/fine-tuning.md     |   4 +-
 docs/models/embeddinggemma/index.md           |   4 +-
 docs/models/embeddinggemma/inference.md       |   4 +-
 docs/models/embeddinggemma/model-card.md      |   6 +-
 docs/models/functiongemma/fine-tuning.md      |   4 +-
 docs/models/functiongemma/formatting.md       |   6 +-
 docs/models/functiongemma/full-sequence.md    |   2 +-
 docs/models/functiongemma/index.md            |   8 +-
 docs/models/functiongemma/inference.md        |   4 +-
 docs/models/functiongemma/model-card.md       |   2 +-
 docs/models/gemma-1/index.md                  |   2 +-
 docs/models/gemma-2/index.md                  |   2 +-
 docs/models/gemma-3/index.md                  |   2 +-
 docs/models/gemma-3n/index.md                 |  12 +-
 docs/models/gemma-3n/model-card.md            |   4 +-
 docs/models/gemma-4/index.md                  |  20 +-
 docs/models/gemma-4/model-card.md             |   2 +-
 docs/models/gemma-aps/index.md                |  43 ++
 docs/models/paligemma/fine-tuning.md          |   2 +-
 docs/models/paligemma/index.md                |   6 +-
 docs/models/paligemma/inference.md            |   2 +-
 docs/models/paligemma/model-card-v1.md        |   4 +-
 docs/models/paligemma/model-card-v2.md        |   8 +-
 docs/models/paligemma/prompting.md            |  10 +-
 docs/models/recurrentgemma/fine-tuning.md     |   4 +-
 docs/models/recurrentgemma/index.md           |   2 +-
 docs/models/recurrentgemma/inference.md       |   4 +-
 docs/models/recurrentgemma/model-card.md      |   6 +-
 docs/models/releases.md                       |  56 +-
 docs/models/shieldgemma/index.md              |   6 +-
 docs/models/shieldgemma/model-card-v1.md      |  10 +-
 docs/models/shieldgemma/model-card-v2.md      |  12 +-
 docs/tuning/spoken-language.md                | 491 ++++++++++++++++
 scripts/check_fidelity.py                     |  27 +-
 scripts/link_map.toml                         | 161 +++++
 scripts/lint_docs.py                          |  10 +-
 scripts/rewrite_links.py                      | 235 ++++++++
 scripts/sources.toml                          |  52 ++
 66 files changed, 2880 insertions(+), 282 deletions(-)
 create mode 100644 docs/assets/deployment/gallery-mobile-actions-finetune-challenge.png
 create mode 100644 docs/assets/deployment/gallery-mobile-actions.png
 create mode 100644 docs/assets/learn/bite-webapp.png
 create mode 100644 docs/assets/learn/pipet-2-ext-dev-host-settings.png
 create mode 100644 docs/assets/learn/pipet-2.png
 create mode 100644 docs/assets/tuning/k-mail-webapp.png
 create mode 100644 docs/deployment/index.md
 create mode 100644 docs/deployment/mobile-actions.md
 create mode 100644 docs/get-started/capabilities/mtp.md
 create mode 100644 docs/get-started/prompt-structure.md
 create mode 100644 docs/get-started/setup.md
 create mode 100644 docs/learn/business-email-assistant.md
 create mode 100644 docs/learn/personal-code-assistant.md
 create mode 100644 docs/models/gemma-aps/index.md
 create mode 100644 docs/tuning/spoken-language.md
 create mode 100644 scripts/link_map.toml
 create mode 100644 scripts/rewrite_links.py

diff --git a/.lintbaseline.json b/.lintbaseline.json
index 543e6cf..2944c4b 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -11,7 +11,7 @@
     "admonitions",
     "size"
   ],
-  "count": 266,
+  "count": 136,
   "known": [
     "frontmatter|docs/community/discord.md|no frontmatter block",
     "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
@@ -20,11 +20,13 @@
     "frontmatter|docs/demos/concurrent.md|no frontmatter block",
     "frontmatter|docs/deployment/google-cloud.md|no frontmatter block",
     "frontmatter|docs/deployment/huggingface.md|no frontmatter block",
+    "frontmatter|docs/deployment/index.md|no frontmatter block",
     "frontmatter|docs/deployment/langchain.md|no frontmatter block",
     "frontmatter|docs/deployment/litert.md|no frontmatter block",
     "frontmatter|docs/deployment/llamacpp.md|no frontmatter block",
     "frontmatter|docs/deployment/lmstudio.md|no frontmatter block",
     "frontmatter|docs/deployment/mlx.md|no frontmatter block",
+    "frontmatter|docs/deployment/mobile-actions.md|no frontmatter block",
     "frontmatter|docs/deployment/mobile.md|no frontmatter block",
     "frontmatter|docs/deployment/ollama.md|no frontmatter block",
     "frontmatter|docs/deployment/qat.md|no frontmatter block",
@@ -34,6 +36,7 @@
     "frontmatter|docs/get-started/capabilities/audio.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/function-calling.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/image.md|no frontmatter block",
+    "frontmatter|docs/get-started/capabilities/mtp.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/text.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/thinking.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/video.md|no frontmatter block",
@@ -41,8 +44,12 @@
     "frontmatter|docs/get-started/index.md|no frontmatter block",
     "frontmatter|docs/get-started/prompt-formatting.md|missing required key: description",
     "frontmatter|docs/get-started/prompt-formatting.md|missing required key: title",
+    "frontmatter|docs/get-started/prompt-structure.md|no frontmatter block",
     "frontmatter|docs/get-started/quickstart.md|missing required key: description",
     "frontmatter|docs/get-started/quickstart.md|missing required key: title",
+    "frontmatter|docs/get-started/setup.md|no frontmatter block",
+    "frontmatter|docs/learn/business-email-assistant.md|no frontmatter block",
+    "frontmatter|docs/learn/personal-code-assistant.md|no frontmatter block",
     "frontmatter|docs/models/datagemma/index.md|no frontmatter block",
     "frontmatter|docs/models/diffusiongemma/explained.md|no frontmatter block",
     "frontmatter|docs/models/diffusiongemma/index.md|no frontmatter block",
@@ -65,6 +72,7 @@
     "frontmatter|docs/models/gemma-3n/model-card.md|no frontmatter block",
     "frontmatter|docs/models/gemma-4/index.md|no frontmatter block",
     "frontmatter|docs/models/gemma-4/model-card.md|no frontmatter block",
+    "frontmatter|docs/models/gemma-aps/index.md|no frontmatter block",
     "frontmatter|docs/models/gemma-scope/index.md|no frontmatter block",
     "frontmatter|docs/models/index.md|no frontmatter block",
     "frontmatter|docs/models/paligemma/fine-tuning.md|no frontmatter block",
@@ -89,151 +97,32 @@
     "frontmatter|docs/tuning/keras-lora.md|no frontmatter block",
     "frontmatter|docs/tuning/overview.md|no frontmatter block",
     "frontmatter|docs/tuning/qlora.md|no frontmatter block",
+    "frontmatter|docs/tuning/spoken-language.md|no frontmatter block",
     "frontmatter|docs/tuning/vision-qlora.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/diffusiongemma.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/gemma-4-12b.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/gemma-4.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/mtp.md|no frontmatter block",
-    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/audio/roses-are.wav",
-    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/thinking",
-    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision",
-    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n",
-    "links|docs/get-started/capabilities/audio.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
-    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/audio",
-    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/thinking",
-    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision",
-    "links|docs/get-started/capabilities/function-calling.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
-    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
-    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/images/cat.png",
-    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/images/kitchen.jpg",
-    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/images/surprise.png",
-    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
-    "links|docs/get-started/capabilities/image.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
-    "links|docs/get-started/capabilities/text.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
-    "links|docs/get-started/capabilities/text.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
-    "links|docs/get-started/capabilities/text.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
-    "links|docs/get-started/capabilities/thinking.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
-    "links|docs/get-started/capabilities/thinking.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
-    "links|docs/get-started/capabilities/thinking.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
-    "links|docs/get-started/capabilities/video.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
-    "links|docs/get-started/capabilities/video.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4",
-    "links|docs/get-started/capabilities/video.md|link to upstream docs: https://ai.google.dev/gemma/docs/run",
-    "links|docs/get-started/prompt-formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure",
-    "links|docs/get-started/quickstart.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/datagemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/diffusiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma/explained",
-    "links|docs/models/diffusiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf",
-    "links|docs/models/diffusiongemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma/explained",
-    "links|docs/models/diffusiongemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/diffusiongemma",
-    "links|docs/models/embeddinggemma/fine-tuning.md|link to upstream docs: http://ai.google.dev/gemma/docs/embeddinggemma/model_card#prompt_instructions",
-    "links|docs/models/embeddinggemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers",
-    "links|docs/models/embeddinggemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers",
-    "links|docs/models/embeddinggemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers",
-    "links|docs/models/embeddinggemma/inference.md|link to upstream docs: http://ai.google.dev/gemma/docs/embeddinggemma/model_card#prompt_instructions",
-    "links|docs/models/embeddinggemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers",
-    "links|docs/models/embeddinggemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
-    "links|docs/models/embeddinggemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma",
-    "links|docs/models/functiongemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma",
-    "links|docs/models/functiongemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/mobile-actions",
-    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure",
-    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
-    "links|docs/models/functiongemma/formatting.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma",
-    "links|docs/models/functiongemma/full-sequence.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
-    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
-    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices",
-    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/function-calling-with-hf",
-    "links|docs/models/functiongemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/mobile-actions",
-    "links|docs/models/functiongemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma",
-    "links|docs/models/functiongemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma",
-    "links|docs/models/functiongemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma",
-    "links|docs/models/gemma-1/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/gemma-2/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/gemma-3/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/huggingface_inference#audio",
-    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/huggingface_inference#vision",
-    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#conditional-parameter",
-    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#matformer",
-    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#ple-caching",
-    "links|docs/models/gemma-3n/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/get_started",
-    "links|docs/models/gemma-3n/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n",
-    "links|docs/models/gemma-3n/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n#parameters",
-    "links|docs/models/gemma-3n/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-3n},",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/audio",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/thinking",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/image",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/capabilities/vision/video",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core#previous-models",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_4",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/get_started",
-    "links|docs/models/gemma-4/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/mtp/overview",
-    "links|docs/models/gemma-4/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/paligemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
-    "links|docs/models/paligemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma",
-    "links|docs/models/paligemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/paligemma/inference-with-keras",
-    "links|docs/models/paligemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/get_started#models-list",
-    "links|docs/models/paligemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions##prompt_task_syntax",
-    "links|docs/models/paligemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#evaluation_approach",
-    "links|docs/models/paligemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
-    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#evaluation_approach",
-    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
-    "links|docs/models/paligemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
-    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure",
-    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/prompt-structure#system-instructions",
-    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma#categories",
-    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/inference-with-keras",
-    "links|docs/models/paligemma/prompting.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions#prompt_task_syntax",
-    "links|docs/models/recurrentgemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
-    "links|docs/models/recurrentgemma/fine-tuning.md|link to upstream docs: https://ai.google.dev/gemma/docs/setup",
-    "links|docs/models/recurrentgemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/recurrentgemma/model_card",
-    "links|docs/models/recurrentgemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
-    "links|docs/models/recurrentgemma/inference.md|link to upstream docs: https://ai.google.dev/gemma/docs/setup",
-    "links|docs/models/recurrentgemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#implementation_information",
-    "links|docs/models/recurrentgemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card#model_data",
-    "links|docs/models/recurrentgemma/model-card.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/3n",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/business-email-assistant",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/codegemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/datagemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/embeddinggemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/functiongemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma-aps",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/gemma_scope",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/mtp/overview",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/paligemma/model-card-2",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/personal-code-assistant",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/recurrentgemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma",
-    "links|docs/models/releases.md|link to upstream docs: https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning",
-    "links|docs/models/shieldgemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/shieldgemma/model_card",
-    "links|docs/models/shieldgemma/index.md|link to upstream docs: http://ai.google.dev/gemma/docs/shieldgemma/model_card_2",
-    "links|docs/models/shieldgemma/index.md|link to upstream docs: https://ai.google.dev/gemma/docs/core",
-    "links|docs/models/shieldgemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_2",
-    "links|docs/models/shieldgemma/model-card-v1.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma",
-    "links|docs/models/shieldgemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/core/model_card_3",
-    "links|docs/models/shieldgemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma",
-    "links|docs/models/shieldgemma/model-card-v2.md|link to upstream docs: https://ai.google.dev/gemma/docs/shieldgemma/model_card_2#harm-types",
+    "internal|docs/deployment/index.md|broken internal link: keras.md",
+    "orphans|docs/deployment/index.md|page exists but is not in nav",
+    "orphans|docs/deployment/mobile-actions.md|page exists but is not in nav",
+    "orphans|docs/get-started/capabilities/mtp.md|page exists but is not in nav",
     "orphans|docs/get-started/capabilities/vision.md|page exists but is not in nav",
     "orphans|docs/get-started/index.md|page exists but is not in nav",
+    "orphans|docs/get-started/prompt-structure.md|page exists but is not in nav",
+    "orphans|docs/get-started/setup.md|page exists but is not in nav",
+    "orphans|docs/learn/business-email-assistant.md|page exists but is not in nav",
+    "orphans|docs/learn/personal-code-assistant.md|page exists but is not in nav",
+    "orphans|docs/models/gemma-aps/index.md|page exists but is not in nav",
     "orphans|docs/models/index.md|page exists but is not in nav",
     "orphans|docs/tools/index.md|page exists but is not in nav",
+    "orphans|docs/tuning/spoken-language.md|page exists but is not in nav",
     "stubs|docs/community/discord.md|stub: 46 chars of content (minimum 400)",
     "stubs|docs/community/gemma-skills.md|stub: 62 chars of content (minimum 400)",
     "stubs|docs/community/gemmaverse.md|placeholder link: example.com",
     "stubs|docs/cookbook.md|stub: 395 chars of content (minimum 400)",
     "stubs|docs/demos/concurrent.md|stub: 52 chars of content (minimum 400)",
     "stubs|docs/deployment/google-cloud.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/deployment/huggingface.md|placeholder text: 'Content coming soon'",
     "stubs|docs/deployment/langchain.md|stub: 59 chars of content (minimum 400)",
     "stubs|docs/deployment/litert.md|stub: 52 chars of content (minimum 400)",
     "stubs|docs/deployment/llamacpp.md|placeholder text: 'Content coming soon'",
diff --git a/PLAN.md b/PLAN.md
index e62546e..0f4d461 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -92,7 +92,7 @@ Get started    Quickstart · Install & setup · Choose your model
                              Multi-Token Prediction · Long context
 Models         Gemma 4 (overview, model card, benchmarks)
                Variants: Gemma 3n · DiffusionGemma · FunctionGemma
-                         EmbeddingGemma · PaliGemma · ShieldGemma · CodeGemma
+                         EmbeddingGemma · PaliGemma · ShieldGemma
                Previous generations: Gemma 3 · 2 · 1
                Research: RecurrentGemma · DataGemma · Gemma Scope
                Releases
@@ -358,13 +358,108 @@ finding**, not just the mandatory-check list. Both workflows now pass clean.
 
     For annotated tags, dereference: `gh api repos/<repo>/git/tags/<sha> --jq '.object.sha'`.
 
-### Step 0.5 — Rewrite internal links
+### Step 0.5 — Rewrite internal links ✅
+
+- [x] `scripts/link_map.toml` — reviewable data, not logic buried in a script. Three sections:
+      `[ported]` (target exists), `[planned]` (eventual path; tracked as known-pending),
+      `[delink]` (drop the link, keep the text).
+- [x] `scripts/rewrite_links.py` — applies the map, computing each relative path per page.
+      An unmapped target is **reported and left alone**; silently dropping a link is worse than
+      leaving one to fix.
+- [x] **144 prose links rewritten**: 122 ported, 20 planned, 2 delinked. Zero unmapped.
+- [x] **CodeGemma delinked** (decision: not being ported). Both references in `releases.md`
+      keep their text — "Release of CodeGemma v1.1." still reads correctly.
+- [x] `--fix-anchors` pass: 12 fragments repaired. devsite used `#model_data`, Zensical emits
+      `#model-data`; 8 were the mechanical underscore→hyphen case, 4 needed the explicit
+      `[anchors]` override table because the heading text itself changed.
+- **Verify:** `uv run python scripts/lint_docs.py --check links` → zero. Both passes idempotent.
+- **Done when:** ✅ zero outbound links to the site we're replacing.
+
+**Left external on purpose** — 5 URLs inside code blocks: four are sample data the reader
+fetches (`img_url = "https://.../cat.png"`) and one is a BibTeX citation. Rewriting those would
+break copy-paste and misattribute the citation. `check_links` now skips code blocks for this reason.
+
+**5 anchors remain unresolved**, none of them actionable now:
+
+| Anchor | Why |
+|---|---|
+| `huggingface.md#audio`, `#vision` | target is a stub; resolves in Step 2.2 |
+| `quickstart.md#models-list` | section removed during hand-editing |
+| `paligemma/index.md#categories` | **broken upstream too** — no such section on ai.google.dev |
+| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
+
+The last two were dead links on the original site; the port reproduced them faithfully.
+Inventing a target would be an editorial guess, so they are left alone pending a content decision.
+
+```bash
+uv run python scripts/rewrite_links.py --report
+```
+
+### Phase 0 progress: 300 → 137
+
+| Check | Start | Now | |
+|---|---|---|---|
+| `links` | 122 | **0** | ✅ cleared |
+| `images` | 52 | **0** | ✅ cleared |
+| `fences` | 7 | **0** | ✅ cleared |
+| `size` | 1 | **0** | ✅ cleared |
+| `admonitions` | 0 | **0** | ✅ |
+| `internal` | 0 | 20 | known-pending: links to planned pages |
+| `stubs` | 33 | 32 | → Steps 2.2–2.4 |
+| `orphans` | 4 | 4 | → Step 2.1 |
+| `frontmatter` | 81 | 81 | → Step 3.1 |
+
+
 - [ ] Map all 122 `ai.google.dev/gemma/docs/*` links to local paths.
 - [ ] For pages not yet ported, point at the eventual local path and let the linter flag it as
       a known-pending target (don't leave live external links).
 - **Verify:** `uv run python scripts/lint_docs.py --check links`
 - **Done when:** zero outbound links to the site we're replacing.
 
+### Step 0.5b — Port the pages Step 0.5 pointed at ✅
+
+Step 0.5 left 19 build warnings by design (links aimed at eventual paths). Every one of those
+targets turned out to exist upstream, so porting them was cheaper than leaving the warnings —
+and it advanced Step 2.4 at the same time.
+
+- [x] **10 pages ported**, all fidelity-clean:
+
+| Page | Source | Size |
+|---|---|---|
+| `get-started/setup.md` | `/setup` | 2.5 KB |
+| `get-started/prompt-structure.md` | `/core/prompt-structure` | 2.2 KB |
+| `get-started/capabilities/mtp.md` | `/mtp/overview` | 2.8 KB |
+| `deployment/index.md` | `/run` | 10.7 KB |
+| `deployment/huggingface.md` | `/core/huggingface_inference` | 11.9 KB (**replaced a stub**) |
+| `deployment/mobile-actions.md` | `/mobile-actions` | 4.2 KB |
+| `tuning/spoken-language.md` | `/spoken-language/task-specific-tuning` | 22.9 KB |
+| `learn/business-email-assistant.md` | `/business-email-assistant` | 20.5 KB |
+| `learn/personal-code-assistant.md` | `/personal-code-assistant` | 22.6 KB |
+| `models/gemma-aps/index.md` | `/gemma-aps` | 1.8 KB |
+
+- [x] **The MTP gap is closed.** It was the flagship Gemma 4 feature with zero documentation.
+- [x] Link map grew to 75 `[ported]` entries; 23 more anchor overrides applied.
+- [x] Fixed a mis-targeted mapping: `/core/prompt-system-instructions` (no such upstream page)
+      was sent to `paligemma/prompting.md`, but the referring context is core Gemma
+      `start_of_turn`/`end_of_turn` tags, so it now points at `get-started/prompt-structure.md`.
+- **Verify:** build warnings **19 → 5**; all four pipeline stages idempotent.
+
+**5 warnings remain, all deliberate:**
+
+| Warning | Status |
+|---|---|
+| `deployment/keras.md` ×2 | planned page — Step 2.1 decides whether "Run > Local" gains Keras |
+| `quickstart.md#models-list` | section removed during your hand-editing |
+| `paligemma/index.md#categories` | **broken upstream too** |
+| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
+
+The last three are left alone on purpose: picking a target is an editorial decision, and the
+dangling anchor is a useful signal that a section is genuinely missing. **Needs a content call.**
+
+!!! warning "The 9 new pages are not in the nav"
+    `orphans` rose 4 → 13. That is expected — Step 2.1 places them. Do not "fix" it by
+    deleting pages.
+
 ### Step 0.6 — Model capability matrix as single source of truth
 - [ ] Create `data/models.yml`: every Gemma 4 size with parameters, modalities in/out, context
       window, memory at each quantization, MTP support, target platform.
@@ -467,7 +562,8 @@ actual model support) → context/KV-cache flags → troubleshooting → measure
 - **Verify:** `uv run python scripts/lint_docs.py --check stubs` → zero.
 
 ### Step 2.4 — Port remaining upstream pages
-- [ ] CodeGemma (5 pages, currently absent from nav entirely).
+- [x] ~~CodeGemma~~ — **not being ported** (decision: 2026-08-03). Existing references are
+      delinked, keeping the text. Do not add it back without a new decision.
 - [ ] `setup`, `run`, `gemma_chat`, `gemma_library`, `keras_inference`, `pytorch_gemma`,
       `huggingface_inference`, `lora_tuning`, `prompt-structure`.
 - [ ] Application tutorials → Learn: `business-email-assistant`, `personal-code-assistant`,
diff --git a/docs/assets/deployment/gallery-mobile-actions-finetune-challenge.png b/docs/assets/deployment/gallery-mobile-actions-finetune-challenge.png
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diff --git a/docs/deployment/huggingface.md b/docs/deployment/huggingface.md
index a941367..ee22163 100644
--- a/docs/deployment/huggingface.md
+++ b/docs/deployment/huggingface.md
@@ -1,5 +1,201 @@
-# Hugging Face Transformers
+# Run Gemma with Hugging Face Transformers
 
-Run Gemma with Hugging Face Transformers for inference and integration.
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+Generating text, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models. This tutorial shows you how to get started running Gemma using Hugging Face Transformers using both text and image input to generate text content. The Transformers Python library provides a API for accessing pre-trained generative AI models, including Gemma. For more information, see the [Transformers](https://huggingface.co/docs/transformers/en/index) documentation.
+
+!!! Note
+    Gemma 3 and later models support input of both images and text. Earlier versions of Gemma only support text input, except for some variants, including [PaliGemma](../get-started/setup.md).
+
+
+### Install Python packages
+
+Install the Hugging Face libraries required for running the Gemma model and making requests.
+
+```python
+# Install Pytorch
+%pip install torch
+
+# Install a transformers
+%pip install "transformers>=5.10.1"
+```
+
+## Generate text from text
+
+Prompting a Gemma model with text to get a text response is the simplest way to use Gemma and works with nearly all Gemma variants. This section shows how to use the Hugging Face Transformers library load and configure a Gemma model for text to text generation.
+
+### Load model
+
+Use the `torch` and `transformers` libraries to create an instance of a model execution `pipeline` class with Gemma. When using a model for generating output or following directions, select an instruction tuned (IT) model, which typically has `it` in the model ID string. Using the `pipeline` object, you specify the Gemma variant you want to use, the type of task you want to perform, specifically `"any-to-any"` for multimodal generation, as shown in the following code example:
+
+```python
+from transformers import pipeline
+
+MODEL_ID = "google/gemma-4-E2B-it"
+
+pipe = pipeline(
+    task="any-to-any",
+    model=MODEL_ID,
+    device_map="auto",
+    dtype="auto"
+)
+```
+
+``` { .no-copy .output }
+config.json:   0%|          | 0.00/4.95k [00:00<?, ?B/s]
+model.safetensors:   0%|          | 0.00/10.2G [00:00<?, ?B/s]
+Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
+generation_config.json:   0%|          | 0.00/208 [00:00<?, ?B/s]
+processor_config.json:   0%|          | 0.00/1.69k [00:00<?, ?B/s]
+chat_template.jinja:   0%|          | 0.00/17.3k [00:00<?, ?B/s]
+tokenizer_config.json:   0%|          | 0.00/2.10k [00:00<?, ?B/s]
+tokenizer.json:   0%|          | 0.00/32.2M [00:00<?, ?B/s]
+```
+
+Gemma supports only a few `task` settings for generation. For more information on the available `task` settings, see the Hugging Face Pipelines [task()](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.pipeline.task) documentation. For more information about using the Pipeline class, see the Hugging Face [Pipelines](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines) documentation.
+
+### Run text generation
+
+Once you have the Gemma model loaded and configured in a `pipeline` object, you can send prompts to the model. The following example code shows a basic request using the [`text`](https://www.tensorflow.org/text/api_docs/python/text) parameter:
+
+```python
+pipe(text="<|turn>user\nroses are red<turn|>\n<|turn>model\n")
+```
+
+``` { .no-copy .output }
+[transformers] The input data was not formatted as a chat with dicts containing 'role' and 'content' keys, even though this model supports chat. Consider using the chat format for better results. For more information, see https://huggingface.co/docs/transformers/en/chat_templating
+[transformers] Keyword argument `video` is not a valid argument for this processor and will be ignored.
+[transformers] Both `max_new_tokens` (=256) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)
+[{'input_text': '<|turn>user\nroses are red<turn|>\n<|turn>model\n',
+  'generated_text': "<|turn>user\nroses are red<turn|>\n<|turn>model\nThat's a classic rhyme! It immediately brings up the image of **roses and the color red**.\n\nIs there anything else you'd like to talk about, or perhaps another rhyme you'd like to try? 😊<turn|>"}]
+```
+
+### Use a prompt template
+
+When generating content with more complex prompting, use a prompt template to structure your request. A prompt template allows you to specify input from specific roles, such as `user` or `model`, and is a required format for managing multi-turn chat interactions with Gemma models. The following example code shows how to constuct a prompt template for Gemma:
+
+```python
+from transformers import GenerationConfig
+config = GenerationConfig.from_pretrained(MODEL_ID)
+config.max_new_tokens = 512
+gen_kwargs = dict(generation_config=config)
+
+messages = [
+    {
+        "role": "system",
+        "content": [{"type": "text", "text": "You are a helpful assistant."}]
+    },
+    {
+        "role": "user",
+        "content": [{"type": "text", "text": "Roses are red..."}]
+    },
+]
+
+pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)
+```
+
+``` { .no-copy .output }
+[{'input_text': [{'role': 'system',
+    'content': [{'type': 'text', 'text': 'You are a helpful assistant.'}]},
+   {'role': 'user',
+    'content': [{'type': 'text', 'text': 'Roses are red...'}]}],
+  'generated_text': 'Roses are red,\nViolets are blue,\nHow do you do?<turn|>'}]
+```
+
+## Generate text from image data
+
+Starting with Gemma 3, for model sizes 4B and higher, you can use image data as part of your prompt. This section shows how to use the Transformers library to load and configure a Gemma model to use image data and text input to generate text output.
+
+### Use a prompt template
+
+When generating content with more complex prompting, use a prompt template to structure your request. A prompt template allows you to specify input from specific roles, such as `user` or `model`, and is a required format for managing multi-turn chat interactions with Gemma models. The following example code shows how to constuct a prompt template for Gemma:
+
+```python
+from transformers import GenerationConfig
+config = GenerationConfig.from_pretrained(MODEL_ID)
+config.max_new_tokens = 512
+gen_kwargs = dict(generation_config=config)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://ai.google.dev/static/gemma/docs/images/thali-indian-plate.jpg"},
+            {"type": "text", "text": "What is shown in this image?"},
+        ]
+    },
+    {
+        "role": "assistant",
+        "content": [
+            {"type": "text", "text": "This image shows"},
+        ],
+    },
+]
+
+pipe(text=messages, return_full_text=False, generate_kwargs=gen_kwargs)
+```
+
+``` { .no-copy .output }
+[{'input_text': [{'role': 'user',
+    'content': [{'type': 'image',
+      'url': 'https://ai.google.dev/static/gemma/docs/images/thali-indian-plate.jpg'},
+     {'type': 'text', 'text': 'What is shown in this image?'}]},
+   {'role': 'assistant',
+    'content': [{'type': 'text', 'text': 'This image shows'}]}],
+  'generated_text': " a platter of Indian food, likely a meal or a spread featuring various side dishes and bread.\n\nHere's a breakdown of what appears to be present:\n\n* **Flatbread:** There is a large, folded flatbread in the center, which looks like a type of roti, chapati, or perhaps a paratha.\n* **Dips/Sauces/Condiments (in small bowls):**\n    * A bowl containing a yellow substance (possibly a sauce, chutney, or butter/ghee).\n    * A bowl containing a creamy white sauce (like yogurt dip or raita).\n    * A bowl containing a reddish-brown, somewhat thick sauce or curry.\n    * A bowl containing a bright yellow/orange sauce.\n    * A bowl containing a green sauce or chutney.\n* **Rice:** A portion of white, fluffy rice is visible on the left side.\n* **Bread/Crackers:** In the upper right corner, there is a piece of what looks like a dry, textured bread or cracker.\n* **Other Items:** There are some reddish, textured pieces mixed in with the sauces on the right side, which might be fried items or components of a curry.\n\nOverall, it looks like a traditional Indian meal setup with bread, rice, and various accompanying sauces or curries.<turn|>"}]
+```
+
+You can include multiple images in your prompt by including additional `"type": "image",` entries in the `content` list.
+!!! Note
+    Do not use `<|image|>`, `<start_of_image>` or `<image_soft_token>` tokens in text portion of a prompt template as this approach creates redundant tokens and processing errors.
+    
+
+## Generate text from audio data
+
+With [Gemma 4](../models/gemma-4/index.md) and [Gemma 3n](../models/gemma-3n/index.md), you can use audio data as part of your prompt. This section shows how to use the Transformers library to load and configure a Gemma model to use audio data and text input to generate text output.
+
+### Use a prompt template
+
+When generating content with audio, use a prompt template to structure your request. A prompt template allows you to specify input from specific roles, such as `user` or `model`, and is a required format for managing multi-turn chat interactions with Gemma models. The following example code shows how to constuct a prompt template for Gemma with audio data input:
+
+```python
+from transformers import GenerationConfig
+config = GenerationConfig.from_pretrained(MODEL_ID)
+config.max_new_tokens = 512
+gen_kwargs = dict(generation_config=config)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three."},
+            {"type": "audio", "audio": "https://ai.google.dev/gemma/docs/audio/roses-are.wav"},
+        ]
+    }
+]
+
+pipe(text=messages, return_full_text=False, generate_kwargs=gen_kwargs)
+```
+
+``` { .no-copy .output }
+[{'input_text': [{'role': 'user',
+    'content': [{'type': 'text',
+      'text': 'Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.'},
+     {'type': 'audio',
+      'audio': 'https://ai.google.dev/gemma/docs/audio/roses-are.wav'}]}],
+  'generated_text': 'Roses are red, violets are blue.<turn|>'}]
+```
+
+You can include multiple audio files in your prompt by including additional `"type": "audio",` entries in the `content` list.
+!!! Note
+    Do not use `<|audio|>` or `<audio_soft_token>` tokens in text portion of a prompt template as this approach creates redundant tokens and processing errors.
+    
+
+## Next steps
+
+Build and explore more with Gemma models:
+
+- [Fine-tune Gemma for text tasks using Hugging Face Transformers](../tuning/qlora.md)
+- [Fine-tune Gemma for vision tasks using Hugging Face Transformers](../tuning/vision-qlora.md)
+- [Perform distributed fine-tuning and inference on Gemma models](../tuning/distributed.md)
+- [Use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma)
+- [Fine-tune Gemma using Keras and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb)
diff --git a/docs/deployment/index.md b/docs/deployment/index.md
new file mode 100644
index 0000000..88c3c1f
--- /dev/null
+++ b/docs/deployment/index.md
@@ -0,0 +1,124 @@
+There are two key decisions to make when you want to run a Gemma model:
+1) what Gemma variant you want to run, and 2) what AI execution framework you
+are going to use to run it? A key issue in making both these decisions has to do
+with what hardware you and your users have available to run the model.
+
+This overview helps you navigate these decisions and start working with Gemma
+models. The general steps for running a Gemma model are as follows:
+
+- [Choose a framework for running](index.md#choose-a-framework)
+- [Select a Gemma variant](index.md#select-a-gemma-variant)
+- [Run generation and inference requests](index.md#run-generation-and-inference-requests)
+
+## Choose a framework
+
+Gemma models are compatible with a wide variety of ecosystem tools. Choosing the
+right one depends on your available hardware (Cloud GPUs versus Local Laptop)
+and your interface preference (Python code versus Desktop Application).
+
+Use the following table to quickly identify the best tool for your needs:
+
+| If you want to... | Recommended Framework | Best For |
+|---|---|---|
+| **Run locally with a Chat UI** | - **[LM Studio](lmstudio.md)** - **[Ollama](ollama.md)** | Beginners, or users who want a "Gemini-like" experience on their laptop. |
+| **Run efficiently on Edge** | - **[LiteRT-LM](https://ai.google.dev/edge/litert-lm/models/gemma-4)** - **[llama.cpp](llamacpp.md)** - **[MLX](mlx.md)** | High-performance local inference with minimal resources. |
+| **Build/Train in Python** | - **[Tunix (Tune-in-JAX)](https://tunix.readthedocs.io/en/latest/)** - **[Hugging Face Transformers](https://huggingface.co/docs/transformers/en/model_doc/gemma4)** - **[Keras](keras.md)** - **[Unsloth](https://unsloth.ai/docs/models/gemma-4)** | Researchers and Developers building custom applications or fine-tuning models. |
+| **Deploy to Production / Enterprise** | - **[Google Cloud Kubernetes Engine (GKE)](google-cloud.md)** - **[Gemini Enterprise Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/open-models/use-gemma)** - **[vLLM](https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html)** - **[SGLang](https://lmsysorg.mintlify.app/cookbook/autoregressive/Google/Gemma4)** | Scalable, managed cloud deployment with enterprise security and MLOps support. |
+
+### Framework Details
+
+The following are guides for running Gemma models categorized by your deployment
+environment.
+
+#### 1. Desktop \& Local Inference (High Efficiency)
+
+These tools allow you to run Gemma on consumer hardware (laptops, desktops) by
+utilizing optimized formats (like GGUF) or specific hardware accelerators.
+
+- **[LM Studio](lmstudio.md)**: A desktop application that lets you download and chat with Gemma models in a user-friendly interface. No coding required.
+- **[llama.cpp](llamacpp.md)**: A popular open-source C++ port of Llama (and Gemma) that runs incredibly fast on CPUs and Apple Silicon.
+- **[LiteRT-LM](https://ai.google.dev/edge/litert-lm/models/gemma-4)** : Offers a command-line interface ([CLI](https://ai.google.dev/edge/litert-lm/cli)) to run optimized `.litertlm` Gemma models on desktop (Windows, Linux, macOS), powered by LiteRT (formerly TFLite).
+- **[MLX](mlx.md)**: A framework designed specifically for machine learning on Apple Silicon, perfect for Mac users who want built-in performance.
+- **[Ollama](ollama.md)**: A tool to run open LLMs locally, often used to power other applications.
+
+#### 2. Python Development (Research \& Fine-tuning)
+
+Standard frameworks for AI developers building applications, pipelines, or
+training models.
+
+- **[Hugging Face Transformers](https://huggingface.co/docs/transformers/en/model_doc/gemma4)**: The industry standard for quick access to models and pipelines.
+- **[Unsloth](https://unsloth.ai/docs/models/gemma-4)**: An optimized library for fine-tuning LLMs. It lets you train Gemma models 2-5x faster with significantly less memory, making it possible to fine-tune on consumer GPUs (e.g., free Google Colab tiers).
+- **[Keras](keras.md)** / **[Tunix (Tune-in-JAX)](https://tunix.readthedocs.io/en/latest/)**: Core libraries for deep learning research and custom architecture implementation.
+
+#### 3. Mobile \& Edge Deployment (On-Device)
+
+Frameworks designed to run LLMs directly on user devices (Android, iOS, Web)
+without internet connectivity, often utilizing NPUs (Neural Processing Units).
+
+- **[LiteRT-LM](https://ai.google.dev/edge/litert-lm/models/gemma-4)**: The fully open-source framework for on-device LLM development that offers maximum performance and fine-grained control, with direct support for CPU, GPU, and NPU acceleration on Android and iOS.
+
+#### 4. Cloud \& Production Deployment
+
+Managed services for scaling your application to thousands of users or accessing
+massive compute power.
+
+- **[Gemini Enterprise Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/open-models/use-gemma)**: Google Cloud's fully managed AI platform. Best for enterprise applications requiring SLAs and scaling.
+- **[Google Cloud Kubernetes Engine (GKE)](google-cloud.md)**: For orchestrating your own serving clusters.
+- **[vLLM](https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html)**: A high-throughput and memory-efficient inference and serving engine, often used in cloud deployments.
+- **[SGLang](https://lmsysorg.mintlify.app/cookbook/autoregressive/Google/Gemma4)**: High-performance serving framework for large language and multimodal models.
+
+Make sure your intended deployment Gemma model format, such as Keras built-in
+format, Safetensors, or GGUF, is supported by your chosen framework.
+
+## Select a Gemma variant
+
+Gemma models are available in several variants and sizes, including the
+foundation or [core](../models/gemma-4/index.md) Gemma models, and more
+specialized model variants such as
+[PaliGemma](../models/paligemma/index.md) and
+[DataGemma](../models/datagemma/index.md), and many variants
+created by the AI developer community on sites such as
+[Kaggle](https://www.kaggle.com/models?query=gemma) and
+[Hugging Face](https://huggingface.co/models?search=gemma). If you are unsure
+about what variant you should start with, select the latest Gemma
+[core](../models/gemma-4/index.md) instruction-tuned (IT) model with
+the lowest number of parameters. This type of Gemma model has low compute
+requirements and be able to respond to a wide variety of prompts without
+requiring additional development.
+
+Consider the following factors when choosing a Gemma variant:
+
+- **Gemma core, and other variant families such as PaliGemma, CodeGemma** : *Recommend Gemma (core).* Gemma variants beyond the core version have the same architecture as the core model, and are trained to perform better at specific tasks. Unless your application or goals align with the specialization of a specific Gemma variant, it is best to start with a Gemma core, or base, model.
+- **Instruction-tuned (IT), pre-trained (PT), fine-tuned (FT), mixed
+  (mix)** : *Recommend IT.*
+    - *Instruction-tuned* (IT) Gemma variants are models that have been trained to respond to a variety of instructions or requests in human language. These model variants are the best place to start because they can respond to prompts without further model training.
+    - *Pre-trained* (PT) Gemma variants are models that have been trained to make inferences about language or other data, but have not been trained to follow human instructions. These models require additional training or tuning to be able to perform tasks effectively, and are meant for researchers or developers who want to study or develop the capabilities of the model and its architecture.
+    - *Fine-tuned* (FT) Gemma variants can be considered IT variants, but are typically trained to perform a specific task, or perform well on a specific generative AI benchmark. The PaliGemma variant family includes a number of FT variants.
+    - *Mixed* (mix) Gemma variants are versions of PaliGemma models that have been instruction tuned with a variety of instructions and are suitable for general use.
+- **Parameters** : *Recommend smallest number available*. In general, the more parameters a model has, the more capable it is. However, running larger models requires larger and more complex compute resources, and generally slows down development of an AI application. Unless you have already determined that a smaller Gemma model cannot meet your needs, choose a one with a small number of parameters.
+- **Quantization levels:** *Recommend half precision (16-bit), except for
+  tuning*. Quantization is a complex topic that boils down to what size and precision of data, and consequently how much memory a generative AI model uses for calculations and generating responses. After a model is trained with high-precision data, which is typically 32-bit floating point data, models like Gemma can be modified to use lower precision data such as 16, 8 or 4-bit sizes. These quantized Gemma models can still perform well, depending on the complexity of the tasks, while using significantly less compute and memory resources. However, tools for tuning quantized models are limited and may not be available within your chosen AI development framework. Typically, you must fine-tune a model like Gemma at full precision, then quantize the resulting model.
+
+For a list of key, Google-published Gemma models, see the
+[Getting started with Gemma models](../get-started/quickstart.md), Gemma model list.
+
+## Run generation and inference requests
+
+After you have selected an AI execution framework and a Gemma variant, you can
+start running the model, and prompting it to generate content or complete tasks.
+For more information on how to run Gemma with a specific framework, see the
+guides linked in the [Choose a framework](index.md#choose-a-framework) section.
+
+### Prompt formatting
+
+All instruction-tuned Gemma variants have specific prompt formatting
+requirements. Some of these formatting requirements are handled automatically by
+the framework you use to run Gemma models, but when you are sending prompt data
+directly to a tokenizer, you must add specific tags, and the tagging
+requirements can change depending on the Gemma variant you are using. See the
+following guides for information on Gemma variant prompt formatting and system
+instructions:
+
+- [Gemma prompt and system instructions](../get-started/prompt-formatting.md)
+- [PaliGemma prompt and system instructions](../models/paligemma/prompting.md)
+- [FunctionGemma formatting and best practices](../models/functiongemma/formatting.md)
diff --git a/docs/deployment/mobile-actions.md b/docs/deployment/mobile-actions.md
new file mode 100644
index 0000000..ddf63f0
--- /dev/null
+++ b/docs/deployment/mobile-actions.md
@@ -0,0 +1,101 @@
+# Fine-tune FunctionGemma 270M for Mobile Actions
+
+
+## Overview
+
+Mobile Actions is a demo app where users can trigger actions on their device
+from voice or text input. It reimagines assistant interaction as a fully offline
+capability. Whether it's "Create a calendar event for lunch tomorrow", "Add John
+to my contacts", or "Turn on the flashlight" the model parses the natural
+language and identifies the correct OS tool to execute the command.
+
+This guide shows you how to:
+
+1. **Fine-tuning the FunctionGemma 270M model using the Mobile Actions dataset**
+2. **Deploying the customized model to the Google AI Edge Gallery**
+
+You will be able to learn end-to-end from fine-tuning a model to deploying it on
+device.
+
+## Step 1: Fine-tuning FunctionGemma with the Mobile Actions Dataset
+
+[FunctionGemma](https://huggingface.co/google/functiongemma-270m-it) is a 270
+million parameter model based on the Gemma 3 architecture. It has been trained
+specifically for function calling, enabling it to translate natural language
+requests into function calls.
+
+This model is small and efficient enough to run on a mobile phone, but as is
+common for models of this size, it requires fine-tuning to specialize it for the
+task it is going to perform.
+
+To fine-tune FunctionGemma, we use the [Mobile Actions
+dataset](https://huggingface.co/datasets/google/mobile-actions), which is
+publicly available on Hugging Face. Each entry in this dataset provides:
+
+- The set of tools (functions) the model can use:
+    1. Turns the flashlight on
+    2. Turns the flashlight off
+    3. Creates a contact in the phone's contact list
+    4. Sends an email
+    5. Shows a location on the map
+    6. Opens the WiFi settings
+    7. Creates a new calendar event
+- The system prompt providing the context like current date and time
+- The user prompt, like `turn on the flashlight`.
+- The expected model response, including the appropriate function calls.
+
+Here is how the show map function looks:
+
+```text
+{
+  "function": {
+    "name": "show_map",
+    "description": "Shows a location on the map.",
+    "parameters": {
+      "type": "OBJECT",
+      "properties": {
+        "query": {
+          "type": "STRING",
+          "description": "The location to search for. May be the name of a place, a business, or an address."
+        }
+      },
+      "required": [
+        "query"
+      ]
+    }
+  }
+}
+```
+
+The [colab notebook](https://github.com/google-gemini/gemma-cookbook/blob/main/FunctionGemma/%5BFunctionGemma%5DFinetune_FunctionGemma_270M_for_Mobile_Actions_with_Hugging_Face.ipynb)
+covers all necessary steps, including:
+
+- Setting up the environment
+- Loading and preprocessing the Mobile Actions dataset
+- Fine-tuning the model using Hugging Face TRL
+- Converting the model to `.litertlm` format for deployment
+
+## Step 2: Deploying on Google AI Edge Gallery
+
+**Prerequisite** : You need the same Google Account you used to save the
+`.litertlm` file in step 1 and to be signed in with it on your Android phone.
+
+After fine-tuning, we convert and quantize the model weights to `.litertlm`
+format.
+
+You can deploy the model to the Google AI Edge Gallery - Mobile Actions option
+by choosing `Load Model` and selecting it from your Google Drive (or alternative
+method of distribution). The [Google AI Edge Gallery](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery) is
+available on Google Play Store.
+![Mobile Actions Finetune Challenge in Google AI Edge Gallery](../assets/deployment/gallery-mobile-actions-finetune-challenge.png) ![Mobile Actions task in Google AI Edge Gallery](../assets/deployment/gallery-mobile-actions.png)
+
+Now, you can try giving a voice command or typing in the app to see how well
+your new fine-tuned model does calling the functions available to it.
+
+## Next Steps
+
+Congratulations! You now know how to fine-tune a model with Hugging Face and
+deploy it on-device with LiteRT-LM.
+
+- [LiteRT-LM Overview](https://github.com/google-ai-edge/LiteRT-LM)
+- [LiteRT-LM Kotlin API for Android and JVM](https://github.com/google-ai-edge/LiteRT-LM/tree/main/kotlin)
diff --git a/docs/get-started/capabilities/audio.md b/docs/get-started/capabilities/audio.md
index fa783c0..e9531df 100644
--- a/docs/get-started/capabilities/audio.md
+++ b/docs/get-started/capabilities/audio.md
@@ -1,10 +1,10 @@
 
 # Audio Understanding
 
-Starting with [Gemma 3n](https://ai.google.dev/gemma/docs/gemma-3n), you can use audio directly into your prompts and workflows. Audio and spoken language are rich sources of data for capturing user intents, recording information about the world around us, and understanding
+Starting with [Gemma 3n](../../models/gemma-3n/index.md), you can use audio directly into your prompts and workflows. Audio and spoken language are rich sources of data for capturing user intents, recording information about the world around us, and understanding
 specific problems to be solved.
 
-This guide provides an overview of the audio processing capabilities of [Gemma 4](https://ai.google.dev/gemma/docs/core), including automatic speech recognition (ASR), translation, and general speech understanding.
+This guide provides an overview of the audio processing capabilities of [Gemma 4](../../models/gemma-4/index.md), including automatic speech recognition (ASR), translation, and general speech understanding.
 
 
 ## Install Python packages
@@ -316,6 +316,6 @@ In this guide, you learned how to process audio using Gemma 4 models. The exampl
 <!-- TODO: Port over
 ### Next steps
 
-- [Run Gemma overview](https://ai.google.dev/gemma/docs/run)
-- [Vision understanding](https://ai.google.dev/gemma/docs/capabilities/vision)
-- [Thinking mode](https://ai.google.dev/gemma/docs/capabilities/thinking) -->
+- [Run Gemma overview](../../deployment/index.md)
+- [Vision understanding](vision.md)
+- [Thinking mode](thinking.md) -->
diff --git a/docs/get-started/capabilities/function-calling.md b/docs/get-started/capabilities/function-calling.md
index cba560c..0594151 100644
--- a/docs/get-started/capabilities/function-calling.md
+++ b/docs/get-started/capabilities/function-calling.md
@@ -626,7 +626,7 @@ You have established how to build an application that can call functions with Ge
 ### Next Steps
 Check out the following documentation for further reading.
 
-- [Run Gemma overview](https://ai.google.dev/gemma/docs/run)
-- [Vision understanding](https://ai.google.dev/gemma/docs/capabilities/vision)
-- [Audio understanding](https://ai.google.dev/gemma/docs/capabilities/audio)
-- [Thinking mode](https://ai.google.dev/gemma/docs/capabilities/thinking) -->
+- [Run Gemma overview](../../deployment/index.md)
+- [Vision understanding](vision.md)
+- [Audio understanding](audio.md)
+- [Thinking mode](thinking.md) -->
diff --git a/docs/get-started/capabilities/image.md b/docs/get-started/capabilities/image.md
index 5635f30..8dcdcce 100644
--- a/docs/get-started/capabilities/image.md
+++ b/docs/get-started/capabilities/image.md
@@ -566,6 +566,6 @@ In this guide, you learned how to use Gemma 4 models for image understanding tas
 <!-- TODO: Port over 
 ### Next Steps
 
-- [Prompt and system instructions](https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4)
-- [Function calling](https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4)
-- [Run Gemma overview](https://ai.google.dev/gemma/docs/run) -->
+- [Prompt and system instructions](../prompt-formatting.md)
+- [Function calling](function-calling.md)
+- [Run Gemma overview](../../deployment/index.md) -->
diff --git a/docs/get-started/capabilities/mtp.md b/docs/get-started/capabilities/mtp.md
new file mode 100644
index 0000000..63faa47
--- /dev/null
+++ b/docs/get-started/capabilities/mtp.md
@@ -0,0 +1,43 @@
+# Speed-up Gemma 4 with Multi-Token Prediction
+
+In Gemma 4, Multi-Token Prediction (MTP) is the specific architecture
+used to enable highly efficient Speculative Decoding. Speculative
+decoding is a technique to speed up inference in large language models.
+Instead of relying solely on the large target model to generate tokens
+autoregressively (generating one token at a time, where each new token
+depends on the previous ones), a smaller, faster 'draft model' predicts
+several tokens ahead. The target model then verifies these drafted
+tokens in parallel. If the target model rejects a drafted token, it
+still produces the correct token for that position (ensuring that step
+is not wasted), and the draft model resumes predicting from that new
+correct token.
+
+Gemma 4 implements MTP by extending the base model with
+this smaller, faster draft model. This draft model is not independent as
+it shares the input embedding table with the target model and builds
+directly upon its last-layer activations. This results in significant
+decoding speedups while guaranteeing the exact
+same quality as standard autoregressive generation, making these
+checkpoints perfect for low-latency and on-device applications.
+
+Speculative decoding works by drafting several tokens and verifying
+them in a single forward pass. For dense models, the same weights are
+used for every token, so verifying multiple drafted tokens adds minimal
+overhead. Mixture of Experts (MoE) models like Gemma 4 26B A4B work
+differently. Each token may activate different experts, so verifying
+drafted tokens can require loading additional expert weights from
+memory, offsetting the gains from drafting. At higher batch sizes,
+there is typically more overlap in activated experts across sequences,
+improving reuse of loaded weights. At batch size 1 this overlap is
+limited, which is why the 26B A4B drafter may not yield speedups on
+hardware platforms without good parallelism.
+
+## MTP Enhancements
+
+Gemma 4 introduces several enhancements to the standard speculative
+decoding pipeline to improve the quality of drafted tokens and
+efficiency:
+
+- **Shared Input Embeddings**: The draft model shares the input embedding table with the target model.
+- **Target Activations**: The draft model uses the activations from the last layer of the target model, concatenates them with the token embeddings, and down-projects them to the drafter model's dimension.
+- **Efficient Embedder**: To avoid the expensive operation of predicting across the entire vocabulary, the model groups similar tokens into clusters. It first identifies the most likely clusters and then restricts its final calculations to only the tokens within those selected clusters (E2B and E4B only).
diff --git a/docs/get-started/capabilities/text.md b/docs/get-started/capabilities/text.md
index fe13ceb..1c2bfe5 100644
--- a/docs/get-started/capabilities/text.md
+++ b/docs/get-started/capabilities/text.md
@@ -307,6 +307,6 @@ In this guide, you learned how to perform basic text inference with Gemma 4 usin
 <!-- TODO: Port over
 ### Next Steps
 
-- [Prompt and system instructions](https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4)
-- [Function calling](https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4)
-- [Run Gemma overview](https://ai.google.dev/gemma/docs/run) -->
\ No newline at end of file
+- [Prompt and system instructions](../prompt-formatting.md)
+- [Function calling](function-calling.md)
+- [Run Gemma overview](../../deployment/index.md) -->
\ No newline at end of file
diff --git a/docs/get-started/capabilities/thinking.md b/docs/get-started/capabilities/thinking.md
index d12da8b..77f2c0a 100644
--- a/docs/get-started/capabilities/thinking.md
+++ b/docs/get-started/capabilities/thinking.md
@@ -501,6 +501,6 @@ In this guide, you learned how to use the thinking capabilities of Gemma 4 model
 
 Explore more capabilities of Gemma 4:
 
-- [Prompt and system instructions](https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4)
-- [Function calling](https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4)
-- [Run Gemma overview](https://ai.google.dev/gemma/docs/run) -->
+- [Prompt and system instructions](../prompt-formatting.md)
+- [Function calling](function-calling.md)
+- [Run Gemma overview](../../deployment/index.md) -->
diff --git a/docs/get-started/capabilities/video.md b/docs/get-started/capabilities/video.md
index ac91893..84b431f 100644
--- a/docs/get-started/capabilities/video.md
+++ b/docs/get-started/capabilities/video.md
@@ -95,6 +95,6 @@ In this guide, you learned how to process video data using Gemma 4 models. The e
 <!-- TODO: Port over
 ### Next steps
 
-- [Prompt and system instructions](https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4)
-- [Function calling](https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4)
-- [Run Gemma overview](https://ai.google.dev/gemma/docs/run) -->
+- [Prompt and system instructions](../prompt-formatting.md)
+- [Function calling](function-calling.md)
+- [Run Gemma overview](../../deployment/index.md) -->
diff --git a/docs/get-started/prompt-formatting.md b/docs/get-started/prompt-formatting.md
index edd19e3..a178104 100644
--- a/docs/get-started/prompt-formatting.md
+++ b/docs/get-started/prompt-formatting.md
@@ -6,7 +6,7 @@ hide:
 # Prompt Formatting
 
 Starting with Gemma 4, we introduce new control tokens. For Gemma 3 and lower,
-see the [previous document](https://ai.google.dev/gemma/docs/core/prompt-structure).
+see the [previous document](prompt-structure.md).
 
 The following sections specify the control tokens used by Gemma 4 and their use
 cases. Note that the control tokens are reserved in and specific to our
diff --git a/docs/get-started/prompt-structure.md b/docs/get-started/prompt-structure.md
new file mode 100644
index 0000000..d13057b
--- /dev/null
+++ b/docs/get-started/prompt-structure.md
@@ -0,0 +1,63 @@
+# Gemma formatting and system instructions
+
+Gemma instruction-tuned (IT) models are trained with a specific *formatter* that
+annotates all instruction tuning examples with extra information, both at
+training and inference time. The formatter has two purposes:
+
+1. Indicating roles in a conversation, such as the *system* , *user* , or *assistant* roles.
+2. Delineating turns in a conversation, especially in a multi-turn conversation.
+
+Below, we specify the control tokens used by Gemma and their use cases. Note
+that the control tokens are reserved in and specific to our tokenizer.
+
+- Token to indicate a user turn: `user`
+- Token to indicate a model turn: `model`
+- Token to indicate the beginning of dialogue turn: `<start_of_turn>`
+- Token to indicate the end of dialogue turn: `<end_of_turn>`
+
+Here's an example dialogue:
+
+```text
+<start_of_turn>user
+knock knock<end_of_turn>
+<start_of_turn>model
+who is there<end_of_turn>
+<start_of_turn>user
+Gemma<end_of_turn>
+<start_of_turn>model
+Gemma who?<end_of_turn>
+```
+
+The token `"<end_of_turn>\n"` is the turn separator, and the prompt prefix is
+`"<start_of_turn>model\n"`. This means that if you'd like to prompt the model
+with a question like, "What is Cramer's Rule?", you should instead feed the
+model as follows:
+
+```text
+"<start_of_turn>user
+What is Cramer's Rule?<end_of_turn>
+<start_of_turn>model"
+```
+
+Note that if you want to finetune the pretrained Gemma models with your own
+data, you can use any such schema for control tokens, as long as it's consistent
+between your training and inference use cases.
+
+## System instructions
+
+Gemma's instruction-tuned models are designed to work with only two roles:
+`user` and `model`. Therefore, the `system` role or a system turn is not
+supported.
+
+Instead of using a separate system role, provide system-level instructions
+directly within the initial user prompt. The model instruction following
+capabilities allow Gemma to interpret the instructions effectively. For example:
+
+```text
+<start_of_turn>user
+Only reply like a pirate.
+
+What is the answer to life the universe and everything?<end_of_turn>
+<start_of_turn>model
+Arrr, 'tis 42,<end_of_turn>
+```
diff --git a/docs/get-started/quickstart.md b/docs/get-started/quickstart.md
index 8d042b4..331ef23 100644
--- a/docs/get-started/quickstart.md
+++ b/docs/get-started/quickstart.md
@@ -11,11 +11,11 @@ The Gemma family of open models includes a range of model sizes, capabilities, a
 
 | Size | Variant | Input ➔ Output | Intended Platform |
 |---|---|---|---|
-| **E2B** | [Gemma 4 (core)](https://ai.google.dev/gemma/docs/core) | Text, images, audio ➔ Text | Mobile devices |
-| **E4B** | [Gemma 4 (core)](https://ai.google.dev/gemma/docs/core) | Text, images, audio ➔ Text | Mobile devices and laptops |
-| **12B** | [Gemma 4 (core)](https://ai.google.dev/gemma/docs/core) | Text, images, audio ➔ Text | Laptops, desktop computers and small servers |
-| **A4B** | [Gemma 4 (core)](https://ai.google.dev/gemma/docs/core) | Text, images ➔ Text | Desktop computers and small servers |
-| **31B** | [Gemma 4 (core)](https://ai.google.dev/gemma/docs/core) | Text, images ➔ Text | Large servers or server clusters |
+| **E2B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images, audio ➔ Text | Mobile devices |
+| **E4B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images, audio ➔ Text | Mobile devices and laptops |
+| **12B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images, audio ➔ Text | Laptops, desktop computers and small servers |
+| **A4B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images ➔ Text | Desktop computers and small servers |
+| **31B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images ➔ Text | Large servers or server clusters |
 
 !!! tip "New to Gemma?"
     A good place to start is the Gemma 4 12B model, which can be used for many tasks and runs on laptop hardware.
diff --git a/docs/get-started/setup.md b/docs/get-started/setup.md
new file mode 100644
index 0000000..046161c
--- /dev/null
+++ b/docs/get-started/setup.md
@@ -0,0 +1,58 @@
+# Legacy Gemma setup
+
+!!! Note
+    This is only relevant for Gemma 3 and prior versions.
+
+
+This page provides setup instructions for using Gemma in Colab. Some of the
+instructions are applicable to other development environments as well.
+
+## Get access to Gemma
+
+Before using Gemma for the first time, you must request access to the
+model through Kaggle. As part of the process, you'll have to use a Kaggle
+account to accept the Gemma use policy and license terms.
+
+If you don't already have a Kaggle account, you can register for one at
+[kaggle.com](https://www.kaggle.com). Then complete the following steps:
+
+1. Go to the [Gemma model card](https://www.kaggle.com/models/google/gemma) and select **Request Access**.
+2. Complete the consent form and accept the terms and conditions.
+
+## Select a Colab runtime
+
+To complete a Colab tutorial, you must have a Colab runtime with sufficient
+resources to run the Gemma model. To [get started](quickstart.md), you can
+use a T4 GPU:
+
+1. In the upper-right of the Colab window, select ▾ (**Additional connection options**).
+2. Select **Change runtime type**.
+3. Under **Hardware accelerator** , select **T4 GPU**.
+
+## Configure your API key
+
+To use Gemma, you must provide your Kaggle username and a Kaggle API key. To
+generate and configure these values, follow these steps:
+
+1. To generate a Kaggle API key, go to the **Account** tab of your Kaggle [user
+   profile](https://www.kaggle.com/settings) and select **Create New Token** . This will trigger the download of a `kaggle.json` file containing your API credentials.
+2. Open `kaggle.json` in a text editor. The contents should look something like
+   this:
+
+```text
+   {"username":"your_username","key":"012345678abcdef012345678abcdef1a"}
+```
+
+3. In Colab, select **Secrets** (🔑) and add your Kaggle username and Kaggle
+   API key. Store your username under the name `KAGGLE_USERNAME` and your API
+   key under the name `KAGGLE_KEY`.
+
+!!! Note
+    Kaggle notebooks have a key storage feature under **Add-ons** \> **Secrets**, along with instructions for accessing stored keys.
+
+
+Now you're ready to complete the remaining setup steps in Colab. If you're
+working through a Colab tutorial, go to Colab and set the environment variables.
+
+!!! Tip
+    As an alternative to setting environment variables, you can use `kagglehub` to [authenticate](https://github.com/Kaggle/kagglehub?tab=readme-ov-file#authenticate).
diff --git a/docs/learn/business-email-assistant.md b/docs/learn/business-email-assistant.md
new file mode 100644
index 0000000..e8a2d50
--- /dev/null
+++ b/docs/learn/business-email-assistant.md
@@ -0,0 +1,464 @@
+# Build a business email AI assistant with Gemma
+
+[Video](https://www.youtube.com/watch?v=YxhzozLH1Dk)
+
+Handling customer inquiries, including emails, is a necessary part of running
+many businesses, but it can quickly get overwhelming. With a bit of effort,
+artificial intelligence (AI) models like Gemma can help make this work easier.
+
+Every business handles inquiries like emails a little differently, so it's
+important to be able to adapt technologies like generative AI to the needs of
+your business. This project tackles the specific problem of extracting order
+information from emails to a bakery into structured data, so it can be quickly
+added to an order handling system. Using 10 to 20 examples of inquiries and
+the output you want, you can tune a Gemma model to process emails from your
+customers, help you respond quickly, and integrate with your existing business systems.
+This project is built as an AI application pattern you can extend and adapt to
+get value from Gemma models for your business.
+
+For a video overview of the project and how to extend it, including insights
+from the folks who built it, check out the
+[Business Email AI Assistant](https://youtu.be/YxhzozLH1Dk)
+Build with Google AI video. You can also review the code for this project in the
+[Gemma Cookbook code repository](https://github.com/google-gemini/gemma-cookbook/tree/main/Demos/business-email-assistant).
+Otherwise, you can get started extending the project using the following
+instructions.
+
+## Overview
+
+This tutorial walks you through setting up, running, and extending a business
+email assistant application built with Gemma, Python, and Flask. The project
+provides a basic web user interface that you can modify to fit your needs. The
+application is built to extract data from customer emails into a structure
+for a fictitious bakery. You can use this application pattern for any business
+task that uses text input and text output.
+
+![Screenshot of web app user interface](../assets/learn/bite-webapp.png)
+
+**Figure 1.** Project user interface for processing bakery email inquiries
+
+## Hardware requirements
+
+Run this tuning process on a computer with a graphics processing unit (GPU) or a
+Tensor processing unit (TPU), and sufficient GPU or TPU memory to hold the
+existing model, plus the tuning data. To run the tuning configuration in this
+project, you need about 16GB of GPU memory, about the same amount of regular
+RAM, and a minimum of 50GB of disk space.
+
+You can run the Gemma model tuning portion of this tutorial using a Colab
+environment with a **T4 GPU** runtime. If you are building this project on a
+Google Cloud
+[VM instance](https://cloud.google.com/compute/docs/instances),
+configure the instance following these requirements:
+
+- **GPU hardware**: A NVIDIA T4 is required to run this project (NVIDIA L4 or higher recommended)
+- **Operating System** : Choose a **Deep Learning on Linux** option, specifically **Deep Learning VM with CUDA 12.3 M124** with pre-installed GPU software drivers.
+- **Boot disk size** : Provision at least **50GB** of disk space for your data, models, and supporting software.
+
+## Project setup
+
+These instructions walk you through getting this project ready for development
+and testing. The general setup steps include installing prerequisite software,
+cloning the project from the code repository, setting a few environment
+variables, installing Python libraries, and testing the web application.
+
+### Install and configure
+
+This project uses Python 3 and Virtual Environments (`venv`) to manage packages
+and run the application. The following installation instructions are for a Linux
+host machine.
+
+To install the required software:
+
+- Install Python 3 and the `venv` virtual environment package for Python:
+
+```bash
+  sudo apt update
+  sudo apt install git pip python3-venv
+```
+
+#### Clone the project
+
+Download the project code to your development computer. You need
+[git](https://git-scm.com/) source control software to retrieve the
+project source code.
+
+To download the project code:
+
+1. Clone the git repository using the following command:
+
+```bash
+   git clone https://github.com/google-gemini/gemma-cookbook.git
+```
+
+2. Optionally, configure your local git repository to use sparse checkout,
+   so you have only the files for the project:
+
+```bash
+   cd gemma-cookbook/
+   git sparse-checkout set Demos/business-email-assistant/
+   git sparse-checkout init --cone
+```
+
+#### Install Python libraries
+
+Install the Python libraries with the `venv` Python virtual environment
+activated to manage Python packages and dependencies. Make sure you activate the
+Python virtual environment *before* installing Python libraries with the `pip`
+installer. For more information about using Python virtual environments, see the
+[Python venv](https://docs.python.org/3/library/venv.html) documentation.
+
+To install the Python libraries:
+
+1. In a terminal window, navigate to the `business-email-assistant` directory:
+
+```bash
+   cd Demos/business-email-assistant/
+```
+
+2. Configure and activate Python virtual environment (venv) for this project:
+
+```text
+   python3 -m venv venv
+   source venv/bin/activate
+```
+
+3. Install the required Python libraries for this project using the
+   `setup_python` script:
+
+```text
+   ./setup_python.sh
+```
+
+!!! Tip
+    On Linux operating systems, you may need to allow execution of the bash script by running the command `chmod +x setup_python.sh`.
+
+
+#### Set environment variables
+
+This project requires a few environment variables to run,
+including a Kaggle username and a Kaggle API token. You must have a Kaggle
+account and request access to the Gemma models to be able to download them. For
+this project, you add your Kaggle Username and Kaggle API token to two `.env`
+files, which are read by the web application and the tuning program,
+respectively.
+
+!!! Caution
+    Treat your Kaggle API token like a password and protect it appropriately. Don't embed your key in publicly published code.
+
+
+To set the environment variables:
+
+1. Obtain your Kaggle username and your token key by following the instructions in the [Kaggle documentation](https://www.kaggle.com/docs/api#authentication).
+2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
+3. Create environment variable files for the project, by creating a `.env` text file at *each* these locations in your clone of the project:
+
+``` { .no-copy .output }
+   email-processing-webapp/.env
+   model-tuning/.env
+```
+4. After creating the `.env` text files, add the following settings to **both**
+   files:
+
+```python
+   KAGGLE_USERNAME=<YOUR_KAGGLE_USERNAME_HERE>
+   KAGGLE_KEY=<YOUR_KAGGLE_KEY_HERE>
+```
+
+### Run and test the application
+
+Once you have completed the installation and configuration of the project, run
+the web application to confirm that you have configured it correctly. You should
+do this as a baseline check before editing the project for your own use.
+
+To run and test the project:
+
+1. In a terminal window, navigate to the `email-processing-webapp` directory:
+
+```bash
+   cd business-email-assistant/email-processing-webapp/
+```
+
+2. Run the application using the `run_app` script:
+
+```text
+   ./run_app.sh
+```
+
+3. After starting the web application, the program code lists a URL where
+   you can browse and test. Typically, this address is:
+
+```text
+   http://127.0.0.1:5000/
+```
+
+4. In the web interface, press the **Get data** button below the first input
+   field to generate a response from the model.
+
+The first response from the model after you run the application takes longer
+since it must complete initialization steps on the first generation run.
+Subsequent prompt requests and generation on an already-running web application
+complete in less time.
+
+## Extend the application
+
+Once you have the application running, you can extend it by modifying the user
+interface and business logic to make it work for tasks that are relevant to you
+or your business. You can also modify the behavior of the Gemma model using the
+application code by changing the components of the prompt that the app sends to
+the generative AI model.
+
+The application provides instructions to the model along with the input data
+from the user a complete prompt of the model. You can modify these instructions
+to change the behavior of the model, such as specifying the names of parameters
+and structure of the JSON to generate. A simpler way to change the behavior of
+the model is to provide additional instructions or guidance for the model's
+response, such as specifying that the generated replies should not include any
+Markdown formatting.
+
+To modify prompt instructions:
+
+1. In the development project, open the `business-email-assistant/email-processing-webapp/app.py` code file.
+2. In the `app.py` code, add additions instructions to the
+   `get_prompt():` function:
+
+```python
+   def get_prompt():
+     return """
+       Extract the relevant details of this request and return them in
+       JSON code, with no additional markdown formatting:\n"""
+```
+
+This example adds the phrase "with no additional markdown formatting" to the
+instructions.
+
+Providing additional prompt instruction can strongly influence the generated
+output, and takes significantly less effort to implement. You should try this
+method first to see if you can get the behavior you want from the model.
+However, using prompt instructions to modify a Gemma model's behavior has its
+limits. In particular, the overall input token limit of the model, which is
+8,192 tokens for Gemma 2, requires you to balance detailed prompt instructions
+with the size of the new data you provide so that you stay under that limit.
+
+## Tune the model
+
+Performing fine-tuning of a Gemma model is the recommended way to get it to
+respond more reliably for specific tasks. In particular, if you want the model
+to generate JSON in with a specific structure, including specifically named
+parameters, you should consider tuning the model for that behavior.
+Depending on the task you want the model to complete, you can achieve basic
+functionality with 10 to 20 examples. This section of the tutorial
+explains how to set up and run fine-tuning on a Gemma model for a specific task.
+
+The following directions explain how to perform the fine-tuning operation on a
+VM environment, however, you can also perform this tuning operation using the
+associated
+[Colab noteboook](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/Demos/business-email-assistant/model-tuning/notebook/bakery_inquiry_model_tuned_with_gemma.ipynb)
+for this project.
+
+#### Hardware requirements
+
+The compute requirements for fine-tuning are the same as the
+[hardware requirements](business-email-assistant.md#hardware-requirements) for rest of the project. You can
+run the tuning operation in a Colab environment with a **T4 GPU** runtime *if*
+you limit the input tokens to 256 and the batch size to 1.
+
+### Prepare data
+
+Before you start tuning a Gemma model, you must prepare data for tuning. When
+you are tuning a model for a specific task, you need a set of request and
+response examples. These examples should show the request text, *without any
+instructions*, and the expected response text. To start with, you should prepare
+a dataset with around 10 examples. These examples should represent a full
+variety of requests and the ideal responses. Make sure that the requests and
+responses are not repetitive, as that can cause the models responses to be
+repetitive and not adjust appropriately to variations in requests. If you are
+tuning the model to produce a structured data format, make sure all the provided
+responses strictly conform to the data output format you want. The following
+table shows a few sample records from the dataset of this code example:
+
+| Request | Response |
+|---|---|
+| Hi Indian Bakery Central,\\nDo you happen to have 10 pendas, and thirty bundi ladoos on hand? Also do you sell a vanilla frosting and chocolate flavor cakes. I'm looking for a 6 inch size | { "type": "inquiry", "items": \[ { "name": "pendas", "quantity": 10 }, { "name": "bundi ladoos", "quantity": 30 }, { "name": "cake", "filling": null, "frosting": "vanilla", "flavor": "chocolate", "size": "6 in" } \] } |
+| I saw your business on google maps. Do you sell jellabi and gulab jamun? | { "type": "inquiry", "items": \[ { "name": "jellabi", "quantity": null }, { "name": "gulab jamun", "quantity": null } \] } |
+
+**Table 1.** Partial listing of the tuning dataset for the bakery email data
+extractor.
+
+#### Data format and loading
+
+You can store your tuning data in any format that is convenient, including
+database records, JSON files, CSV, or plain text files, as long as you have the
+means to retrieve the records with Python code. This project reads JSON files
+from a `data` directory into an array of dictionary objects.
+In this example tuning program, the tuning dataset is loaded in the
+`model-tuning/main.py` module using the `prepare_tuning_dataset()` function:
+
+```python
+def prepare_tuning_dataset():
+    # collect data from JSON files
+    prompt_data = read_json_files_to_dicts("./data")
+    ...
+```
+
+As mentioned previously, you can store the dataset in a format that is
+convenient, as long as you can retrieve the requests with the associated
+responses and assemble them into a text string which is used as a tuning record.
+
+#### Assemble tuning records
+
+For the actual tuning process, the program assembles each request and response
+into a single string with the prompt instructions and the content of the
+response. The tuning program then tokenizes the string for consumption by the
+model. You can see the code for assembling a tuning record in the
+`model-tuning/main.py` module `prepare_tuning_dataset()` function, as follows:
+
+```python
+def prepare_tuning_dataset():
+    ...
+    # prepare data for tuning
+    tuning_dataset = []
+    template = "{instruction}\n{response}"
+
+    for prompt in prompt_data:
+        tuning_dataset.append(template.format(instruction=prompt["prompt"],
+                                              response=prompt["response"]))
+
+    return tuning_dataset
+```
+
+This function takes the data as its input and formats it by adding a line break
+between the instruction and the response.
+
+!!! Important
+    Make sure you are using the Gemma tokenizer when creating tuning records. Using an incompatible tokenizer can cause your tuning output to not work with the Gemma model and cause it to generate unhelpful results.
+
+
+### Generate model weights
+
+Once you have the tuning data in place and loading, you can run the tuning
+program. The tuning process for this example application uses the Keras NLP
+library to tune the model with a
+[Low Rank Adaptation](https://arxiv.org/pdf/2106.09685), or LoRA technique,
+to generate new model weights. Compared to full precision tuning, using LoRA is
+significantly more memory-efficient because it approximates the changes to the
+model weights. You can then overlay these approximated weights onto the existing
+model weights to change the model's behavior.
+
+To perform the tuning run and calculate new weights:
+
+1. In a terminal window, navigate to the `model-tuning/` directory.
+
+```bash
+   cd business-email-assistant/model-tuning/
+```
+
+2. Run the tuning process using the `tune_model` script:
+
+```text
+   ./tune_model.sh
+```
+
+The tuning process takes several minutes depending on your available compute
+resources. When it completes successfully, the tuning program writes new `*.h5`
+weight files in the `model-tuning/weights` directory with the following format:
+
+``` { .no-copy .output }
+gemma2-2b_inquiry_tuned_4_epoch##.lora.h5
+```
+
+#### Troubleshooting
+
+If the tuning does not complete successfully, there are two likely reasons:
+
+- **Out of memory or resources exhausted** : These errors occur when the tuning process requests memory that exceeds the available GPU memory or CPU memory. Make sure you are not running the web application while the tuning process is running. If you are tuning on a device with 16GB of GPU memory, makes sure your have the `token_limit` set to **256** and the `batch_size` set to **1**.
+- **GPU drivers not installed or incompatible with JAX** : The tuning process requires that the compute device has hardware drivers installed which are compatible with the version of the [JAX libraries](https://jax.readthedocs.io/). For more details, see the [JAX installation](https://jax.readthedocs.io/en/latest/installation.html) documentation.
+
+## Deploy tuned model
+
+The tuning process generates multiple weights based on the tuning data and the
+total number of epochs set in the tuning application. By default, the tuning
+program generates 3 model weight files, one for each tuning epoch. Each
+successive tuning epoch produces weights that more accurately reproduce the
+results of the tuning data. You can see the accuracy rates for each epoch in the
+terminal output of the tuning process, as follows:
+
+```text
+...
+8/8 ━━━━━━━━━━━━━━━━━━━━ 121s 195ms/step - loss: 0.5432 - sparse_categorical_accuracy: 0.5982
+Epoch 2/3
+8/8 ━━━━━━━━━━━━━━━━━━━━ 2s 194ms/step - loss: 0.3320 - sparse_categorical_accuracy: 0.6966
+Epoch 3/3
+8/8 ━━━━━━━━━━━━━━━━━━━━ 2s 192ms/step - loss: 0.2135 - sparse_categorical_accuracy: 0.7848
+```
+
+While you want the accuracy rate to be relatively high, around 0.80, you do not
+want the rate to be too high, or very close to 1.00, because that means the
+weights have come close to overfitting the tuning data. When that happens, the
+model does not perform well on requests that are significantly different from
+the tuning examples. By default, the deployment script picks the epoch 3
+weights, which typically have an accuracy rate around 0.80.
+
+To deploy the generated weights to the web application:
+
+1. In a terminal window, navigate to the `model-tuning` directory:
+
+```bash
+   cd business-email-assistant/model-tuning/
+```
+
+2. Run the tuning process using the `deploy_weights` script:
+
+```text
+   ./deploy_weights.sh
+```
+
+After running this script, you should see a new `*.h5` file in the
+`email-processing-webapp/weights/` directory.
+
+### Test the new model
+
+Once you have deployed the new weights to the application, it's time to try out
+the newly tuned model. You can do this by re-running the web application and
+generating a response.
+
+To run and test the project:
+
+1. In a terminal window, navigate to the `email-processing-webapp` directory:
+
+```bash
+   cd business-email-assistant/email-processing-webapp/
+```
+
+2. Run the application using the `run_app` script:
+
+```text
+   ./run_app.sh
+```
+
+3. After starting the web application, the program code lists a URL where
+   you can browse and test, typically this address is:
+
+```text
+   http://127.0.0.1:5000/
+```
+
+4. In the web interface, press the **Get data** button below the first input
+   field to generate a response from the model.
+
+You have now tuned and deployed a Gemma model in an application! Experiment with
+the application and try to determine the limits of the tuned model's generation
+capability for your task. If you find scenarios where the model does not perform
+well, consider adding some of those requests to your list of tuning example data
+by adding the request and providing an ideal response. Then re-run the tuning
+process, re-deploy the new weights, and test the output.
+
+## Additional resources
+
+For more information about this project, see the
+[Gemma Cookbook code repository](https://github.com/google-gemini/gemma-cookbook/tree/main/Demos/business-email-assistant).
+If you need help building the application or are looking to collaborate with
+other developers, check out the
+[Google Developers Community Discord](https://ai.google.dev/docs/discord)
+server. For more Build with Google AI projects, check out the
+[video playlist](https://goo.gle/BuildwithGoogleAI).
diff --git a/docs/learn/personal-code-assistant.md b/docs/learn/personal-code-assistant.md
new file mode 100644
index 0000000..8684105
--- /dev/null
+++ b/docs/learn/personal-code-assistant.md
@@ -0,0 +1,551 @@
+# Build a personal AI coding assistant with Gemma
+
+[Video](https://www.youtube.com/watch?v=Zpo7UTvg_9E)
+
+Getting code assistance from artificial intelligence (AI) models can be very
+useful, but what if you are restricted from using third-party, hosted generative
+AI models because of connectivity, cost, or data security restrictions? Google's
+family of Gemma models are available to download and run on your own hardware,
+so you can keep everything local and even have the option to tune the model to
+work better with your codebase.
+
+Running your own instance of [Gemma](../models/gemma-4/index.md) or
+CodeGemma can get
+you AI coding assistance with low latency, high availability, potentially lower
+cost, and the ability to keep all your coding data on your own network. This
+project shows you how to set up your own web service for hosting Gemma and
+connecting it to a Microsoft Visual Studio Code extension, to make using the
+model more convenient while coding. This project includes two sub-projects: One
+project to set up and wrap Gemma into a web service, and a second project for a
+VS Code extension that connects and uses the web service.
+
+For a video overview of this project and how to extend it, including insights
+from the folks who built it, check out the
+[Personal AI Code Assistant](https://youtu.be/Zpo7UTvg_9E)
+Build with Google AI video. You can also review the code for this project in the
+[Gemma Cookbook code repository](https://github.com/google-gemini/gemma-cookbook/tree/main/Demos/personal-code-assistant).
+Otherwise, you can get started extending the project using the following
+instructions.
+
+## Overview
+
+This tutorial shows you how to set up and extend two projects: a web service for
+Gemma and a VS Code extension to use to that service. The web service uses
+Python, Keras, JAX, and the FastAPI libraries to serve the Gemma model and
+handle requests. The VS Code extension, called Pipet, adds commands to the
+Command Palette that let you make requests to the Gemma web service by selecting
+code, text, or comments in a code editing window, as shown in Figure 1.
+
+![Screenshot of VS Code extension user interface](../assets/learn/pipet-2.png)
+
+**Figure 1.** Project command user interface for the Pipet extension in Visual
+Studio Code
+
+The complete source code for both projects is provided in the Gemma Cookbook
+[code repository](https://github.com/google-gemini/gemma-cookbook/tree/main/Demos/personal-code-assistant)
+and you can extend both projects to adapt to your needs and preferred workflow.
+
+## Project setup
+
+These instructions walk you through getting this project ready for development
+and testing. The general setup steps include installing prerequisite software,
+cloning the project from the code repository, setting a few environment
+variables, installing Python and Node.js libraries, and testing the web
+application.
+
+!!! Note
+    The following setup instructions are for a Linux host machine.
+
+
+#### Install required software
+
+This project uses Python 3, Virtual Environments (`venv`), Node.js, and Node
+Package Manager (`npm`) to manage packages and run the two projects.
+
+To install the required software:
+
+- Install Python 3, the virtual environment (`venv`)package for Python,
+  Node.js, and the Node.js package manager (`npm`):
+
+```bash
+  sudo apt update
+  sudo apt install git pip python3-venv nodejs npm
+```
+
+#### Clone the project
+
+Download the project code to your development computer. You need
+[git](https://git-scm.com/) source control software to retrieve the
+project source code.
+
+To download the project code:
+
+1. Clone the git repository using the following command:
+
+```bash
+   git clone https://github.com/google-gemini/gemma-cookbook.git
+```
+
+2. Optional: Configure your local git repository to use sparse checkout,
+   so you have only the files for the project:
+
+```bash
+   cd gemma-cookbook/
+   git sparse-checkout set Demos/personal-code-assistant/
+   git sparse-checkout init --cone
+```
+
+## Gemma web service project
+
+The web service portion of this project (`gemma-web-service`) creates an
+independently hosted instance of Gemma 2 2B wrapped with a basic web service to
+handle generation requests and responses. The [VS Code extension](personal-code-assistant.md#vs-code-extension-project),
+covered later in this tutorial, connects to this service to handle code
+assistance requests.
+
+!!! Tip
+    You can set up the web service to use CodeGemma model variants instead. However, the CodeGemma 2B model size has different input requirements for code generation. The larger sizes of CodeGemma are more flexible in terms of how you prompt them, but also have higher hardware resource requirements.
+
+
+These instructions walk you through getting this project ready for development
+and testing. The general setup steps include installing prerequisite software,
+cloning the project from the code repository, setting a few environment
+variables, installing Python libraries, and testing the web service.
+
+### Hardware requirements
+
+Run the Gemma web service project on a computer with a graphics processing unit
+(GPU) or a Tensor processing unit (TPU), and sufficient GPU or TPU memory to
+hold the model. To run the Gemma 2 2B configuration in this web service project,
+you need about 16GB of GPU memory, about the same amount of regular RAM, and a
+minimum of 20GB of disk space.
+
+If you are deploying the Gemma web service project on a Google Cloud
+[VM instance](https://cloud.google.com/compute/docs/instances),
+configure the instance following these requirements:
+
+- **GPU hardware**: A NVIDIA T4 is required to run this project (NVIDIA L4 or higher recommended)
+- **Operating System** : Choose a **Deep Learning on Linux** option, specifically **Deep Learning VM with CUDA 12.3 M124** with pre-installed GPU software drivers.
+- **Boot disk size** : Provision at least **20GB** of disk space for your data, model, and supporting software.
+
+### Configure project
+
+This project uses Python 3 and Virtual Environments (`venv`) to manage packages
+and run the web service. Install the Python libraries with the `venv` Python
+virtual environment activated to manage Python packages and dependencies. Make
+sure you activate the Python virtual environment *before* installing Python
+libraries with the `setup_python` script or with the `pip` installer. For more
+information about using Python virtual environments, see the
+[Python venv](https://docs.python.org/3/library/venv.html) documentation.
+
+To install the Python libraries:
+
+1. In a terminal window, navigate to the `gemma-web-service` directory:
+
+```bash
+   cd Demos/personal-code-assistant/gemma-web-service/
+```
+
+2. Configure and activate a Python virtual environment (venv) for this project:
+
+```text
+   python3 -m venv venv
+   source venv/bin/activate
+```
+
+3. Install the required Python libraries for this project using the
+   `setup_python` script:
+
+```text
+   ./setup_python.sh
+```
+
+!!! Tip
+    On Linux operating systems, you may need to allow execution of the bash script by running the command `chmod +x setup_python.sh`.
+
+
+#### Set environment variables
+
+This project requires a few environment variables to run,
+including a Kaggle username and a Kaggle API token. You must have a Kaggle
+account and request access to the Gemma models to be able to download them. For
+this project, you add your Kaggle Username and Kaggle API token in an `.env`
+file, which is used by the web service program to download the model.
+
+!!! Caution
+    Treat your Kaggle API token like a password and protect it appropriately. Don't embed your API token in publicly published code.
+
+
+To set the environment variables:
+
+1. Obtain your Kaggle username and your API token by following the instructions in the [Kaggle documentation](https://www.kaggle.com/docs/api#authentication).
+2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
+3. Create an environment variable file for the project, by creating a
+   `.env` text file at this location in your clone of the project:
+
+```text
+   personal-code-assistant/gemma-web-service/.env
+```
+
+4. After creating the `.env` text file, add the following settings to it:
+
+```python
+   KAGGLE_USERNAME=<YOUR_KAGGLE_USERNAME_HERE>
+   KAGGLE_KEY=<YOUR_KAGGLE_KEY_HERE>
+```
+
+### Run and test the web service
+
+Once you have completed the installation and configuration of the project, run
+the web application to confirm that you have configured it correctly. You should
+do this as a baseline check before editing the project for your own use.
+
+To run and test the project:
+
+1. In a terminal window, navigate to the `gemma-web-service` directory:
+
+```bash
+   cd personal-code-assistant/gemma-web-service/
+```
+
+2. Run the application using the `run_service` script:
+
+```text
+   ./run_service.sh
+```
+
+3. After starting the web service, the program code lists a URL where
+   you can access the service. Typically, this address is:
+
+```text
+   http://localhost:8000/
+```
+
+4. Test the service by running the `test_post` script:
+
+```text
+   ./test/test_post.sh
+```
+
+When you have successfully run and tested the service with this script, you
+should be ready to connect to it with the VS Code extension in the next section
+of this tutorial.
+
+!!! Important
+    Make sure you set your the firewall rules on your virtual machine or server to allow communication on **port 8000**, or a different port if you set that in the web service.
+
+
+## VS Code extension project
+
+The VS Code extension of this project (`pipet-code-agent-2`) creates a software
+extension of the Microsoft Visual Studio Code application that's designed to add
+new AI coding commands. This extension communicates with the
+[Gemma web service](personal-code-assistant.md#gemma-web-service-project) described previously in this tutorial. The
+extension communicates with the web services over http using JSON-format
+messages.
+
+### Configure project
+
+These instructions walk you through getting the Pipet Code Agent v2 project set
+up for development and testing. The general steps are installing required
+software, running the configuration installation, configuring an extension
+setting, and testing the extension.
+
+#### Install required software
+
+The Pipet Code Agent project runs as an extension of Microsoft
+[Visual Studio Code](https://code.visualstudio.com/), and uses
+[Node.js](https://nodejs.org/) and the Node Package Manager (`npm`) tool to
+manage packages and run the application.
+
+To install the required software:
+
+1. Download and install [Visual Studio Code](https://code.visualstudio.com/download) for your platform.
+2. Make sure Node.js is installed by following the [installation
+   instructions](https://nodejs.org/) for your platform.
+
+#### Configure project libraries
+
+Use the `npm` command line tool to download the required dependencies and
+configure the project.
+
+To configure the project code:
+
+1. Navigate to the Pipet Code Agent project root directory.
+
+```bash
+   cd Demos/personal-code-assistant/pipet-code-agent-2/
+```
+
+2. Run the install command to download dependencies and configure the project:
+
+```bash
+   npm install
+```
+
+#### Configure the extension
+
+You should now be able to test your installation by running Pipet Code Agent as
+a development extension in VS Code on your device. The test opens a separate VS
+Code **Extension Development Host** window where the new extension is available.
+In this new window, you configure the settings for the extension uses to access
+your personal Gemma web service.
+
+![Pipet Code Agent running in the Extension Development Host window](../assets/learn/pipet-2-ext-dev-host-settings.png)
+**Figure 2.** VS Code Extension Development Host window with the Pipet
+extension Settings.
+
+To configure and test your setup:
+
+1. Start the VS Code application.
+2. In VS Code, create a new window by selecting **File \> New Window**.
+3. Open the Pipet Code Agent project by selecting **File \> Open Folder** , and selecting the `personal-code-assistant/pipet-code-agent-2/` folder.
+4. Open the `pipet-code-agent-2/src/extension.ts` file.
+5. Run the extension in debug mode by selecting **Run \> Start Debugging** and if necessary, select the **VS Code Extension Development Host** option. This step opens a separate **Extension Development Host** window.
+6. In the new VS Code window, open the VS Code settings by selecting **Code \> Settings \> Settings**.
+7. Set the host address of your Gemma web service server as a configuration
+   setting. In the **Search Settings** field, type `Gemma`, select the **User**
+   tab, and in the **Gemma \> Service: Host** setting, click the **Edit in
+   settings.json** link, and add the host address such as `127.0.0.1`,
+   `localhost`, or `my-server.my-local-domain.com`:
+
+```text
+   "gemma.service.host": "your-host-address-here"
+```
+
+8. Save the changes to the `settings.json` file and close the settings tabs.
+
+#### Test the extension
+
+You should now be able to test your installation by running Pipet Code Agent as
+a development extension in VS Code on your device. The test opens a separate VS
+Code **Extension Development Host** window where the new extension is available.
+
+To test the extension commands:
+
+1. In the VS Code **Extension Development Host** window, select any code in the editor window.
+2. Open the command palette by selecting **View \> Command Palette**.
+3. In the Command Palette, type `Pipet` and select one of the commands with that prefix.
+
+### Modify existing commands
+
+Modifying the commands provided in Pipet Code Agent is the simplest way to
+change the behavior and capabilities of the extension. This prompt context
+information guides the Gemma generative model in forming a response. By changing
+the prompt instructions in the existing Pipet commands, you can change
+how each of the commands behave.
+
+This set of instructions explains how to modify the `review.ts` command by
+changing the prompt text of the command.
+
+To prepare to edit the `review.ts` command:
+
+1. Start the VS Code application.
+2. In VS Code, create a new window by selecting **File \> New Window**.
+3. Open the Pipet Code Agent project by selecting **File \> Open Folder** , and selecting the `pipet-code-agent/` folder.
+4. Open `pipet-code-agent/src/review.ts` file.
+
+To modify the behavior of the `review.ts` command:
+
+1. In the `review.ts` file, change the second to last line of
+   `PROMPT_INSTRUCTIONS` constant to add `Also note potential
+   performance improvements`.
+
+```python
+   const PROMPT_INSTRUCTIONS = `
+   Reviewing code involves finding bugs and increasing code quality. Examples of
+   bugs are syntax errors or typos, out of memory errors, and boundary value
+   errors. Increasing code quality entails reducing complexity of code, eliminating
+   duplicate code, and ensuring other developers are able to understand the code.
+   Also note potential performance improvements.
+
+   Write a review of the following code:
+   `;
+```
+
+2. Save the changes to the `review.ts` file.
+
+To test the modified command:
+
+1. In your VS Code Pipet extension project window, open the `src/extension.ts` file.
+2. Build the updated code by selecting **Terminal \> Run Build Task...** and then the **npm: compile** option.
+3. Restart the debugger by selecting **Run \> Restart Debugging**.
+4. In the VS Code **Extension Development Host** window, select any code in the editor window.
+5. Open the command palette by selecting **View \> Command Palette**.
+6. In the Command Palette, type `Pipet` and select the **Pipet: Review the
+   selected code** command.
+
+### Create new commands
+
+You can extend Pipet by creating new commands that perform completely new tasks
+with the Gemma model. Each command file, such as `comment.ts` or `review.ts`, is
+mostly self-contained, and includes code for collecting text from the active
+editor, composing a prompt, connecting to the Gemma web service, sending a
+prompt, and handling the response.
+
+This set of instructions explains how to build a new command using the code of
+an existing command, `question.ts`, as a template.
+
+To create a command that recommends names for functions:
+
+1. Make a copy of the `pipet-code-agent-2/src/question.ts` file called `new-service.ts` in the `src/` directory.
+2. In VS Code, open the `src/new-service.ts` file.
+3. Change the prompt instructions in the new file by
+   editing the `PROMPT_INSTRUCTIONS` value.
+
+```python
+   // Provide instructions for the AI model
+   const PROMPT_INSTRUCTIONS = `
+   Build a Python web API service using FastAPI and uvicorn.
+    - Just output the code, DO NOT include any explanations.
+    - Do not include an 'if __name__ == "__main__":' statement.
+    - Do not include a '@app.get("/")' statement
+    - Do not include a '@app.get("/info")' statement
+   `;
+```
+
+4. Add the service boilerplate by creating a new
+   `BOILERPLATE_CODE` constant.
+
+```python
+   const BOILERPLATE_CODE = `
+   # the following code for testing and diagnosis:
+   @app.get("/")
+   async def root():
+       return "Server: OK"
+
+   @app.get("/info")
+   async def info():
+       return "Service using FastAPI version: " + fastapi.__version__
+
+   # Run the service
+   if __name__ == "__main__":
+       # host setting makes service available to other devices
+       uvicorn.run(app, host="0.0.0.0", port=8000)
+   `;
+```
+
+5. Change the name of the command function to `newService()`
+   and update its information message.
+
+```python
+   export async function newService() {
+     vscode.window.showInformationMessage('Building new service from template...');
+   ...
+```
+
+6. Update the prompt assembly code to include the text selected in the editor
+   and the `PROMPT_INSTRUCTIONS`.
+
+```python
+   // Build the full prompt using the template.
+     const promptText = `${selectedCode}${PROMPT_INSTRUCTIONS}`;
+```
+
+7. Change the response insert code to include the response and the
+   boilerplate code.
+
+```python
+   // Insert answer after selection.
+   editor.edit((editBuilder) => {
+       editBuilder.insert(selection.end, "\n\n" + responseText);
+       editBuilder.insert(selection.end, "\n" + BOILERPLATE_CODE);
+   });
+```
+
+8. Save the changes to the `new-service.ts` file.
+
+#### Integrate the new command
+
+After you complete the code for the new command, you need to integrate it with
+the rest of the extension. Update the `extension.ts` and `package.json` files to
+make the new command part of the extension, and enable VS Code to invoke
+the new command.
+
+To integrate the `new-service` command with the extension code:
+
+1. In VS Code, open the `pipet-code-agent-2/src/extension.ts` file.
+2. Add the new code file to the extension by adding a new import
+   statement.
+
+```text
+   import { newService } from './new-service';
+```
+
+3. Register the new command by adding the following code to the
+   `activate()` function.
+
+```python
+   export function activate(context: vscode.ExtensionContext) {
+       ...
+       vscode.commands.registerCommand('pipet-code-agent.newService', newService);
+   }
+```
+
+4. Save the changes to the `extension.ts` file.
+
+To integrate the `name` command with the extension package:
+
+1. In VS Code, open the `pipet-code-agent/package.json` file.
+2. Add the new command to the `commands` section of the package file.
+
+```text
+   "contributes": {
+     "commands": [
+       ...
+       {
+         "command": "pipet-code-agent.newService",
+         "title": "Pipet: Generate a FastAPI service."
+       }
+     ],
+```
+
+3. Save the changes to the `package.json` file.
+
+#### Test the new command
+
+Once you have completed coding the command and integrating it with the
+extension, you can test it. Your new command is only available in the VS Code
+**Extension Development Host** window, and *not* in the VS Code window where you
+edited the code for the extension.
+
+To test the modified command:
+
+1. In your VS Code Pipet extension project window, open the `src/extension.ts` file.
+2. Build the updated code by selecting **Terminal \> Run Build Task...** and then the **npm: compile** option.
+3. In your VS Code Pipet extension project window, restart the debugger by selecting **Run \> Restart Debugging** , which restarts a separate **Extension Development Host** window.
+4. In the VS Code **Extension Development Host** window, select some code in the editor window.
+5. Open the command palette by selecting **View \> Command Palette**.
+6. In the Command Palette, type `Pipet` and select the **Pipet: Generate a
+   FastAPI service** command.
+
+You have now built a VS Code extension command that works with a Gemma AI
+model! Try experimenting with different commands and instructions to build an
+AI-assisted code development workflow that works for you!
+
+### Package and install the extension
+
+You can package your extension as a `.vsix` file for local installation in your
+VS Code instance. Use the `vsce` command line tool to generate a `.vsix` package
+file from your extension project, which you can then install in your VS Code
+instance. For details on packaging your extension, see the VS Code
+[Publishing Extensions](https://code.visualstudio.com/api/working-with-extensions/publishing-extension#review-extension-installs-and-ratings)
+documentation. When you have completed packaging your extension as a VSIX file,
+you can then manually install it into VS Code.
+
+To install the VSIX packaged extension:
+
+1. In your VS Code instance, open the **Extensions** panel by choosing **File \>
+   Extensions**.
+2. In the **Extensions** panel, select the three dot menu on the top right and then **Install from VSIX**.
+3. Open the `.vsix` package file you generated from your extension project to install it.
+
+## Additional resources
+
+For more details on the code for this project, see the
+[Gemma Cookbook code repository](https://goo.gle/3Y8Y8b8).
+If you need help building the application or are looking to collaborate with
+other developers, check out the
+[Google Developers Community Discord](https://ai.google.dev/docs/discord)
+server. For more Build with Google AI projects, check out the
+[video playlist](https://goo.gle/BuildwithGoogleAI).
diff --git a/docs/models/datagemma/index.md b/docs/models/datagemma/index.md
index b40309e..4085a37 100644
--- a/docs/models/datagemma/index.md
+++ b/docs/models/datagemma/index.md
@@ -6,7 +6,7 @@
 DataGemma is a research tool that lets users ask questions in plain language and receive
 answers based on publicly available statistical data in the
 [Data Commons](https://datacommons.org/) repository. The tool uses
-specially built versions of [Gemma](https://ai.google.dev/gemma/docs/core),
+specially built versions of [Gemma](../gemma-4/index.md),
 the [Gemini API](https://ai.google.dev/gemini-api) with Gemini 1.5 Pro, and a set of libraries
 specifically designed to work with Data Commons.
 
diff --git a/docs/models/diffusiongemma/index.md b/docs/models/diffusiongemma/index.md
index ed488c2..b7c581b 100644
--- a/docs/models/diffusiongemma/index.md
+++ b/docs/models/diffusiongemma/index.md
@@ -58,8 +58,8 @@ default parameters for the Diffusion Sampling Settings:
 Access the experimental model weights (released under the Apache 2.0 license),
 allowing you to deploy it in your own projects and applications.
 
-[Learn more about DiffusionGemma architecture](https://ai.google.dev/gemma/docs/diffusiongemma/explained)
-[Try DiffusionGemma](https://ai.google.dev/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf)
+[Learn more about DiffusionGemma architecture](explained.md)
+[Try DiffusionGemma](inference.md)
 
 [Fine-tune DiffusionGemma](https://github.com/google/hackable_diffusion)
 [Deploy DiffusionGemma](https://docs.cloud.google.com/kubernetes-engine/docs/tutorials/serve-gemma-gpu-vllm)
diff --git a/docs/models/diffusiongemma/inference.md b/docs/models/diffusiongemma/inference.md
index e2f6204..0b69edc 100644
--- a/docs/models/diffusiongemma/inference.md
+++ b/docs/models/diffusiongemma/inference.md
@@ -580,4 +580,4 @@ print(text[0])
 
 ## Summary and next steps
 
-In this guide, you learned how to use DiffusionGemma for various tasks. Check out [Diffusion in Text Generation Explained](https://ai.google.dev/gemma/docs/diffusiongemma/explained) to understand how model works in detail.
+In this guide, you learned how to use DiffusionGemma for various tasks. Check out [Diffusion in Text Generation Explained](explained.md) to understand how model works in detail.
diff --git a/docs/models/diffusiongemma/model-card.md b/docs/models/diffusiongemma/model-card.md
index 9dac7b5..1f3e296 100644
--- a/docs/models/diffusiongemma/model-card.md
+++ b/docs/models/diffusiongemma/model-card.md
@@ -3,7 +3,7 @@
 [Hugging Face](https://huggingface.co/google/diffusiongemma-26B-A4B-it) \|
 [GitHub](https://github.com/google-gemma) \|
 [Launch Blog](https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/) \|
-[Documentation](https://ai.google.dev/gemma/docs/diffusiongemma)
+[Documentation](index.md)
 
 
 **License** : [Apache 2.0](https://ai.google.dev/gemma/apache_2) \| **Authors** : [Google DeepMind](https://deepmind.google/models/gemma/)
diff --git a/docs/models/embeddinggemma/fine-tuning.md b/docs/models/embeddinggemma/fine-tuning.md
index 3b3de2c..6268cba 100644
--- a/docs/models/embeddinggemma/fine-tuning.md
+++ b/docs/models/embeddinggemma/fine-tuning.md
@@ -102,7 +102,7 @@ A search for "tax-free investment" might have given the following results, with
 3. Document: Home Loan Application Guide (Score: 0.44)
 
 !!! Note
-    To generate optimal embeddings with EmbeddingGemma, you should add an "instructional prompt" or "task" to the beginning of your input text. You will use `STS` for sentence similarity. For details on all available EmbeddingGemma prompts, see the [model card](http://ai.google.dev/gemma/docs/embeddinggemma/model_card#prompt_instructions).
+    To generate optimal embeddings with EmbeddingGemma, you should add an "instructional prompt" or "task" to the beginning of your input text. You will use `STS` for sentence similarity. For details on all available EmbeddingGemma prompts, see the [model card](model-card.md#prompt-instructions).
     
 
 ```python
@@ -250,5 +250,5 @@ You have now learned how to adapt an EmbeddingGemma model for a specific domain
 Explore what more you can do with EmbeddingGemma:
 
 - [Training Overview](https://sbert.net/docs/sentence_transformer/training_overview.html) in Sentence Transformers Documentation
-- [Generate embeddings with Sentence Transformers](https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers)
+- [Generate embeddings with Sentence Transformers](inference.md)
 - [Simple RAG example](https://github.com/google-gemma/cookbook/blob/main/Gemma/%5BGemma_3%5DRAG_with_EmbeddingGemma.ipynb) in the Gemma Cookbook
diff --git a/docs/models/embeddinggemma/index.md b/docs/models/embeddinggemma/index.md
index 68bbb55..8ac3511 100644
--- a/docs/models/embeddinggemma/index.md
+++ b/docs/models/embeddinggemma/index.md
@@ -38,5 +38,5 @@ As with other Gemma models, EmbeddingGemma is provided with open weights and
 licensed for responsible [commercial use](https://ai.google.dev/gemma/terms), allowing you to
 fine tune and deploy it in your own projects and applications.
 
-[Try EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers)
-[Fine-tune EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers)
+[Try EmbeddingGemma](inference.md)
+[Fine-tune EmbeddingGemma](fine-tuning.md)
diff --git a/docs/models/embeddinggemma/inference.md b/docs/models/embeddinggemma/inference.md
index e52ad8a..5f390b1 100644
--- a/docs/models/embeddinggemma/inference.md
+++ b/docs/models/embeddinggemma/inference.md
@@ -257,7 +257,7 @@ For RAG systems, use the following `prompt_name` values to create specialized em
 
 #### Further Reading
 
-- For details on all available EmbeddingGemma prompts, see the [model card](http://ai.google.dev/gemma/docs/embeddinggemma/model_card#prompt_instructions).
+- For details on all available EmbeddingGemma prompts, see the [model card](model-card.md#prompt-instructions).
 - For general information on prompt templates, see the [Sentence Transformer documentation](https://sbert.net/examples/sentence_transformer/applications/computing-embeddings/README.html#prompt-templates).
 - For a demo of RAG, see the [Simple RAG example](https://github.com/google-gemma/cookbook/blob/main/Gemma/%5BGemma_3%5DRAG_with_EmbeddingGemma.ipynb) in the Gemma Cookbook.
 
@@ -382,5 +382,5 @@ You are now equipped to generate high-quality text embeddings using EmbeddingGem
 
 Check out the following docs next:
 
-- [Fine-tune EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers)
+- [Fine-tune EmbeddingGemma](fine-tuning.md)
 - [Simple RAG example](https://github.com/google-gemma/cookbook/blob/main/Gemma/%5BGemma_3%5DRAG_with_EmbeddingGemma.ipynb) in the Gemma Cookbook
diff --git a/docs/models/embeddinggemma/model-card.md b/docs/models/embeddinggemma/model-card.md
index 3a0d737..e9ce990 100644
--- a/docs/models/embeddinggemma/model-card.md
+++ b/docs/models/embeddinggemma/model-card.md
@@ -1,6 +1,6 @@
 # EmbeddingGemma model card
 
-**Model Page** : [EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma)
+**Model Page** : [EmbeddingGemma](index.md)
 
 **Resources and Technical Documentation**:
 
@@ -87,12 +87,12 @@ data:
 
 EmbeddingGemma was trained using the latest generation of
 [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e), for more details refer to
-the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3).
+the [Gemma 3 model card](../gemma-3/index.md).
 
 ### Software
 
 Training was done using [JAX](https://github.com/jax-ml/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/). For more details refer
-to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3).
+to the [Gemma 3 model card](../gemma-3/index.md).
 
 ## Evaluation
 
diff --git a/docs/models/functiongemma/fine-tuning.md b/docs/models/functiongemma/fine-tuning.md
index 02e5a06..6f73e64 100644
--- a/docs/models/functiongemma/fine-tuning.md
+++ b/docs/models/functiongemma/fine-tuning.md
@@ -10,7 +10,7 @@ Models, especially smaller ones, have fewer parameters available to retain compl
 Common use cases for fine-tuning tool calling include:
 
 - **Model Distillation**: Generating synthetic training data with a larger model and fine-tuning a smaller model to replicate the specific workflow efficiently.
-- **Handling Non-Standard Schemas** : Overcoming base model struggles with legacy, highly complex data structures or proprietary format not found in public data, such as handling [domain-specific mobile actions](https://ai.google.dev/gemma/docs/mobile-actions).
+- **Handling Non-Standard Schemas** : Overcoming base model struggles with legacy, highly complex data structures or proprietary format not found in public data, such as handling [domain-specific mobile actions](../../deployment/mobile-actions.md).
 - **Optimizing Context Usage**: "Baking" tool definitions into the model's weights. This allows you to use shorthand descriptions in your prompts, freeing up the context window for the actual conversation.
 - **Resolving Selection Ambiguity**: Biasing the model toward specific enterprise policies, such as prioritizing an internal knowledge base over an external search engine.
 
@@ -605,5 +605,5 @@ Now that you have a fine-tuned model, consider the following steps to move towar
 
 Check out the following docs next:
 
-- [Full function calling sequence with FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma)
+- [Full function calling sequence with FunctionGemma](full-sequence.md)
 - [Fine-tune FunctionGemma for Mobile Actions](https://github.com/google-gemma/cookbook/blob/main/FunctionGemma/%5BFunctionGemma%5DFinetune_FunctionGemma_270M_for_Mobile_Actions_with_Hugging_Face.ipynb) in the Gemma Cookbook
diff --git a/docs/models/functiongemma/formatting.md b/docs/models/functiongemma/formatting.md
index e51fe48..a807de3 100644
--- a/docs/models/functiongemma/formatting.md
+++ b/docs/models/functiongemma/formatting.md
@@ -11,7 +11,7 @@ results.
 
 ## Base Prompt Structure
 
-FunctionGemma builds on the [Gemma prompt structure](https://ai.google.dev/gemma/docs/core/prompt-structure), using
+FunctionGemma builds on the [Gemma prompt structure](../../get-started/prompt-structure.md), using
 `<start_of_turn>role` and `<end_of_turn>` to describe conversational turns.
 The `role` is typically `user` or `model` (and sometimes `developer` for
 providing initial context, as seen below).
@@ -112,7 +112,7 @@ does not make the semantic connection in that specific context.
 - **Mitigation Strategies:**
     - **Enriched Tool Definition:** This is often the most effective fix. Expanding the function's description to include semantic keywords helps the model bridge the gap. For example, adding "This function can be used to determine if the weather is hot or cold in a given location" to the docstring allows FunctionGemma to correctly map those qualitative descriptors to the tool.
     - **Prompt Engineering:** Making the user query more detailed or explicit can help the model trigger the correct tool, though this relies on user behavior.
-    - **Fine-tuning:** For production environments where users frequently use highly indirect language (e.g., "is it nice out?" versus "get weather"), we recommend [fine-tuning](https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma) the model on a dataset that explicitly maps these semantic nuances to the correct tool definitions.
+    - **Fine-tuning:** For production environments where users frequently use highly indirect language (e.g., "is it nice out?" versus "get weather"), we recommend [fine-tuning](fine-tuning.md) the model on a dataset that explicitly maps these semantic nuances to the correct tool definitions.
 
 **Example: Make Semantic Connection**
 > **User:** "Hey, is it cold in Paris right now?"
@@ -193,4 +193,4 @@ The current weather in Tokyo is sunny with a temperature of 15 degrees Celsius.<
 ```
 
 You can see more complete version of this example in the
-[Full function calling sequence with FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma)
+[Full function calling sequence with FunctionGemma](full-sequence.md)
diff --git a/docs/models/functiongemma/full-sequence.md b/docs/models/functiongemma/full-sequence.md
index 6aea934..94bea56 100644
--- a/docs/models/functiongemma/full-sequence.md
+++ b/docs/models/functiongemma/full-sequence.md
@@ -271,4 +271,4 @@ You have established how to build an application that can calls functions with F
 
 Check out the following documentation for further reading.
 
-- [Fine-tuning with FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma)
+- [Fine-tuning with FunctionGemma](fine-tuning.md)
diff --git a/docs/models/functiongemma/index.md b/docs/models/functiongemma/index.md
index 0c1b6b4..d0017ad 100644
--- a/docs/models/functiongemma/index.md
+++ b/docs/models/functiongemma/index.md
@@ -34,8 +34,8 @@ As with other Gemma models, FunctionGemma is provided with open weights and
 licensed for responsible [commercial use](https://ai.google.dev/gemma/terms), allowing you to
 fine tune and deploy it in your own projects and applications.
 
-[Formatting and best practices](https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices)
-[Try FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma/function-calling-with-hf)
+[Formatting and best practices](formatting.md)
+[Try FunctionGemma](inference.md)
 
-[Fine-tune FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma)
-[Fine-tune the Mobile Actions demo](https://ai.google.dev/gemma/docs/mobile-actions)
+[Fine-tune FunctionGemma](fine-tuning.md)
+[Fine-tune the Mobile Actions demo](../../deployment/mobile-actions.md)
diff --git a/docs/models/functiongemma/inference.md b/docs/models/functiongemma/inference.md
index 7c26c52..525a026 100644
--- a/docs/models/functiongemma/inference.md
+++ b/docs/models/functiongemma/inference.md
@@ -254,5 +254,5 @@ Now you understand function calling with FunctionGemma. Key takeaways from this
 
 Check out the following docs next:
 
-- [Full function calling sequence with FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma)
-- [Fine-tuning with FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma)
+- [Full function calling sequence with FunctionGemma](full-sequence.md)
+- [Fine-tuning with FunctionGemma](fine-tuning.md)
diff --git a/docs/models/functiongemma/model-card.md b/docs/models/functiongemma/model-card.md
index 462b412..6c16e7a 100644
--- a/docs/models/functiongemma/model-card.md
+++ b/docs/models/functiongemma/model-card.md
@@ -1,6 +1,6 @@
 # FunctionGemma model card
 
-**Model Page** : [FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma)
+**Model Page** : [FunctionGemma](index.md)
 
 **Resources and Technical Documentation**:
 
diff --git a/docs/models/gemma-1/index.md b/docs/models/gemma-1/index.md
index aa03841..4e06feb 100644
--- a/docs/models/gemma-1/index.md
+++ b/docs/models/gemma-1/index.md
@@ -1,6 +1,6 @@
 # Gemma model card
 
-**Model Page** : [Gemma](https://ai.google.dev/gemma/docs/core)
+**Model Page** : [Gemma](../gemma-4/index.md)
 
 **Resources and Technical Documentation**:
 
diff --git a/docs/models/gemma-2/index.md b/docs/models/gemma-2/index.md
index c92505d..e70ea46 100644
--- a/docs/models/gemma-2/index.md
+++ b/docs/models/gemma-2/index.md
@@ -1,6 +1,6 @@
 # Gemma 2 model card
 
-**Model Page** : [Gemma](https://ai.google.dev/gemma/docs/core)
+**Model Page** : [Gemma](../gemma-4/index.md)
 
 **Resources and Technical Documentation**:
 
diff --git a/docs/models/gemma-3/index.md b/docs/models/gemma-3/index.md
index 0d74008..a4afb92 100644
--- a/docs/models/gemma-3/index.md
+++ b/docs/models/gemma-3/index.md
@@ -1,6 +1,6 @@
 # Gemma 3 model card
 
-**Model Page** : [Gemma](https://ai.google.dev/gemma/docs/core)
+**Model Page** : [Gemma](../gemma-4/index.md)
 
 **Resources and Technical Documentation**:
 
diff --git a/docs/models/gemma-3n/index.md b/docs/models/gemma-3n/index.md
index f68ab1b..b116f30 100644
--- a/docs/models/gemma-3n/index.md
+++ b/docs/models/gemma-3n/index.md
@@ -11,12 +11,12 @@ handling, as well as text and visual data.
 
 Gemma 3n includes the following key features:
 
-- **Audio input** : Process sound data for speech recognition, translation, and audio data analysis. [Learn more](https://ai.google.dev/gemma/docs/core/huggingface_inference#audio)
-- **Visual and text input** : Multimodal capabilities let you handle vision, sound, and text to help you understand and analyze the world around you. [Learn more](https://ai.google.dev/gemma/docs/core/huggingface_inference#vision)
+- **Audio input** : Process sound data for speech recognition, translation, and audio data analysis. [Learn more](../../deployment/huggingface.md#generate-text-from-audio-data)
+- **Visual and text input** : Multimodal capabilities let you handle vision, sound, and text to help you understand and analyze the world around you. [Learn more](../../deployment/huggingface.md#generate-text-from-image-data)
 - **Vision encoder:** High-performance MobileNet-V5 encoder substantially improves speed and accuracy of processing visual data. [Learn more](https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/#mobilenet-v5:-new-state-of-the-art-vision-encoder)
-- **PLE caching** : Per-Layer Embedding (PLE) parameters contained in these models can be cached to fast, local storage to reduce model memory run costs. [Learn more](https://ai.google.dev/gemma/docs/gemma-3n#ple-caching)
-- **MatFormer architecture:** Matryoshka Transformer architecture allows for selective activation of the models parameters per request to reduce compute cost and response times. [Learn more](https://ai.google.dev/gemma/docs/gemma-3n#matformer)
-- **Conditional parameter loading:** Bypass loading of vision and audio parameters in the model to reduce the total number of loaded parameters and save memory resources. [Learn more](https://ai.google.dev/gemma/docs/gemma-3n#conditional-parameter)
+- **PLE caching** : Per-Layer Embedding (PLE) parameters contained in these models can be cached to fast, local storage to reduce model memory run costs. [Learn more](index.md#ple-caching)
+- **MatFormer architecture:** Matryoshka Transformer architecture allows for selective activation of the models parameters per request to reduce compute cost and response times. [Learn more](index.md#matformer-architecture)
+- **Conditional parameter loading:** Bypass loading of vision and audio parameters in the model to reduce the total number of loaded parameters and save memory resources. [Learn more](index.md#conditional-parameter-loading)
 - **Wide language support**: Wide linguistic capabilities, trained in over 140 languages.
 - **32K token context**: Substantial input context for analyzing data and handling processing tasks.
 
@@ -107,5 +107,5 @@ demanding tasks.
 
 
 Ready to start building?
-[Get started](https://ai.google.dev/gemma/docs/get_started)
+[Get started](../../get-started/quickstart.md)
 with Gemma models!
diff --git a/docs/models/gemma-3n/model-card.md b/docs/models/gemma-3n/model-card.md
index e883d81..c7af07b 100644
--- a/docs/models/gemma-3n/model-card.md
+++ b/docs/models/gemma-3n/model-card.md
@@ -1,6 +1,6 @@
 # Gemma 3n model card
 
-**Model Page** : [Gemma 3n](https://ai.google.dev/gemma/docs/gemma-3n)
+**Model Page** : [Gemma 3n](index.md)
 
 **Resources and Technical Documentation**:
 
@@ -32,7 +32,7 @@ requirements. This technique allows the models to operate at an effective size
 of 2B and 4B parameters, which is lower than the total number of parameters they
 contain. For more information on Gemma 3n's efficient parameter management
 technology, see the
-[Gemma 3n](https://ai.google.dev/gemma/docs/gemma-3n#parameters)
+[Gemma 3n](index.md#model-parameters-and-effective-parameters)
 page.
 
 ### Inputs and outputs
diff --git a/docs/models/gemma-4/index.md b/docs/models/gemma-4/index.md
index 31fb99b..00e9f1a 100644
--- a/docs/models/gemma-4/index.md
+++ b/docs/models/gemma-4/index.md
@@ -19,10 +19,10 @@ You can download Gemma 4 models from
 [Kaggle](https://www.kaggle.com/models?query=gemma-4&publisher=google) and
 [Hugging Face](https://huggingface.co/collections/google/gemma-4).
 For more technical details on Gemma 4, see the
-[Model Card](https://ai.google.dev/gemma/docs/core/model_card_4) and
+[Model Card](model-card.md) and
 [Technical Report](https://goo.gle/Gemma4Report).
 Earlier versions of Gemma core models are also available for download. For more
-information, see [Previous Gemma models](https://ai.google.dev/gemma/docs/core#previous-models).
+information, see [Previous Gemma models](index.md#previous-gemma-models).
 
 <div class="cookbook-grid">
 <a class="cookbook-card" href="https://www.kaggle.com/models?query=gemma-4&publisher=google" target="_blank" rel="noopener">
@@ -39,13 +39,13 @@ information, see [Previous Gemma models](https://ai.google.dev/gemma/docs/core#p
 ## Capabilities
 
 - **Reasoning:** All models in the family are designed as highly capable reasoners, with configurable [thinking
-  modes](https://ai.google.dev/gemma/docs/capabilities/thinking).
-- **Extended Multimodalities:** Processes Text, [Image](https://ai.google.dev/gemma/docs/capabilities/vision/image) with variable aspect ratio and resolution support (all models), [Video](https://ai.google.dev/gemma/docs/capabilities/vision/video), and [Audio](https://ai.google.dev/gemma/docs/capabilities/audio) (featured natively on the E2B, E4B and 12B models).
+  modes](../../get-started/capabilities/thinking.md).
+- **Extended Multimodalities:** Processes Text, [Image](../../get-started/capabilities/image.md) with variable aspect ratio and resolution support (all models), [Video](../../get-started/capabilities/video.md), and [Audio](../../get-started/capabilities/audio.md) (featured natively on the E2B, E4B and 12B models).
 - **Increased Context Window:** Small models feature a 128K context window, while the medium models support 256K.
 - **Enhanced Coding \& Agentic Capabilities:** Achieves notable improvements in coding benchmarks alongside built-in [function-calling
-  support](https://ai.google.dev/gemma/docs/capabilities/text/function-calling-gemma4), powering highly capable autonomous agents.
+  support](../../get-started/capabilities/function-calling.md), powering highly capable autonomous agents.
 - **Native System Prompt Support:** Gemma 4 introduces built-in support for the system role, enabling more structured and controllable conversations.
-- **[Multi-Token Prediction](https://ai.google.dev/gemma/docs/mtp/overview):** All Gemma 4 models (E2B, E4B, 12B, 31B, and 26B A4B) include a dedicated draft model for speculative decoding, enabling significantly faster inference with no quality loss.
+- **[Multi-Token Prediction](../../get-started/capabilities/mtp.md):** All Gemma 4 models (E2B, E4B, 12B, 31B, and 26B A4B) include a dedicated draft model for speculative decoding, enabling significantly faster inference with no quality loss.
 
 ## Parameter sizes and quantization
 
@@ -128,10 +128,10 @@ available from [Kaggle](https://www.kaggle.com/models?query=gemma) and
 For more technical details about previous Gemma models, see the following
 model card pages:
 
-- Gemma 3 [Model Card](https://ai.google.dev/gemma/docs/core/model_card_3)
-- Gemma 2 [Model Card](https://ai.google.dev/gemma/docs/core/model_card_2)
-- Gemma 1 [Model Card](https://ai.google.dev/gemma/docs/core/model_card)
+- Gemma 3 [Model Card](../gemma-3/index.md)
+- Gemma 2 [Model Card](../gemma-2/index.md)
+- Gemma 1 [Model Card](../gemma-1/index.md)
 
 Ready to start building?
-[Get started](https://ai.google.dev/gemma/docs/get_started)
+[Get started](../../get-started/quickstart.md)
 with Gemma models!
diff --git a/docs/models/gemma-4/model-card.md b/docs/models/gemma-4/model-card.md
index 559433c..8b64af8 100644
--- a/docs/models/gemma-4/model-card.md
+++ b/docs/models/gemma-4/model-card.md
@@ -4,7 +4,7 @@
 [Hugging Face](https://huggingface.co/collections/google/gemma-4) \|
 [GitHub](https://github.com/google-gemma) \|
 [Launch Blog](https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/) \|
-[Documentation](https://ai.google.dev/gemma/docs/core) \|
+[Documentation](index.md) \|
 [Technical Report](https://arxiv.org/abs/2607.02770)
 
 
diff --git a/docs/models/gemma-aps/index.md b/docs/models/gemma-aps/index.md
new file mode 100644
index 0000000..2f6964c
--- /dev/null
+++ b/docs/models/gemma-aps/index.md
@@ -0,0 +1,43 @@
+# Gemma for abstractive proposition segmentation (APS)
+
+## Gemma-APS
+
+
+Abstractive proposition segmentation (APS) is an analysis technique that breaks down text
+information into component parts. Gemma-APS is a variant of Gemma that takes a given
+passage of text and attempts to segment the content into the individual
+facts, statements, and ideas expressed in the text, and restates them in full sentences
+with small changes to the original text.
+
+
+This model can be used for research where there is a need to break down text content into
+meaningful components. Applications include generative content grounding, content
+retrieval, fact-checking, and evaluation of generative output, such as summarization,
+where it is useful to divide up individual factual data so that they can be verified
+independently. For more information, check out the
+[research paper](https://arxiv.org/pdf/2406.19803).
+
+-
+
+  #### Analyze content for component parts
+
+  Apply generative artificial intelligence (AI) to break down content into discrete statements for analysis or further processing.
+-
+
+  #### Combine with larger content analysis
+
+  Use generated content segments in larger content analysis projects such as grounding, retrieval, fact-checking, evaluation of generation tasks, and duplicate content detection.
+
+## Learn more
+
+### [View 2B model](https://huggingface.co/google/gemma-2b-aps-it)
+
+View more code, notebooks, information, and discussions about the Gemma-APS 2B model on Hugging Face.
+
+### [View 7B model](https://huggingface.co/google/gemma-7b-aps-it)
+
+View more code, notebooks, information, and discussions about the Gemma-APS 7B model on Hugging Face.
+
+### [Read the research](https://arxiv.org/pdf/2406.19803)
+
+Get more insights into the research, development, and operation of the Gemma-APS model.
diff --git a/docs/models/paligemma/fine-tuning.md b/docs/models/paligemma/fine-tuning.md
index 20d7396..c7731fb 100644
--- a/docs/models/paligemma/fine-tuning.md
+++ b/docs/models/paligemma/fine-tuning.md
@@ -1,7 +1,7 @@
 # Fine-tune PaliGemma with JAX and Flax
 
 
-This notebook shows how to fine-tune [PaliGemma](https://ai.google.dev/gemma/docs/paligemma) on a vision-language task with [JAX](https://jax.readthedocs.io/en/latest/index.html). *Fine-tuning* is a process that can improve your model's performance on specific tasks or help the model adhere to specific output requirements when instructions aren't sufficient and you have a set of examples that demonstrate the outputs you want. Gemma-based models like PaliGemma require fine-tuning to produce expected results.
+This notebook shows how to fine-tune [PaliGemma](index.md) on a vision-language task with [JAX](https://jax.readthedocs.io/en/latest/index.html). *Fine-tuning* is a process that can improve your model's performance on specific tasks or help the model adhere to specific output requirements when instructions aren't sufficient and you have a set of examples that demonstrate the outputs you want. Gemma-based models like PaliGemma require fine-tuning to produce expected results.
 
 ### What's in this notebook
 
diff --git a/docs/models/paligemma/index.md b/docs/models/paligemma/index.md
index d0ca596..f5c7249 100644
--- a/docs/models/paligemma/index.md
+++ b/docs/models/paligemma/index.md
@@ -16,7 +16,7 @@ object detection, and reading text embedded within images.
 PaliGemma 2 is available in 3B, 10B, and 28B parameter sizes, which are based on Gemma 2
 2B, 9B, and 27B models, respectively. The original PaliGemma models are available in the
 3B size. For more information on Gemma model variants, see the
-[Gemma models list](https://ai.google.dev/gemma/docs/get_started#models-list).
+[Gemma models list](../../get-started/quickstart.md#models-list).
 PaliGemma model variants support different pixel resolutions for image inputs, including
 224 x 224, 448 x 448, and 896 x 896 pixels.
 
@@ -58,11 +58,11 @@ Key benefits include:
 
 ## Learn more
 
-### [Run in Colab](http://ai.google.dev/gemma/docs/paligemma/inference-with-keras)
+### [Run in Colab](inference.md)
 
 Try detection and content generation capabilities with PaliGemma in Colab.
 
-### [Tune in Colab](http://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma)
+### [Tune in Colab](fine-tuning.md)
 
 Fine-tune a PaliGemma model with image data using JAX in Colab.
 
diff --git a/docs/models/paligemma/inference.md b/docs/models/paligemma/inference.md
index 58ff1ae..064e038 100644
--- a/docs/models/paligemma/inference.md
+++ b/docs/models/paligemma/inference.md
@@ -206,7 +206,7 @@ def display_segment_output(image, bounding_box, segment_mask, target_image_size)
 
 ## Generate output
 
-After loading the model and creating utility methods, you can prompt the model with image and text data to generate a responses. PaliGemma models are trained with specific prompt syntax for specific tasks, such as `answer`, `caption`, and `detect`. For more information about PaliGemma prompt task syntax, see [PaliGemma prompt and system instructions](https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions##prompt_task_syntax).
+After loading the model and creating utility methods, you can prompt the model with image and text data to generate a responses. PaliGemma models are trained with specific prompt syntax for specific tasks, such as `answer`, `caption`, and `detect`. For more information about PaliGemma prompt task syntax, see [PaliGemma prompt and system instructions](prompting.md#prompt-task-syntax).
 
 Prepare an image for use in a generation prompt by using the following code to load a test image into an object:
 
diff --git a/docs/models/paligemma/model-card-v1.md b/docs/models/paligemma/model-card-v1.md
index d6010dc..d565a29 100644
--- a/docs/models/paligemma/model-card-v1.md
+++ b/docs/models/paligemma/model-card-v1.md
@@ -1,6 +1,6 @@
 # PaliGemma 1 model card
 
-**Model page:** [PaliGemma](https://ai.google.dev/gemma/docs/paligemma)
+**Model page:** [PaliGemma](index.md)
 
 **Resources and technical documentation:**
 
@@ -177,7 +177,7 @@ models were evaluated against a number of different categories relevant to
 ethics and safety, including:
 
 - Human evaluation on prompts covering child safety, content safety and representational harms. See the [Gemma model
-  card](https://ai.google.dev/gemma/docs/core/model_card#evaluation_approach) for more details on evaluation approach, but with image captioning and visual question answering setups.
+  card](../gemma-1/index.md#evaluation-approach) for more details on evaluation approach, but with image captioning and visual question answering setups.
 - Image-to-Text benchmark evaluation: Benchmark against relevant academic datasets such as FairFace Dataset ([Karkkainen et al.,
   2021](https://arxiv.org/abs/1908.04913)).
 
diff --git a/docs/models/paligemma/model-card-v2.md b/docs/models/paligemma/model-card-v2.md
index 323a23d..b582d0c 100644
--- a/docs/models/paligemma/model-card-v2.md
+++ b/docs/models/paligemma/model-card-v2.md
@@ -1,6 +1,6 @@
 # PaliGemma 2 model card
 
-**Model page:** [PaliGemma](https://ai.google.dev/gemma/docs/paligemma)
+**Model page:** [PaliGemma](index.md)
 
 **Resources and technical documentation:**
 
@@ -32,7 +32,7 @@ PaliGemma 2 is the composition of a
 [Transformer decoder](https://arxiv.org/abs/1706.03762) and a
 [Vision Transformer image encoder](https://arxiv.org/abs/2010.11929).
 The text decoder is initialized from
-[Gemma 2](https://ai.google.dev/gemma/docs/core) in the 2B, 9B, and 27B
+[Gemma 2](../gemma-4/index.md) in the 2B, 9B, and 27B
 parameter sizes. The image encoder is initialized from
 [SigLIP-So400m/14](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/SigLIP_demo.ipynb).
 Similar to the original PaliGemma model, PaliGemma 2 is trained following the
@@ -68,7 +68,7 @@ PaliGemma 2 is pre-trained on the following mixture of datasets:
 
 PaliGemma 2 is based on Gemma 2, and you can find information on the
 pre-training datasets for Gemma 2 in the
-[Gemma 2 model card](https://ai.google.dev/gemma/docs/core/model_card_2).
+[Gemma 2 model card](../gemma-2/index.md).
 
 #### Data responsibility filtering
 
@@ -238,7 +238,7 @@ Our evaluation methods include structured ethics and safety evaluations across
 relevant content policies, including:
 
 - Human evaluation on prompts covering child safety, content safety and representational harms. See the [Gemma model
-  card](https://ai.google.dev/gemma/docs/core/model_card#evaluation_approach) for more details on evaluation approach, but with image captioning and visual question answering setups.
+  card](../gemma-1/index.md#evaluation-approach) for more details on evaluation approach, but with image captioning and visual question answering setups.
 - Image-to-Text benchmark evaluation: Benchmark against relevant academic datasets such as FairFace Dataset ([Karkkainen et al.,
   2021](https://arxiv.org/abs/1908.04913)).
 
diff --git a/docs/models/paligemma/prompting.md b/docs/models/paligemma/prompting.md
index 982f2c7..5999ffe 100644
--- a/docs/models/paligemma/prompting.md
+++ b/docs/models/paligemma/prompting.md
@@ -9,9 +9,9 @@ tasks.
 
 PaliGemma models use the same prompt formatting as the Gemma foundation models
 they are based on. However, PaliGemma models also support a special
-[task syntax](https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions#prompt_task_syntax), which is described in the next section. For
+[task syntax](prompting.md#prompt-task-syntax), which is described in the next section. For
 more information on Gemma prompt formatting, see
-[Gemma prompt and system instructions](https://ai.google.dev/gemma/docs/core/prompt-structure).
+[Gemma prompt and system instructions](../../get-started/prompt-structure.md).
 
 !!! Note
     although the pre-trained checkpoints support this prompt structure, they require fine-tuning to your specific use cases.
@@ -50,11 +50,11 @@ can find the list of supported languages on
 [GitHub](https://github.com/google/crossmodal-3600/blob/main/web-data/README.md).
 
 !!! Important
-    The [PaliGemma FT](https://ai.google.dev/gemma/docs/paligemma#categories) category of models are trained for specific tasks and *only* support the syntax for the target task.
+    The [PaliGemma FT](index.md#categories) category of models are trained for specific tasks and *only* support the syntax for the target task.
 
 
 For a detailed code examples showing how to use this syntax, see the
-[Generate PaliGemma output with Keras](https://ai.google.dev/gemma/docs/paligemma/inference-with-keras)
+[Generate PaliGemma output with Keras](inference.md)
 tutorial.
 
 ### Prompting with natural language
@@ -93,5 +93,5 @@ for output in outputs:
 ## System instructions
 
 The PaliGemma models don't support any additional system instructions beyond
-the [Gemma system instructions](https://ai.google.dev/gemma/docs/core/prompt-structure#system-instructions)
+the [Gemma system instructions](../../get-started/prompt-structure.md#system-instructions)
 from the foundation models they are based on.
diff --git a/docs/models/recurrentgemma/fine-tuning.md b/docs/models/recurrentgemma/fine-tuning.md
index c2569bf..d1461f2 100644
--- a/docs/models/recurrentgemma/fine-tuning.md
+++ b/docs/models/recurrentgemma/fine-tuning.md
@@ -1,7 +1,7 @@
 # Fine-tuning RecurrentGemma using JAX and Flax
 
 
-This tutorial demonstrates how to fine-tune the [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma) 2B Instruct model for an English-French translation task using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma), [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), [Optax](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), and the [MTNT (Machine Translation of Noisy Text) dataset](https://arxiv.org/abs/1809.00388). Although Flax is not used directly in this notebook, Flax was used to create Gemma.
+This tutorial demonstrates how to fine-tune the [RecurrentGemma](index.md) 2B Instruct model for an English-French translation task using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma), [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), [Optax](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), and the [MTNT (Machine Translation of Noisy Text) dataset](https://arxiv.org/abs/1809.00388). Although Flax is not used directly in this notebook, Flax was used to create Gemma.
 
 The `recurrentgemma` library was written with JAX, Flax, [Orbax](https://orbax.readthedocs.io/) (a JAX-based library for training utilities like checkpointing), and [SentencePiece](https://github.com/google/sentencepiece) (a tokenizer/detokenizer library).
 
@@ -13,7 +13,7 @@ The following sections explain the steps for preparing a notebook to use a Recur
 
 ### Set up Kaggle access for Gemma
 
-To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](https://ai.google.dev/gemma/docs/setup) with a few exceptions:
+To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](../../get-started/setup.md) with a few exceptions:
 
 - Get access to RecurrentGemma (instead of Gemma) on [kaggle.com](https://www.kaggle.com/models/google/recurrentgemma).
 - Select a Colab runtime with sufficient resources to run the RecurrentGemma model.
diff --git a/docs/models/recurrentgemma/index.md b/docs/models/recurrentgemma/index.md
index c92aad9..2aad277 100644
--- a/docs/models/recurrentgemma/index.md
+++ b/docs/models/recurrentgemma/index.md
@@ -31,7 +31,7 @@ advantages:
 
 ## More resources
 
-### [View the model card](http://ai.google.dev/gemma/docs/recurrentgemma/model_card)
+### [View the model card](model-card.md)
 
 RecurrentGemma's model card contains detailed information about the model, implementation information, evaluation information, model usage and limitations, and more.
 
diff --git a/docs/models/recurrentgemma/inference.md b/docs/models/recurrentgemma/inference.md
index 4e41f13..0d44995 100644
--- a/docs/models/recurrentgemma/inference.md
+++ b/docs/models/recurrentgemma/inference.md
@@ -1,7 +1,7 @@
 # Inference with RecurrentGemma using JAX and Flax
 
 
-This tutorial demonstrates how to perform basic sampling/inference with the [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma) 2B Instruct model using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma) that was written with [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Orbax](https://orbax.readthedocs.io/) (a JAX-based library for training utilities like checkpointing), and [SentencePiece](https://github.com/google/sentencepiece) (a tokenizer/detokenizer library). Although Flax is not used directly in this notebook, Flax was used to create Gemma and RecurrentGemma (the Griffin model).
+This tutorial demonstrates how to perform basic sampling/inference with the [RecurrentGemma](index.md) 2B Instruct model using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma) that was written with [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Orbax](https://orbax.readthedocs.io/) (a JAX-based library for training utilities like checkpointing), and [SentencePiece](https://github.com/google/sentencepiece) (a tokenizer/detokenizer library). Although Flax is not used directly in this notebook, Flax was used to create Gemma and RecurrentGemma (the Griffin model).
 
 This notebook can run on Google Colab with the T4 GPU (go to **Edit** \> **Notebook settings** \> Under **Hardware accelerator** select **T4 GPU**).
 
@@ -11,7 +11,7 @@ The following sections explain the steps for preparing a notebook to use a Recur
 
 ### Set up Kaggle access for Gemma
 
-To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](https://ai.google.dev/gemma/docs/setup) with a few exceptions:
+To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](../../get-started/setup.md) with a few exceptions:
 
 - Get access to RecurrentGemma (instead of Gemma) on [kaggle.com](https://www.kaggle.com/models/google/recurrentgemma).
 - Select a Colab runtime with sufficient resources to run the RecurrentGemma model.
diff --git a/docs/models/recurrentgemma/model-card.md b/docs/models/recurrentgemma/model-card.md
index 0060775..17eaf5c 100644
--- a/docs/models/recurrentgemma/model-card.md
+++ b/docs/models/recurrentgemma/model-card.md
@@ -1,6 +1,6 @@
 # RecurrentGemma model card
 
-**Model page:** [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma)
+**Model page:** [RecurrentGemma](index.md)
 
 **Resources and technical documentation:**
 
@@ -50,14 +50,14 @@ Gemma and achieves faster inference when generating long sequences.
 
 RecurrentGemma uses the same training data and data processing as used by the
 Gemma model family. A full description can be found on the [Gemma model
-card](https://ai.google.dev/gemma/docs/core/model_card#model_data).
+card](../gemma-1/index.md#model-data).
 
 ## Implementation information
 
 ### Hardware and frameworks used during training
 
 Like
-[Gemma](https://ai.google.dev/gemma/docs/core/model_card#implementation_information),
+[Gemma](../gemma-1/index.md#implementation-information),
 RecurrentGemma was trained on
 [TPUv5e](https://cloud.google.com/tpu/docs/intro-to-tpu?_gl=1*18wi411*_ga*MzE3NDU5OTY1LjE2MzQwNDA4NDY.*_ga_WH2QY8WWF5*MTcxMTA0MjUxMy4xNy4wLjE3MTEwNDI1MTkuMC4wLjA.&_ga=2.239449409.-317459965.1634040846),
 using [JAX](https://github.com/jax-ml/jax) and [ML
diff --git a/docs/models/releases.md b/docs/models/releases.md
index 01255cb..b330c57 100644
--- a/docs/models/releases.md
+++ b/docs/models/releases.md
@@ -2,15 +2,15 @@ This page documents releases for the Gemma family of models.
 
 ## June 3, 2026
 
-- Release of [Gemma 4 12B Unified](https://ai.google.dev/gemma/docs/core).
+- Release of [Gemma 4 12B Unified](gemma-4/index.md).
 
 ## April 16, 2026
 
-- Release of [Gemma 4 - MTP](https://ai.google.dev/gemma/docs/mtp/overview) for E2B, E4B, 31B, and 26B A4B.
+- Release of [Gemma 4 - MTP](../get-started/capabilities/mtp.md) for E2B, E4B, 31B, and 26B A4B.
 
 ## March 31, 2026
 
-- Release of [Gemma 4](https://ai.google.dev/gemma/docs/core) in E2B, E4B, 31B and 26B A4B sizes.
+- Release of [Gemma 4](gemma-4/index.md) in E2B, E4B, 31B and 26B A4B sizes.
 
 ## January 15, 2026
 
@@ -22,11 +22,11 @@ This page documents releases for the Gemma family of models.
 
 ## December 19, 2025
 
-- Release of [Gemma Scope 2](https://ai.google.dev/gemma/docs/gemma_scope), an interpretability suite for Gemma 3 models.
+- Release of [Gemma Scope 2](gemma-scope/index.md), an interpretability suite for Gemma 3 models.
 
 ## December 18, 2025
 
-- Release of [FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma) in 270M parameter size.
+- Release of [FunctionGemma](functiongemma/index.md) in 270M parameter size.
 - Release of [T5Gemma v2](https://blog.google/technology/developers/t5gemma-2/) in 270M-270M, 1B-1B, and 4B-4B parameter sizes.
 
 ## September 13, 2025
@@ -35,11 +35,11 @@ This page documents releases for the Gemma family of models.
 
 ## September 4, 2025
 
-- Release of [EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma) in 308M parameter size.
+- Release of [EmbeddingGemma](embeddinggemma/index.md) in 308M parameter size.
 
 ## August 14, 2025
 
-- Release of [Gemma 3](https://ai.google.dev/gemma/docs/core/model_card_3) in 270M size.
+- Release of [Gemma 3](gemma-3/index.md) in 270M size.
 
 ## July 9, 2025
 
@@ -48,7 +48,7 @@ This page documents releases for the Gemma family of models.
 
 ## June 26, 2025
 
-- Release of [Gemma 3n](https://ai.google.dev/gemma/docs/3n) in E2B and E4B sizes.
+- Release of [Gemma 3n](gemma-3n/index.md) in E2B and E4B sizes.
 
 ## May 20, 2025
 
@@ -56,69 +56,69 @@ This page documents releases for the Gemma family of models.
 
 ## March 10, 2025
 
-- Release of [Gemma 3](https://ai.google.dev/gemma/docs/core/model_card_3) in 1B, 4B, 12B and 27B sizes.
-- Release of [ShieldGemma 2](https://ai.google.dev/gemma/docs/shieldgemma).
+- Release of [Gemma 3](gemma-3/index.md) in 1B, 4B, 12B and 27B sizes.
+- Release of [ShieldGemma 2](shieldgemma/index.md).
 
 ## February 19, 2025
 
-- Release of [PaliGemma 2 mix](https://ai.google.dev/gemma/docs/paligemma/model-card-2) in 3B, 10B, and 28B parameter sizes.
+- Release of [PaliGemma 2 mix](paligemma/model-card-v2.md) in 3B, 10B, and 28B parameter sizes.
 
 ## December 5, 2024
 
-- Release of [PaliGemma 2](https://ai.google.dev/gemma/docs/paligemma) in 3B, 10B, and 28B parameter sizes.
+- Release of [PaliGemma 2](paligemma/index.md) in 3B, 10B, and 28B parameter sizes.
 
 ## October 16, 2024
 
-- Release of [Personal AI code assistant](https://ai.google.dev/gemma/docs/personal-code-assistant) developer guide.
+- Release of [Personal AI code assistant](../learn/personal-code-assistant.md) developer guide.
 
 ## October 15, 2024
 
-- Release of [Gemma-APS](https://ai.google.dev/gemma/docs/gemma-aps) in 2B and 7B sizes.
+- Release of [Gemma-APS](gemma-aps/index.md) in 2B and 7B sizes.
 
 ## October 8, 2024
 
-- Release of [Business email assistant](https://ai.google.dev/gemma/docs/business-email-assistant) developer guide.
+- Release of [Business email assistant](../learn/business-email-assistant.md) developer guide.
 
 ## October 3, 2024
 
 - Release of [Gemma 2 JPN](https://www.kaggle.com/models/google/gemma-2-2b-jpn-it) in 2B size.
-- Release of [Spoken language tasks](https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning) developer guide.
+- Release of [Spoken language tasks](../tuning/spoken-language.md) developer guide.
 
 ## September 12, 2024
 
-- Release of [DataGemma](https://ai.google.dev/gemma/docs/datagemma) in 2B size.
+- Release of [DataGemma](datagemma/index.md) in 2B size.
 
 ## July 31, 2024
 
-- Release of [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2) in 2B size.
-- Initial release of [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma).
-- Initial release of [Gemma Scope](https://ai.google.dev/gemma/docs/gemma_scope).
+- Release of [Gemma 2](gemma-2/index.md) in 2B size.
+- Initial release of [ShieldGemma](shieldgemma/index.md).
+- Initial release of [Gemma Scope](gemma-scope/index.md).
 
 ## June 27, 2024
 
-- Initial release of [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2) in 9B and 27B sizes.
+- Initial release of [Gemma 2](gemma-2/index.md) in 9B and 27B sizes.
 
 ## June 11, 2024
 
-- Release of [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma) 9B variant.
+- Release of [RecurrentGemma](recurrentgemma/index.md) 9B variant.
 
 ## May 14, 2024
 
-- Initial release of [PaliGemma](https://ai.google.dev/gemma/docs/paligemma).
+- Initial release of [PaliGemma](paligemma/index.md).
 
 ## May 3, 2024
 
-- Release of [CodeGemma](https://ai.google.dev/gemma/docs/codegemma) v1.1.
+- Release of CodeGemma v1.1.
 
 ## April 9, 2024
 
-- Initial release of [CodeGemma](https://ai.google.dev/gemma/docs/codegemma).
-- Initial release of [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma).
+- Initial release of CodeGemma.
+- Initial release of [RecurrentGemma](recurrentgemma/index.md).
 
 ## April 5, 2024
 
-- Release of [Gemma](https://ai.google.dev/gemma/docs/core/model_card) 1.1.
+- Release of [Gemma](gemma-1/index.md) 1.1.
 
 ## February 21, 2024
 
-- Initial release of [Gemma](https://ai.google.dev/gemma/docs/core/model_card) in 2B and 7B sizes.
+- Initial release of [Gemma](gemma-1/index.md) in 2B and 7B sizes.
diff --git a/docs/models/shieldgemma/index.md b/docs/models/shieldgemma/index.md
index f23de23..6252291 100644
--- a/docs/models/shieldgemma/index.md
+++ b/docs/models/shieldgemma/index.md
@@ -12,7 +12,7 @@ for your specific use case.
 
 
 ShieldGemma 2 is a 4B parameter model built to label images for safety.
-ShieldGemma 1 is built on [Gemma 2](https://ai.google.dev/gemma/docs/core) in 2B, 9B, and 27B parameter sizes.
+ShieldGemma 1 is built on [Gemma 2](../gemma-4/index.md) in 2B, 9B, and 27B parameter sizes.
 
 
 -
@@ -28,7 +28,7 @@ ShieldGemma 1 is built on [Gemma 2](https://ai.google.dev/gemma/docs/core) in 2B
 
 ## ShieldGemma 2
 
-### [View the model card](http://ai.google.dev/gemma/docs/shieldgemma/model_card_2)
+### [View the model card](model-card-v2.md)
 
 ShieldGemma's 2 model card contains detailed information about the model implementation, evaluations, model usage and limitations, and more.
 
@@ -42,7 +42,7 @@ Run a working example for using ShieldGemma to evaluate images.
 
 ## ShieldGemma 1
 
-### [View the model card](http://ai.google.dev/gemma/docs/shieldgemma/model_card)
+### [View the model card](model-card-v1.md)
 
 ShieldGemma's model card contains detailed information about the model implementation, evaluations, model usage and limitations, and more.
 
diff --git a/docs/models/shieldgemma/model-card-v1.md b/docs/models/shieldgemma/model-card-v1.md
index 2cb4570..7c6b9cd 100644
--- a/docs/models/shieldgemma/model-card-v1.md
+++ b/docs/models/shieldgemma/model-card-v1.md
@@ -1,6 +1,6 @@
 # ShieldGemma model card
 
-**Model Page** : [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma)
+**Model Page** : [ShieldGemma](index.md)
 
 **Resources and Technical Documentation**:
 
@@ -118,12 +118,12 @@ and publicly available datasets. More details can be found in the
 
 ShieldGemma was trained using the latest generation of
 [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e), for more details refer to
-the [Gemma 2 model card](https://ai.google.dev/gemma/docs/core/model_card_2).
+the [Gemma 2 model card](../gemma-2/index.md).
 
 ### Software
 
 Training was done using [JAX](https://github.com/jax-ml/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/). For more
-details refer to the [Gemma 2 model card](https://ai.google.dev/gemma/docs/core/model_card_2).
+details refer to the [Gemma 2 model card](../gemma-2/index.md).
 
 ## Evaluation
 
@@ -174,7 +174,7 @@ applications as part of the Gemma ecosystem.
 ### Limitations
 
 All the usual limitations for large language models apply, see the
-[Gemma 2 model card](https://ai.google.dev/gemma/docs/core/model_card_2) for more details. Additionally,
+[Gemma 2 model card](../gemma-2/index.md) for more details. Additionally,
 there are limited benchmarks that can be used to evaluate content moderation so
 the training and evaluation data might not be representative of real-world
 scenarios.
@@ -192,7 +192,7 @@ The development of large language models (LLMs) raises several ethical concerns.
 We have carefully considered multiple aspects in the development of these
 models.
 
-Refer to the [Gemma model card](https://ai.google.dev/gemma/docs/core/model_card_2) for more details.
+Refer to the [Gemma model card](../gemma-2/index.md) for more details.
 
 ### Benefits
 
diff --git a/docs/models/shieldgemma/model-card-v2.md b/docs/models/shieldgemma/model-card-v2.md
index 1d884a0..e548d38 100644
--- a/docs/models/shieldgemma/model-card-v2.md
+++ b/docs/models/shieldgemma/model-card-v2.md
@@ -1,6 +1,6 @@
 # ShieldGemma model card
 
-**Model Page** : [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma)
+**Model Page** : [ShieldGemma](index.md)
 
 **Resources and Technical Documentation**:
 
@@ -25,7 +25,7 @@ the safety of both synthetic and natural images against key categories to help
 you build robust datasets and models. With this addition to the Gemma family of
 models, researchers and developers can now minimize the risk of harmful content
 affecting their Generative-AI powered applications across key
-[harm types](https://ai.google.dev/gemma/docs/shieldgemma/model_card_2#harm-types).
+[harm types](model-card-v2.md#harm-types).
 
 We recommend using ShieldGemma 2 as an input filter to vision language models,
 or as an output filter of image generation systems. To train a robust image
@@ -106,12 +106,12 @@ in the data preparation process to ensure the exclusion of illegal content.
 
 ShieldGemma 2 was trained using the latest generation of
 [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e), for more details refer to
-the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3).
+the [Gemma 3 model card](../gemma-3/index.md).
 
 ### Software
 
 Training was done using [JAX](https://github.com/jax-ml/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/). For more
-details refer to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3).
+details refer to the [Gemma 3 model card](../gemma-3/index.md).
 
 ## Evaluation
 
@@ -167,7 +167,7 @@ applications as part of the Gemma ecosystem.
 ### Limitations
 
 All the usual limitations for large language models apply, see the
-[Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3) for more details. Additionally,
+[Gemma 3 model card](../gemma-3/index.md) for more details. Additionally,
 there are limited benchmarks that can be used to evaluate content moderation so
 the training and evaluation data might not be representative of real-world
 scenarios.
@@ -185,7 +185,7 @@ The development of large language models (LLMs) raises several ethical concerns.
 We have carefully considered multiple aspects in the development of these
 models.
 
-Refer to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3) for more details.
+Refer to the [Gemma 3 model card](../gemma-3/index.md) for more details.
 
 ### Benefits
 
diff --git a/docs/tuning/spoken-language.md b/docs/tuning/spoken-language.md
new file mode 100644
index 0000000..c8a9b15
--- /dev/null
+++ b/docs/tuning/spoken-language.md
@@ -0,0 +1,491 @@
+# Tasks in spoken languages with Gemma
+
+[Video](https://www.youtube.com/watch?v=M4HGJehH4r0)
+
+Using artificial intelligence (AI) technology in a specific spoken language is a
+critical need for many businesses to be able to use it effectively. The Gemma
+family of models have some multilingual capabilities, but using it in languages
+other than English frequently produces less than ideal results.
+
+Fortunately, you don't need to teach Gemma an entire spoken language to be able
+to complete tasks in that language. What's more, you can tune Gemma models to
+complete specific tasks in a language with much less data and effort than you
+might think. Using about 20 examples of requests and expected responses *in
+your target language* you can get Gemma to help you solve many different
+business problems in the language that best serves you and your customers.
+
+For a video overview of the project and how to extend it, including insights
+from the folks who build it, check out the
+[Spoken Language AI Assistant](https://youtu.be/M4HGJehH4r0)
+Build with Google AI video. You can also review the code for this project in the
+[Gemma Cookbook code repository](https://github.com/google-gemini/gemma-cookbook/tree/main/Demos/spoken-language-tasks).
+Otherwise, you can get started extending the project using the following
+instructions.
+
+## Overview
+
+This tutorial walks you through setting up, running, and extending a spoken
+language task application built with Gemma and Python. The application provides
+a basic web user interface that you can modify to fit your needs. The
+application is built to generate replies to customer emails for a fictitious
+Korean bakery, and all the language input and output is handled entirely in
+Korean. You can use this application pattern with any language and any business
+task that uses text input and text output.
+
+![Screenshot of K-Mail web app user interface](../assets/tuning/k-mail-webapp.png)
+
+**Figure 1.** Project user interface, for Korean bakery email inquiries
+
+## Hardware requirements
+
+Run this tuning process on a computer with a graphics processing
+unit (GPU) or a Tensor processing unit (TPU), and sufficient
+memory to hold the existing model, plus the tuning data. To run the tuning
+configuration in this project, you need about 16GB of GPU memory, about
+the same amount of regular RAM, and a minimum of 50GB of disk space.
+
+You can run the Gemma model tuning portion of this tutorial using a Colab
+environment with a **T4 GPU** runtime. If you are building this project on a
+Google Cloud
+[VM instance](https://cloud.google.com/compute/docs/instances),
+configure the instance following these requirements:
+
+- **GPU hardware**: A NVIDIA T4 is required to run this project, and a NVIDIA L4 or higher is recommended.
+- **Operating System** : Select a **Deep Learning on Linux** option, specifically the **Deep Learning VM with CUDA 12.3 M124** with pre-installed GPU software drivers.
+- **Boot disk size** : Provision at least **50GB** of disk space for your data, models, and supporting software.
+
+## Project setup
+
+These instructions walk you through getting this project ready for development
+and testing. The general setup steps include installing prerequisite software,
+cloning the project from the code repository, setting a few environment
+variables, installing Python libraries, and testing the web application.
+
+### Install and configure
+
+This project uses Python 3 and Virtual Environments (`venv`) to manage packages
+and run the application. The following installation instructions are for a Linux
+host machine.
+
+To install the required software:
+
+- Install Python 3 and the `venv` virtual environment package for Python.
+
+```bash
+  sudo apt update
+  sudo apt install git pip python3-venv
+```
+
+#### Clone the project
+
+Download the project code to your development computer. You need
+[git](https://git-scm.com/) source control software to retrieve the
+project source code.
+
+To download the project code:
+
+1. Clone the git repository using the following command.
+
+```bash
+   git clone https://github.com/google-gemini/gemma-cookbook.git
+```
+
+2. Optionally, configure your local git repository to use sparse checkout,
+   so you have only the files for the project.
+
+```bash
+   cd gemma-cookbook/
+   git sparse-checkout set Demos/spoken-language-tasks/
+   git sparse-checkout init --cone
+```
+
+#### Install Python libraries
+
+Install the Python libraries with the `venv` Python virtual environment
+activated to manage Python packages and dependencies. Make sure you activate the
+Python virtual environment *before* installing Python libraries with the `pip`
+installer. For more information about using Python virtual environments, see the
+[Python venv](https://docs.python.org/3/library/venv.html) documentation.
+
+To install the Python libraries:
+
+1. In a terminal window, navigate to the `spoken-language-tasks` directory:
+
+```bash
+   cd Demos/spoken-language-tasks/
+```
+
+2. Configure and activate Python virtual environment (venv) for this project:
+
+```text
+   python3 -m venv venv
+   source venv/bin/activate
+```
+
+3. Install the required Python libraries for this project using the
+   `setup_python` script.
+
+```text
+   ./setup_python.sh
+```
+
+!!! Tip
+    On Linux operating systems, you may need to allow execution of the bash script by running the command `chmod +x setup_python.sh`.
+
+
+#### Set environment variables
+
+Set a few environment variables that are required to allow this code project to
+run, including a Kaggle username and Kaggle token key. You must have a Kaggle
+account and request access to the Gemma models to be able to download them. For
+this project, you add your Kaggle Username and Kaggle Token Key to two `.env`
+files, which are read by the web application and the tuning program,
+respectively.
+
+!!! Caution
+    Treat your Kaggle Token Key like a password and protect it appropriately. Don't embed your key in publicly published code.
+
+
+To set the environment variables:
+
+1. Obtain your Kaggle username and your token key by following the instructions in the [Kaggle documentation](https://www.kaggle.com/docs/api#authentication).
+2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
+3. Create environment variable files for the project, by creating a `.env` text file at *each* these locations in your clone of the project:
+
+``` { .no-copy .output }
+   k-mail-replier/k_mail_replier/.env
+   k-gemma-it/.env
+```
+4. After creating the `.env` text files, add the following settings to **both**
+   files:
+
+```python
+   KAGGLE_USERNAME=<YOUR_KAGGLE_USERNAME_HERE>
+   KAGGLE_KEY=<YOUR_KAGGLE_KEY_HERE>
+```
+
+### Run and test the application
+
+Once you have completed the installation and configuration of the project, run
+the web application to confirm that you have configured it correctly. You should
+do this as a baseline check before editing the project for your own use.
+
+To run and test the project:
+
+1. In a terminal window, navigate to the `/k_mail_replier/` directory:
+
+```bash
+   cd spoken-language-tasks/k-mail-replier/
+```
+
+2. Run the application using the `run_flask_app.sh` script:
+
+```text
+   ./run_flask_app.sh
+```
+
+3. After starting the web application, the program code lists a URL where
+   you can browse and test. Typically, this address is:
+
+```text
+   http://127.0.0.1:5000/
+```
+
+4. In the web interface, press the **답변 작성** button below the first input
+   field to generate a response from the model.
+
+The first response from the model after you run the application takes longer
+since it must complete initialization steps on the first generation run.
+Subsequent prompt requests and generation on an already-running web application
+complete in less time.
+
+## Extend the application
+
+Once you have the application running, you can extend it by modifying the user
+interface and business logic to make it work for tasks that are relevant to you
+or your business. You can also modify the behavior of the Gemma model using the
+application code by changing the components of the prompt that the app sends to
+the generative AI model.
+
+The application provides instructions to the model along with the input data
+from the user a complete prompt of the model. You can modify these instructions
+to change the behavior of the model, such as specifying that the model should
+extract information from the request and put it in structured data format, such
+as JSON. A simpler way to change the behavior of the model is to provide additional
+instructions or guidance for the model's response, such as specifying that the
+generated replies should be written in a polite tone.
+
+To modify prompt instructions:
+
+1. In the spoken-language-task project, open the `k-mail-replier/k_mail_replier/app.py` code file.
+2. In the `app.py` code, add additions instructions to the
+   `get_prompt():` function:
+
+```python
+   def get_prompt():
+       return "발신자에게 요청에 대한 감사를 전하고, 곧 자세한 내용을
+           알려드리겠다고 정중하게 답장해 주세요. 정중하게 답변해 주세요!:\n"
+```
+
+This example adds the phrase "Please write a polite response!" to the
+instructions in Korean.
+
+Providing additional prompt instruction can strongly influence the generated
+output, and takes significantly less effort to implement. You should try this
+method first to see if you can get the behavior you want from the model.
+However, using prompt instructions to modify a Gemma model's behavior has its
+limits. In particular, the overall input token limit of the model, which is
+8,192 tokens for Gemma 2, requires you to balance detailed prompt instructions
+with the size of the new data you provide so that you stay under that limit.
+
+Furthermore, when you want Gemma to perform tasks in a language other than
+English, simply prompting the base model is not likely to produce reliably
+useful results. Instead, you should tune the model with examples in the target
+language, and *then* consider modifying the prompt instructions to make smaller
+adjustments to the output of the tuned model.
+
+## Tune the model
+
+Performing fine-tuning of a Gemma model is the recommended way to get it to
+respond effectively in a spoken language other than English. However, you don't
+need to aim for full fluency in your target language for the model to be able to
+complete tasks in that language. You can achieve basic functionality in your
+target language for a task with around 20 examples. This section of the tutorial
+explains how to set up and run fine-tuning on a Gemma model for a specific task,
+in a specific language.
+
+The following directions explain how to perform the fine-tuning operation on a
+VM environment, however, you can also perform this tuning operation using the
+associated
+[Colab noteboook](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/Demos/spoken-language-tasks/k-gemma-it/spoken_language_tasks_with_gemma.ipynb)
+for this project.
+
+#### Hardware requirements
+
+The compute requirements for fine-tuning are the same as the
+[hardware requirements](spoken-language.md#hardware-requirements) for rest of the project. You can
+run the tuning operation in a Colab environment with a **T4 GPU** runtime *if*
+you limit the input tokens to 256 and the batch size to 1.
+
+### Prepare data
+
+Before you start tuning a Gemma model, you must prepare data for tuning. When
+you are tuning a model for a specific task and language, you need a set of
+request and response examples in that target language. These
+examples should show the request text, *without any instructions*, and the
+expected response text. To start with, you should prepare a dataset with about
+20 examples. These examples should represent a full variety of requests and the
+ideal responses. Make sure that the requests and responses are not repetitive,
+as that can cause the models responses to be repetitive and not adjust
+appropriately to variations in requests. The following table shows a few sample
+records from the dataset of this code example:
+
+| Request | Response |
+|---|---|
+| 안녕하세요, 2주 뒤에 있을 아이 생일을 위해 3호 케이크 3개를 주문하고 싶은데 가능할까요? | 고객님, 안녕하세요. 2주 뒤 아이 생일을 위한 3호 케이크 2개 주문 문의 감사합니다. 네, 3호 케이크 2개 주문 가능합니다. 아이 생일 케이크인 만큼 더욱 신경 써서 정성껏 준비하겠습니다. 혹시 원하시는 디자인이나 특별한 요청 사항이 있으시면 편하게 말씀해주세요. 픽업 날짜와 시간을 알려주시면 더욱 자세한 안내를 도와드리겠습니다. 다시 한번 문의 감사드리며, 아이 생일 진심으로 축하합니다! \[가게 이름\] 드림 |
+| 안녕하세요, 9월 15일에 있을 아들의 돌잔치를 위해 케이크를 주문하고 싶습니다. - 케이크 종류: 생크림 케이크 - 크기: 2호 - 디자인: 아기자기한 동물 디자인 - 문구: "첫 생일 축하해, 사랑하는 아들!" - 픽업 날짜 및 시간: 9월 14일 오후 3시 가격 및 주문 가능 여부를 알려주시면 감사하겠습니다. 감사합니다. 김민지 드림 | 안녕하세요, 김민지 님, 9월 15일 아드님의 돌잔치를 위한 케이크 주문 문의 감사합니다. - 생크림 케이크 2호, 아기자기한 동물 디자인, "첫 생일 축하해, 사랑하는 아들!" 문구, 9월 14일 오후 3시 픽업 모두 가능합니다. - 가격은 5만원입니다. 주문을 원하시면 연락 주세요 감사합니다. \[가게 이름\] 드림 |
+
+**Table 1.** Partial listing of the tuning dataset for the Korean bakery email
+responder.
+
+#### Data format and loading
+
+You can store your tuning data in any format that is convenient, including
+database records, JSON files, CSV, or plain text files, as long as you have the
+means to retrieve the records with Python code. For convenience, the example
+tuning program gets the records from an
+[online repository](https://huggingface.co/datasets/bebechien/korean_cake_boss).
+In this example turning program, the tuning dataset is loaded in the
+`k-gemma-it/main.py` module using the `prepare_tuning_dataset()` function:
+
+```python
+def prepare_tuning_dataset():
+    tokenizer = keras_nlp.models.GemmaTokenizer.from_preset(model_id)
+
+    # load data from repository (or local directory)
+    from datasets import load_dataset
+    ds = load_dataset(
+        # Dataset : https://huggingface.co/datasets/bebechien/korean_cake_boss
+        "bebechien/korean_cake_boss",
+        split="train",
+    )
+    ...
+```
+
+As mentioned previously, you can store the dataset in a format that's
+convenient, as long as you can retrieve the requests with the associated
+responses and assemble them into a text string which is used as a tuning record.
+
+#### Assemble tuning records
+
+For the actual tuning process, each request and response is assembled into a
+single string with the prompt instructions and tags to indicate the content of
+the request and the content of the response. This tuning program then tokenizes
+the string for consumption by the model. You can see the code for assembling a
+tuning record in the `k-gemma-it/main.py` module `prepare_tuning_dataset()`
+function, as follows:
+
+```python
+def prepare_tuning_dataset():
+    ...
+    prompt_instruction = "다음에 대한 이메일 답장을 작성해줘."
+
+    for x in data:
+        item = f"<start_of_turn>user\n{prompt_instruction}\n\"{x['input']}\"<end_of_turn>\n<start_of_turn>model\n{x['output']}<end_of_turn>"
+        length = len(tokenizer(item))
+        # skip data if the token length is longer than our limit
+        if length < token_limit:
+            tuning_dataset.append(item)
+            if(len(tuning_dataset)>=num_data_limit):
+                break
+    ...
+```
+
+This function reads in the data and formats it by adding `start_of_turn` and
+`end_of_turn` tags which is the
+[required format](../get-started/prompt-structure.md)
+when providing data for tuning a Gemma model. This code also inserts a
+`prompt_instruction` for each request, which you should edit as appropriate for
+your application.
+
+!!! Important
+    Make sure you are using the Gemma tokenizer when creating tuning records. Using an incompatible tokenizer can cause your tuning output to not work with the Gemma model and cause it to generate unhelpful results.
+
+
+### Generate model weights
+
+Once you have the tuning data in place and loading, you can run the tuning
+program. The tuning process for this example application uses the Keras NLP
+library to tune the model with a
+[Low Rank Adaptation](https://arxiv.org/pdf/2106.09685), or LoRA technique,
+to generate new model weights. Compared to full precision tuning, using LoRA is
+significantly more memory efficient because it approximates the changes to the
+model weights. You can then overlay these approximated weights onto the existing
+model weights to change the model's behavior.
+
+To perform the tuning run and calculate new weights:
+
+1. In a terminal window, navigate to the `k-gemma-it/` directory.
+
+```bash
+   cd spoken-language-tasks/k-gemma-it/
+```
+
+2. Run the tuning process using the `tune_model` script:
+
+```text
+   ./tune_model.sh
+```
+
+The tuning process takes several minutes depending on your available compute
+resources. When it completes successfully, the tuning program writes new `*.h5`
+weight files in the `k-gemma-it/weights` directory with the following format:
+
+``` { .no-copy .output }
+gemma2-2b_k-tuned_4_epoch##.lora.h5
+```
+
+#### Troubleshooting
+
+If the tuning does not complete successfully, there are two likely reasons:
+
+1. **Out of memory / resources exhausted** : These errors occur when the tuning process requests memory that exceeds the available GPU memory or CPU memory. Make sure you are not running the web application while the tuning process is running. If you are tuning on a device with 16GB of GPU memory, makes sure your have the `token_limit` set to **256** and the `batch_size` set to **1**.
+2. **GPU drivers not installed or incompatible with JAX** : The turning process requires that the compute device has hardware drivers installed which are compatible with the version of the [JAX libraries](https://jax.readthedocs.io/). For more details, see the [JAX installation](https://jax.readthedocs.io/en/latest/installation.html) documentation.
+
+## Deploy tuned model
+
+The tuning process generates multiple weights based on the tuning data and the
+total number of epochs set in the tuning application. By default, the tuning
+program generates 20 model weight files, one for each tuning epoch. Each
+successive tuning epoch produces weights that more accurately reproduce the
+results of the tuning data. You can see the accuracy rates for each epoch in the
+terminal output of the tuning process, as follows:
+
+```text
+...
+Epoch 14/20
+10/10 ━━━━━━━━━━━━━━━━━━━━ 6s 567ms/step - loss: 0.4026 - sparse_categorical_accuracy: 0.8235
+Epoch 15/20
+10/10 ━━━━━━━━━━━━━━━━━━━━ 6s 569ms/step - loss: 0.3659 - sparse_categorical_accuracy: 0.8382
+Epoch 16/20
+10/10 ━━━━━━━━━━━━━━━━━━━━ 6s 571ms/step - loss: 0.3314 - sparse_categorical_accuracy: 0.8538
+Epoch 17/20
+10/10 ━━━━━━━━━━━━━━━━━━━━ 6s 572ms/step - loss: 0.2996 - sparse_categorical_accuracy: 0.8686
+Epoch 18/20
+10/10 ━━━━━━━━━━━━━━━━━━━━ 6s 574ms/step - loss: 0.2710 - sparse_categorical_accuracy: 0.8801
+Epoch 19/20
+10/10 ━━━━━━━━━━━━━━━━━━━━ 6s 575ms/step - loss: 0.2451 - sparse_categorical_accuracy: 0.8903
+Epoch 20/20
+10/10 ━━━━━━━━━━━━━━━━━━━━ 6s 575ms/step - loss: 0.2212 - sparse_categorical_accuracy: 0.9021
+```
+
+While you want the accuracy rate to be relatively high, around 0.80 to 0.90, you
+do not want the rate to be too high, or very close to 1.00, because that means
+the weights have come close to overfitting the tuning data. When that happens,
+the model does not perform well on requests that is significantly different from
+the tuning examples. By default, the deployment script picks the epoch 17
+weights, which typically have an accuracy rate around 0.90.
+
+To deploy the generated weights to the web application:
+
+1. In a terminal window, navigate to the `k-gemma-it/` directory.
+
+```bash
+   cd spoken-language-tasks/k-gemma-it/
+```
+
+2. Run the tuning process using the `deploy_weights` script:
+
+```text
+   ./deploy_weights.sh
+```
+
+After running this script, you should see a new `*.h5` file in the
+`k-mail-replier/k_mail_replier/weights/` directory.
+
+### Test the new model
+
+Once you have deployed the new weights to the application, it's time to try out
+the newly tuned model. You can do this by re-running the web application and
+generating a response.
+
+To run and test the project:
+
+1. In a terminal window, navigate to the `/k_mail_replier/` directory.
+
+```bash
+   cd spoken-language-tasks/k-mail-replier/
+```
+
+2. Run the application using the `run_flask_app.sh` script:
+
+```text
+   ./run_flask_app.sh
+```
+
+3. After starting the web application, you the program code lists a URL where
+   you can browse and test, typically this address is:
+
+```text
+   http://127.0.0.1:5000/
+```
+
+4. In the web interface, press the **답변 작성** button below the first input
+   field to generate a response from the model.
+
+You have now tuned and deployed a Gemma model in an application! Experiment with
+the application and try to determine the limits of the tuned model's generation
+capability for your task. If you find scenarios where the model does not perform
+well, consider adding some of those requests to your list of tuning example data
+by adding the request and providing an ideal response. Then re-run the tuning
+process, re-deploy the new weights, and test the output.
+
+## Additional resources
+
+For more information about this project, see the
+[Gemma Cookbook code repository](https://github.com/google-gemini/gemma-cookbook/tree/main/Demos/spoken-language-tasks).
+If you need help building the application or are looking to collaborate with
+other developers, check out the
+[Google Developers Community Discord](https://ai.google.dev/docs/discord)
+server. For more Build with Google AI projects, check out the
+[video playlist](https://goo.gle/BuildwithGoogleAI).
diff --git a/scripts/check_fidelity.py b/scripts/check_fidelity.py
index 8d9db17..2e5b822 100644
--- a/scripts/check_fidelity.py
+++ b/scripts/check_fidelity.py
@@ -45,6 +45,18 @@
 MD_IMAGE_TARGET = re.compile(r"(!\[[^\]]*\]\()[^)]*(\))")
 HTML_IMAGE_TARGET = re.compile(r"(<img[^>]+src=[\"'])[^\"']*([\"'])")
 
+# Link targets that Step 0.5 rewrites on purpose: an upstream docs URL in the source becomes a
+# local .md path in the output. Both forms are blanked so the retargeting doesn't read as loss.
+# Deliberately narrow -- other external links (Hugging Face, Kaggle, GitHub) stay in the
+# comparison, so silently losing one of those is still caught.
+REWRITTEN_LINK_TARGET = re.compile(
+    r"(\]\()"
+    r"(?:https?://ai\.google\.dev/gemma/docs[^)\s]*"
+    r"|[^)\s]*\.md(?:#[^)\s]*)?"
+    r")"
+    r"(\))"
+)
+
 
 def strip_designed_drops(source_text: str) -> str:
     """Remove the lines convert_notebook is *designed* to discard.
@@ -73,12 +85,25 @@ def strip_designed_drops(source_text: str) -> str:
     return "\n".join(kept)
 
 
+def normalise(token: str) -> str:
+    """Trim edge punctuation so a word doesn't change identity by moving in a sentence.
+
+    WORD keeps `.`/`-`/`+`/`_` so tokens like `ai.google.dev` and `v1.1` survive intact, but that
+    also glues on trailing punctuation: `[CodeGemma](url).` tokenises as `CodeGemma` (the `]`
+    stops it) while the delinked `CodeGemma.` tokenises with the period. Same word, different
+    token, reported as loss.
+    """
+    return token.strip("._-+")
+
+
 def words(text: str) -> Counter:
     """Word multiset, ignoring image targets and base64 payloads (see the regexes above)."""
     text = BASE64_PAYLOAD.sub(" ", text)
     text = MD_IMAGE_TARGET.sub(r"\1\2", text)
     text = HTML_IMAGE_TARGET.sub(r"\1\2", text)
-    return Counter(WORD.findall(text))
+    text = REWRITTEN_LINK_TARGET.sub(r"\1\2", text)
+    tokens = (normalise(t) for t in WORD.findall(text))
+    return Counter(t for t in tokens if len(t) >= 2)
 
 
 def compare(source_text: str, converted_text: str) -> tuple[Counter, Counter]:
diff --git a/scripts/link_map.toml b/scripts/link_map.toml
new file mode 100644
index 0000000..94b2b61
--- /dev/null
+++ b/scripts/link_map.toml
@@ -0,0 +1,161 @@
+# Upstream documentation path -> local page.
+#
+# Keys are the path after "https://ai.google.dev/gemma/docs" (http/https and #fragments are
+# handled by the rewriter). Values are paths under docs/. Consumed by scripts/rewrite_links.py.
+#
+# Not listed here on purpose:
+#   - URLs inside code blocks (sample data, BibTeX citations) stay external; see check_links.
+#   - Media assets (.wav/.png/.jpg) referenced from snippets stay external so copy-paste works.
+
+# ------------------------------------------------------------------------------------------
+# [ported] Target page exists today.
+# ------------------------------------------------------------------------------------------
+[ported]
+"/core" = "models/gemma-4/index.md"
+"/core/model_card_4" = "models/gemma-4/model-card.md"
+"/core/model_card_3" = "models/gemma-3/index.md"
+"/core/model_card_2" = "models/gemma-2/index.md"
+"/core/model_card" = "models/gemma-1/index.md"
+"/core/prompt-formatting-gemma4" = "get-started/prompt-formatting.md"
+"/core/huggingface_inference" = "deployment/huggingface.md"
+"/releases" = "models/releases.md"
+
+"/get_started" = "get-started/quickstart.md"
+"/capabilities/audio" = "get-started/capabilities/audio.md"
+"/capabilities/thinking" = "get-started/capabilities/thinking.md"
+"/capabilities/vision" = "get-started/capabilities/vision.md"
+"/capabilities/vision/image" = "get-started/capabilities/image.md"
+"/capabilities/vision/video" = "get-started/capabilities/video.md"
+"/capabilities/text/function-calling-gemma4" = "get-started/capabilities/function-calling.md"
+"/capabilities/text/basic" = "get-started/capabilities/text.md"
+
+"/gemma-3n" = "models/gemma-3n/index.md"
+"/3n" = "models/gemma-3n/index.md"                       # shorthand used in a few pages
+"/gemma-3n/model_card" = "models/gemma-3n/model-card.md"
+
+"/diffusiongemma" = "models/diffusiongemma/index.md"
+"/diffusiongemma/model_card" = "models/diffusiongemma/model-card.md"
+"/diffusiongemma/explained" = "models/diffusiongemma/explained.md"
+"/diffusiongemma/inference-diffusiongemma-with-hf" = "models/diffusiongemma/inference.md"
+
+"/functiongemma" = "models/functiongemma/index.md"
+"/functiongemma/model_card" = "models/functiongemma/model-card.md"
+"/functiongemma/formatting-and-best-practices" = "models/functiongemma/formatting.md"
+"/functiongemma/function-calling-with-hf" = "models/functiongemma/inference.md"
+"/functiongemma/full-function-calling-sequence-with-functiongemma" = "models/functiongemma/full-sequence.md"
+"/functiongemma/finetuning-with-functiongemma" = "models/functiongemma/fine-tuning.md"
+
+"/embeddinggemma" = "models/embeddinggemma/index.md"
+"/embeddinggemma/model_card" = "models/embeddinggemma/model-card.md"
+"/embeddinggemma/inference-embeddinggemma-with-sentence-transformers" = "models/embeddinggemma/inference.md"
+"/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers" = "models/embeddinggemma/fine-tuning.md"
+
+"/paligemma" = "models/paligemma/index.md"
+"/paligemma/model-card" = "models/paligemma/model-card-v1.md"
+"/paligemma/model-card-2" = "models/paligemma/model-card-v2.md"
+"/paligemma/prompt-system-instructions" = "models/paligemma/prompting.md"
+"/paligemma/inference-with-keras" = "models/paligemma/inference.md"
+"/paligemma/fine-tuning-paligemma" = "models/paligemma/fine-tuning.md"
+
+"/shieldgemma" = "models/shieldgemma/index.md"
+"/shieldgemma/model_card" = "models/shieldgemma/model-card-v1.md"
+"/shieldgemma/model_card_2" = "models/shieldgemma/model-card-v2.md"
+
+"/recurrentgemma" = "models/recurrentgemma/index.md"
+"/recurrentgemma/model_card" = "models/recurrentgemma/model-card.md"
+"/recurrentgemma/recurrentgemma_jax_inference" = "models/recurrentgemma/inference.md"
+"/recurrentgemma/recurrentgemma_jax_finetune" = "models/recurrentgemma/fine-tuning.md"
+
+"/datagemma" = "models/datagemma/index.md"
+"/gemma_scope" = "models/gemma-scope/index.md"
+"/gemma-4" = "models/gemma-4/index.md"                   # alias for /core used on some pages
+"/gemma-aps" = "models/gemma-aps/index.md"
+
+# Run / deployment. Targets are existing stubs; Steps 2.2-2.3 fill them in.
+"/run" = "deployment/index.md"
+"/integrations/ollama" = "deployment/ollama.md"
+"/integrations/llamacpp" = "deployment/llamacpp.md"
+"/integrations/lmstudio" = "deployment/lmstudio.md"
+"/integrations/mlx" = "deployment/mlx.md"
+"/integrations/web" = "deployment/web.md"
+"/integrations/mobile" = "deployment/mobile.md"
+"/integrations/langchain" = "deployment/langchain.md"
+"/integrations/google-cloud" = "deployment/google-cloud.md"
+"/core/gke" = "deployment/google-cloud.md"               # GKE folded into the Google Cloud page
+"/mobile-actions" = "deployment/mobile-actions.md"
+
+# Fine-tuning. Targets are existing stubs; Step 2.3 fills them in.
+"/tune" = "tuning/overview.md"
+"/core/huggingface_text_finetune_qlora" = "tuning/qlora.md"
+"/core/huggingface_vision_finetune_qlora" = "tuning/vision-qlora.md"
+"/core/huggingface_text_full_finetune" = "tuning/full-finetune.md"
+"/core/distributed_tuning" = "tuning/distributed.md"
+"/core/lora_tuning" = "tuning/keras-lora.md"
+"/spoken-language/task-specific-tuning" = "tuning/spoken-language.md"
+
+# Learn
+"/business-email-assistant" = "learn/business-email-assistant.md"
+"/personal-code-assistant" = "learn/personal-code-assistant.md"
+
+# Now ported (were [planned] in Step 0.5)
+"/setup" = "get-started/setup.md"
+"/core/prompt-structure" = "get-started/prompt-structure.md"
+"/mtp/overview" = "get-started/capabilities/mtp.md"
+"/mtp/mtp" = "get-started/capabilities/mtp.md"
+
+# No such upstream page. Referenced from tuning docs about `start_of_turn`/`end_of_turn` tags
+# for core Gemma, so the core formatting page is the right target -- not paligemma/prompting.md,
+# which covers PaliGemma's task-specific syntax.
+"/core/prompt-system-instructions" = "get-started/prompt-structure.md"
+
+# ------------------------------------------------------------------------------------------
+# [planned] Page does not exist yet. Links point at the eventual path so the linter's
+# `internal` check tracks them as known-pending, rather than leaving live links to the site
+# we are replacing. Paths use the TARGET navigation from PLAN.md, not today's directories.
+# ------------------------------------------------------------------------------------------
+[planned]
+# Keras inference is documented upstream but absent from the target nav in PLAN.md.
+# Decide in Step 2.1 whether "Run > Local" gains a Keras page; the link is local either way.
+"/core/keras_inference" = "deployment/keras.md"
+
+# ------------------------------------------------------------------------------------------
+# [anchors] Fragments whose section exists but under a different slug than devsite used.
+# `--fix-anchors` handles the common underscore -> hyphen case automatically; these are the
+# ones where the heading text itself differs, so a heuristic would risk linking to the wrong
+# section. Key is "<target page>#<upstream fragment>", value is the correct slug.
+#
+# NOT listed here (deliberately): anchors that are broken upstream too. `#harm-types` on
+# shieldgemma/model-card-v2.md and `#categories` on paligemma/index.md point at sections that
+# do not exist on ai.google.dev either. Inventing a target would be an editorial guess; see
+# PLAN.md Notes.
+# ------------------------------------------------------------------------------------------
+[anchors]
+"models/gemma-4/index.md#previous-models" = "previous-gemma-models"
+# devsite used short custom anchors; our headings come from the source's own section titles.
+"deployment/huggingface.md#audio" = "generate-text-from-audio-data"
+"deployment/huggingface.md#vision" = "generate-text-from-image-data"
+"deployment/index.md#select-a-variant" = "select-a-gemma-variant"
+"deployment/index.md#run-generation" = "run-generation-and-inference-requests"
+"get-started/setup.md#get-access" = "get-access-to-gemma"
+"learn/business-email-assistant.md#compute_requirements" = "hardware-requirements"
+"learn/personal-code-assistant.md#compute_requirements" = "hardware-requirements"
+"learn/personal-code-assistant.md#extension" = "vs-code-extension-project"
+"learn/personal-code-assistant.md#web-service" = "gemma-web-service-project"
+"tuning/spoken-language.md#compute_requirements" = "hardware-requirements"
+"models/gemma-3n/index.md#matformer" = "matformer-architecture"
+"models/gemma-3n/index.md#conditional-parameter" = "conditional-parameter-loading"
+"models/gemma-3n/index.md#parameters" = "model-parameters-and-effective-parameters"
+
+# ------------------------------------------------------------------------------------------
+# [delink] Out of scope for these docs. The link is removed but the text is kept, so the
+# sentence still reads correctly and no prose is lost.
+# ------------------------------------------------------------------------------------------
+[delink]
+paths = [
+  "/codegemma",              # CodeGemma is not being ported (decision: 2026-08-03)
+  "/codegemma/model_card",
+  "/codegemma/prompt-structure",
+  "/codegemma/keras_quickstart",
+  "/codegemma/code_assist_keras",
+  "/codegemma/codegemma_flax_inference",
+]
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index 24f0383..f98dc1c 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -248,9 +248,15 @@ def outside_code(page: Page) -> Iterator[tuple[int, str]]:
 
 
 def check_links(ctx: Context) -> Iterable[Finding]:
-    """No links back to the documentation site we're replacing."""
+    """No prose links back to the documentation site we're replacing.
+
+    Code blocks are exempt, and deliberately so. Every upstream URL inside a snippet here is
+    either sample data the reader is meant to fetch (`img_url = "https://.../cat.png"`) or a
+    BibTeX citation, which must keep pointing at the canonical public URL. Rewriting those to
+    local paths would break copy-paste and misattribute citations.
+    """
     for page in ctx.pages:
-        for lineno, line in page.body_lines():
+        for lineno, line in outside_code(page):
             if UPSTREAM_DOCS in line:
                 for match in re.finditer(rf"https?://{re.escape(UPSTREAM_DOCS)}[^\s)\"'>]*", line):
                     yield Finding(
diff --git a/scripts/rewrite_links.py b/scripts/rewrite_links.py
new file mode 100644
index 0000000..2ce2889
--- /dev/null
+++ b/scripts/rewrite_links.py
@@ -0,0 +1,235 @@
+#!/usr/bin/env python3
+"""
+Rewrite upstream ai.google.dev links to local page paths, per scripts/link_map.toml.
+
+    python scripts/rewrite_links.py --dry-run
+    python scripts/rewrite_links.py
+    python scripts/rewrite_links.py --report      # unmapped targets only, no changes
+
+Three behaviours, driven by the map:
+  [ported]   -> relative link to an existing page
+  [planned]  -> relative link to a page that does not exist yet, so the linter's `internal`
+                check tracks it as known-pending instead of leaving a live upstream link
+  [delink]   -> drop the link, keep the text (used for CodeGemma, which is out of scope)
+
+Code blocks are never touched. URLs there are sample data or BibTeX citations that must stay
+external -- the same rule check_links applies.
+
+An unmapped target is reported and left alone. Silently dropping a link is worse than leaving
+one to fix; add it to link_map.toml and re-run.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import re
+import sys
+import tomllib
+from collections import Counter
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from lint_docs import load_pages, outside_code  # noqa: E402
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DOCS_DIR = REPO_ROOT / "docs"
+LINK_MAP = Path(__file__).resolve().parent / "link_map.toml"
+
+BASE = re.compile(r"https?://ai\.google\.dev/gemma/docs")
+# A markdown link whose target is an upstream docs URL.
+MD_LINK = re.compile(r"\[([^\]]*)\]\((https?://ai\.google\.dev/gemma/docs[^)\s]*)\)")
+# Anything with a file extension is an asset (sample image/audio), not a documentation page.
+ASSET = re.compile(r"\.[a-z0-9]{2,4}$", re.IGNORECASE)
+
+
+HEADING = re.compile(r"^(#{1,6})\s+(.+?)\s*$", re.MULTILINE)
+LOCAL_LINK_WITH_ANCHOR = re.compile(r"(\]\()([^)\s#]*\.md)(#+)([^)\s]+)(\))")
+
+
+def slugify(heading: str) -> str:
+    """Mirror python-markdown's default toc slugify, which is what Zensical uses."""
+    value = re.sub(r"<[^>]+>", "", heading)                 # strip inline html
+    value = re.sub(r"[*`_\[\]]|\(.*?\)", "", value)         # strip inline markdown
+    value = re.sub(r"[^\w\s-]", "", value).strip().lower()
+    return re.sub(r"[-\s]+", "-", value)
+
+
+def page_anchors() -> dict[Path, set[str]]:
+    """Heading slugs available on each page, keyed by resolved path."""
+    anchors: dict[Path, set[str]] = {}
+    for page in load_pages():
+        slugs = set()
+        in_code = False
+        for line in page.body.splitlines():
+            if line.lstrip().startswith("```"):
+                in_code = not in_code
+                continue
+            if in_code:
+                continue
+            match = HEADING.match(line)
+            if match:
+                slugs.add(slugify(match.group(2)))
+        anchors[page.path.resolve()] = slugs
+    return anchors
+
+
+def fix_anchors(dry_run: bool) -> int:
+    """Repair fragments on local links: devsite used `#model_data`, Zensical emits `#model-data`.
+
+    Also collapses `##`, which appears in at least one upstream URL verbatim.
+    """
+    anchors = page_anchors()
+    overrides = tomllib.loads(LINK_MAP.read_text(encoding="utf-8")).get("anchors", {})
+    fixed = unresolved = collapsed = 0
+
+    for page in load_pages():
+        if ".md#" not in page.text and ".md##" not in page.text:
+            continue
+
+        def replace(match: re.Match) -> str:
+            nonlocal fixed, unresolved, collapsed
+            opening, target, hashes, fragment, closing = match.groups()
+            resolved = (page.path.parent / target).resolve()
+            available = anchors.get(resolved)
+
+            if len(hashes) > 1:
+                collapsed += 1
+
+            if available is None:
+                return f"{opening}{target}#{fragment}{closing}"  # page doesn't exist yet
+            if fragment in available:
+                return f"{opening}{target}#{fragment}{closing}"
+
+            candidate = fragment.replace("_", "-")
+            if candidate in available:
+                fixed += 1
+                return f"{opening}{target}#{candidate}{closing}"
+
+            # Explicit override: the section was renamed, so no heuristic can find it safely.
+            try:
+                key = f"{resolved.relative_to(DOCS_DIR).as_posix()}#{fragment}"
+            except ValueError:
+                key = ""
+            if key in overrides:
+                fixed += 1
+                return f"{opening}{target}#{overrides[key]}{closing}"
+
+            unresolved += 1
+            print(f"  ?? {page.repo_rel}: #{fragment} not found in {target}")
+            return f"{opening}{target}#{fragment}{closing}"
+
+        updated = LOCAL_LINK_WITH_ANCHOR.sub(replace, page.text)
+        if updated != page.text and not dry_run:
+            page.path.write_text(updated, encoding="utf-8")
+
+    verb = "would fix" if dry_run else "fixed"
+    print(f"\n{verb} {fixed} anchor(s), collapsed {collapsed} '##', {unresolved} unresolved")
+    return 0
+
+
+def load_map() -> tuple[dict[str, str], dict[str, str], set[str]]:
+    data = tomllib.loads(LINK_MAP.read_text(encoding="utf-8"))
+    return (
+        data.get("ported", {}),
+        data.get("planned", {}),
+        set(data.get("delink", {}).get("paths", [])),
+    )
+
+
+def split_target(url: str) -> tuple[str, str]:
+    """Split an upstream URL into (path after /gemma/docs, '#fragment' or '')."""
+    remainder = BASE.sub("", url)
+    path, sep, fragment = remainder.partition("#")
+    return path.rstrip("/"), (sep + fragment if sep else "")
+
+
+def relative(target: str, page_path: Path) -> str:
+    return os.path.relpath(DOCS_DIR / target, page_path.parent).replace("\\", "/")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--report", action="store_true", help="list unmapped targets and exit")
+    parser.add_argument(
+        "--fix-anchors", action="store_true",
+        help="repair #fragments on local links (devsite underscores -> Zensical hyphens)",
+    )
+    args = parser.parse_args()
+
+    if args.fix_anchors:
+        return fix_anchors(args.dry_run)
+
+    ported, planned, delink = load_map()
+    mapping = {**ported, **planned}
+
+    unmapped: Counter = Counter()
+    stats = Counter()
+    changed_pages = 0
+
+    for page in load_pages():
+        if "ai.google.dev/gemma/docs" not in page.text:
+            continue
+
+        # Match over the whole document, not line by line: devsite wraps long links across
+        # lines (`[thinking\nmodes](url)`), which a per-line regex silently misses.
+        # A per-character prose mask keeps code blocks exempt.
+        prose_lines = {n for n, _ in outside_code(page)}
+        mask = bytearray(len(page.text))
+        position = 0
+        for number, line in enumerate(page.text.splitlines(keepends=True), start=1):
+            if number in prose_lines:
+                mask[position : position + len(line)] = b"\x01" * len(line)
+            position += len(line)
+
+        modified = False
+
+        def replace(match: re.Match) -> str:
+            nonlocal modified
+            if not mask[match.start()]:
+                return match.group(0)  # inside a code block
+            text, url = match.group(1), match.group(2)
+            path, fragment = split_target(url)
+
+            if ASSET.search(path):
+                stats["asset (left external)"] += 1
+                return match.group(0)
+            if path in delink:
+                stats["delinked"] += 1
+                modified = True
+                return text
+            if path in mapping:
+                stats["planned" if path in planned else "ported"] += 1
+                modified = True
+                return f"[{text}]({relative(mapping[path], page.path)}{fragment})"
+
+            unmapped[path] += 1
+            return match.group(0)
+
+        updated = MD_LINK.sub(replace, page.text)
+
+        if modified:
+            changed_pages += 1
+            if not args.dry_run and not args.report:
+                page.path.write_text(updated, encoding="utf-8")
+
+    if args.report or unmapped:
+        print("Unmapped upstream targets:" if unmapped else "All targets mapped.")
+        for path, count in unmapped.most_common():
+            print(f"  {count:3}  {path}")
+        if unmapped:
+            print("\nAdd these to scripts/link_map.toml and re-run.")
+
+    if not args.report:
+        verb = "would rewrite" if args.dry_run else "rewrote"
+        print(f"\n{verb} links on {changed_pages} page(s):")
+        for kind, count in sorted(stats.items()):
+            print(f"  {count:4}  {kind}")
+
+    return 1 if unmapped else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/sources.toml b/scripts/sources.toml
index 3485c86..6f2b0ac 100644
--- a/scripts/sources.toml
+++ b/scripts/sources.toml
@@ -11,8 +11,60 @@
 #
 # `assets` is the subdirectory under docs/assets/ that inline base64 images are extracted to.
 
+# --- Get started ------------------------------------------------------------------------------
+# Ported in Step 0.6 to resolve links that Step 0.5 pointed at planned paths. These pages are
+# not in the nav yet -- Step 2.1 places them.
+
+[[page]]
+path = "get-started/setup.md"
+source = "https://ai.google.dev/gemma/docs/setup.md.txt"
+
+[[page]]
+path = "get-started/prompt-structure.md"
+source = "https://ai.google.dev/gemma/docs/core/prompt-structure.md.txt"
+
+[[page]]
+path = "get-started/capabilities/mtp.md"
+source = "https://ai.google.dev/gemma/docs/mtp/overview.md.txt"
+
+# --- Run --------------------------------------------------------------------------------------
+
+[[page]]
+path = "deployment/index.md"
+source = "https://ai.google.dev/gemma/docs/run.md.txt"
+
+[[page]]
+path = "deployment/huggingface.md"
+source = "https://ai.google.dev/gemma/docs/core/huggingface_inference.md.txt"
+assets = "huggingface"
+
+[[page]]
+path = "deployment/mobile-actions.md"
+source = "https://ai.google.dev/gemma/docs/mobile-actions.md.txt"
+
+# --- Tuning / Learn ---------------------------------------------------------------------------
+
+[[page]]
+path = "tuning/spoken-language.md"
+source = "https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning.md.txt"
+assets = "tuning"
+
+[[page]]
+path = "learn/business-email-assistant.md"
+source = "https://ai.google.dev/gemma/docs/business-email-assistant.md.txt"
+assets = "learn"
+
+[[page]]
+path = "learn/personal-code-assistant.md"
+source = "https://ai.google.dev/gemma/docs/personal-code-assistant.md.txt"
+assets = "learn"
+
 # --- Core -------------------------------------------------------------------------------------
 
+[[page]]
+path = "models/gemma-aps/index.md"
+source = "https://ai.google.dev/gemma/docs/gemma-aps.md.txt"
+
 [[page]]
 path = "models/gemma-4/index.md"
 source = "https://ai.google.dev/gemma/docs/core.md.txt"

From 7206ccea1e5650006291c661ad8bb1a93b06de58 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 14:34:18 +0200
Subject: [PATCH 06/20] Track Gemma 4 information

---
 .lintbaseline.json             |   3 +-
 PLAN.md                        |  69 ++++++++++++++++++++-
 data/models.yml                | 107 +++++++++++++++++++++++++++++++++
 docs/get-started/quickstart.md |  14 ++---
 docs/models/index.md           |  17 +++---
 scripts/lint_docs.py           |  54 +++++++++++++++++
 zensical.toml                  |   4 +-
 7 files changed, 250 insertions(+), 18 deletions(-)
 create mode 100644 data/models.yml

diff --git a/.lintbaseline.json b/.lintbaseline.json
index 2944c4b..d782861 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -9,7 +9,8 @@
     "orphans",
     "fences",
     "admonitions",
-    "size"
+    "size",
+    "modeldata"
   ],
   "count": 136,
   "known": [
diff --git a/PLAN.md b/PLAN.md
index 0f4d461..12e0d2d 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -460,7 +460,72 @@ dangling anchor is a useful signal that a section is genuinely missing. **Needs
     `orphans` rose 4 → 13. That is expected — Step 2.1 places them. Do not "fix" it by
     deleting pages.
 
-### Step 0.6 — Model capability matrix as single source of truth
+### Step 0.6 — Model capability matrix as single source of truth ✅
+
+- [x] `data/models.yml` — every Gemma 4 size with parameters, architecture, modalities in/out,
+      context window, memory at each quantization, and target platform.
+- [x] Wired into `zensical.toml` via the macros plugin's `include_yaml`, exposed as `models.*`.
+- [x] `quickstart.md` and `models/index.md` tables **generated** from it.
+- [x] New `modeldata` lint check guarding the one table that can't be generated (below).
+- **Verify:** ✅ changed `context: 128K` → `TESTVALUE`, rebuilt, both tables updated; reverted,
+      both reverted. Drift check verified by injecting a wrong figure — caught it.
+
+**The modality contradiction is resolved.** Confirmed by Maarten (2026-08-03):
+
+| Model | Input | Note |
+|---|---|---|
+| E2B, E4B, 12B | Text, Image, Audio, Video | full multimodal |
+| 26B A4B, 31B | Text, Image, Video | **no audio**; video is processed as individual frames |
+
+This matches `models/gemma-4/index.md:45` ("Audio... featured natively on the E2B, E4B and 12B
+models"). The stale source was the `quickstart.md` table, which omitted video entirely and is
+now generated.
+
+!!! warning "One table is deliberately *not* generated"
+    `models/gemma-4/index.md` is a straight port (`scripts/sources.toml`), so a template there
+    would be wiped by the next `port_docs.py` run. Its memory figures were copied into
+    `data/models.yml` instead, and the new **`modeldata`** check compares the two — so if
+    upstream revises a number, it surfaces as a finding rather than a silent contradiction.
+
+```bash
+uv run python scripts/lint_docs.py --check modeldata
+```
+
+---
+
+## Phase 0 complete — 300 → 136
+
+| Check | Start | Now | |
+|---|---|---|---|
+| `links` | 122 | **0** | ✅ |
+| `images` | 52 | **0** | ✅ |
+| `fences` | 7 | **0** | ✅ |
+| `size` | 1 | **0** | ✅ |
+| `admonitions` | 0 | **0** | ✅ |
+| `modeldata` | — | **0** | ✅ new check |
+| `internal` | 0 | 2 | `deployment/keras.md` — Step 2.1 decision |
+| `stubs` | 33 | 31 | → Steps 2.2–2.4 |
+| `orphans` | 4 | 13 | → Step 2.1 (9 newly ported pages await nav) |
+| `frontmatter` | 81 | 90 | → Step 3.1 |
+
+**Everything remaining is scheduled work, not defects.** The three checks that still have
+findings are all waiting on Phase 2 (nav, content) or Phase 3 (frontmatter schema).
+
+### Open decision for Step 2.1
+
+Whether `docs/models/` should mirror the `get-started/capabilities/` pattern with
+`core/`, `variants/`, `research/` subdirectories.
+
+**Recommendation: no.** Two of the four nav groups are time-dependent — when Gemma 5 ships,
+Gemma 4 moves from *Gemma 4* to *Previous generations*, and every Gemma 4 URL would break at
+the worst possible moment. Classification is a presentation concern; identity is `gemma-4`.
+The nav already groups them (`zensical.toml` lines 40, 49, 88) at zero URL cost, and
+`models/index.md` is the grouped hub — it just needs wiring up in Step 2.1.
+
+If done anyway: land it *during* Step 2.1 (nav is being rewritten regardless) and *before*
+Step 3.1 adds `source_url` frontmatter, as one reviewable pure-move commit with redirects.
+Cost is ~195 path updates (39 files, 90 outbound links, 61 inbound, 44 asset refs).
+
 - [ ] Create `data/models.yml`: every Gemma 4 size with parameters, modalities in/out, context
       window, memory at each quantization, MTP support, target platform.
 - [ ] Resolve the modality contradiction (`quickstart.md` vs `models/index.md:14` vs
@@ -598,7 +663,7 @@ actual model support) → context/KV-cache flags → troubleshooting → measure
 
 ### Step 3.4 — Copy-for-agent affordances
 - [ ] "Copy page as Markdown" button in the page header.
-- [ ] "Open in Claude / ChatGPT" links.
+- [ ] "Open in Gemini / Claude / ChatGPT" links.
 - **Verify:** click the button, paste, confirm clean markdown with frontmatter.
 
 ### Step 3.5 — Docs as a skill
diff --git a/data/models.yml b/data/models.yml
new file mode 100644
index 0000000..413a3c1
--- /dev/null
+++ b/data/models.yml
@@ -0,0 +1,107 @@
+# Gemma model facts -- the single source of truth.
+#
+# Consumed by:
+#   - Generated tables in quickstart.md and models/index.md (macros plugin, `models.*`)
+#   - The VRAM calculator          (PLAN.md Step 4.1)
+#   - Global model/engine switcher (PLAN.md Step 4.2) -- `modalities` drives capability gating,
+#     so an audio example can never offer a model that cannot hear
+#
+# Hand-editing a model table in a page is a bug. Change it here instead.
+#
+# NOT generated from this file: the memory table in models/gemma-4/index.md. That page is a
+# straight port (scripts/sources.toml) and re-porting would overwrite any template. The figures
+# below are copied from it; keep them in sync if upstream revises them.
+
+gemma4:
+  context_windows:
+    small: 128K      # E2B, E4B
+    medium: 256K     # 12B, 26B A4B, 31B
+
+  # Every Gemma 4 model ships a dedicated draft model for speculative decoding.
+  multi_token_prediction: true
+
+  sizes:
+    - id: e2b
+      name: E2B
+      full_name: Gemma 4 E2B
+      parameters: 2B effective
+      architecture: efficient          # Per-Layer Embeddings (PLE)
+      context: 128K
+      platform: Mobile devices
+      platform_short: Mobile devices
+      modalities:
+        input: [text, image, audio, video]
+        output: [text]
+      memory:                          # GB, static weights + ~20% overhead
+        bf16: 11.4
+        sfp8: 5.7
+        q4_0: 2.9
+        mobile: 1.1
+        mobile_text_only: 0.84
+
+    - id: e4b
+      name: E4B
+      full_name: Gemma 4 E4B
+      parameters: 4B effective
+      architecture: efficient
+      context: 128K
+      platform: Mobile devices and laptops
+      platform_short: Mobile and laptops
+      modalities:
+        input: [text, image, audio, video]
+        output: [text]
+      memory:
+        bf16: 17.9
+        sfp8: 8.9
+        q4_0: 4.5
+        mobile: 2.5
+        mobile_text_only: 2.2
+
+    - id: 12b
+      name: 12B
+      full_name: Gemma 4 12B
+      parameters: 12B
+      architecture: unified            # encoder-free; linear projections of the input
+      context: 256K
+      platform: Laptops, desktop computers and small servers
+      platform_short: Laptops and desktops
+      modalities:
+        input: [text, image, audio, video]
+        output: [text]
+      memory:
+        bf16: 26.7
+        sfp8: 13.4
+        q4_0: 6.7
+
+    - id: 26b-a4b
+      name: 26B A4B
+      full_name: Gemma 4 26B A4B
+      parameters: 26B (4B active)
+      architecture: moe                # all 26B must be resident; 4B active per token
+      context: 256K
+      platform: Desktop computers and small servers
+      platform_short: Desktops and servers
+      modalities:
+        # No audio. Video is supported as individual frames, i.e. as images.
+        input: [text, image, video]
+        output: [text]
+      memory:
+        bf16: 57.7
+        sfp8: 28.8
+        q4_0: 14.4
+
+    - id: 31b
+      name: 31B
+      full_name: Gemma 4 31B
+      parameters: 31B
+      architecture: dense
+      context: 256K
+      platform: Large servers or server clusters
+      platform_short: Servers
+      modalities:
+        input: [text, image, video]
+        output: [text]
+      memory:
+        bf16: 69.9
+        sfp8: 34.9
+        q4_0: 17.5
diff --git a/docs/get-started/quickstart.md b/docs/get-started/quickstart.md
index 331ef23..36d0df0 100644
--- a/docs/get-started/quickstart.md
+++ b/docs/get-started/quickstart.md
@@ -9,13 +9,13 @@ The Gemma family of open models includes a range of model sizes, capabilities, a
 
 *The latest generation featuring multimodal (text, image, audio) inputs.*
 
-| Size | Variant | Input ➔ Output | Intended Platform |
-|---|---|---|---|
-| **E2B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images, audio ➔ Text | Mobile devices |
-| **E4B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images, audio ➔ Text | Mobile devices and laptops |
-| **12B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images, audio ➔ Text | Laptops, desktop computers and small servers |
-| **A4B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images ➔ Text | Desktop computers and small servers |
-| **31B** | [Gemma 4 (core)](../models/gemma-4/index.md) | Text, images ➔ Text | Large servers or server clusters |
+<!-- Generated from data/models.yml -- edit that file, not this table. -->
+
+| Size | Variant | Input ➔ Output | Context | Intended Platform |
+|---|---|---|---|---|
+{% for size in models.gemma4.sizes -%}
+| **{{ size.name }}** | [Gemma 4 (core)](../models/gemma-4/index.md) | {{ size.modalities.input | map('capitalize') | join(', ') }} ➔ {{ size.modalities.output | map('capitalize') | join(', ') }} | {{ size.context }} | {{ size.platform }} |
+{% endfor %}
 
 !!! tip "New to Gemma?"
     A good place to start is the Gemma 4 12B model, which can be used for many tasks and runs on laptop hardware.
diff --git a/docs/models/index.md b/docs/models/index.md
index ff15502..a69b61f 100644
--- a/docs/models/index.md
+++ b/docs/models/index.md
@@ -6,13 +6,16 @@ The Gemma family of open models spans multiple generations and specialized varia
 
 Gemma 4 spans four distinct architectures:
 
-| Variant | Parameters | Input | Intended Platform |
-|---|---|---|---|
-| **E2B** | 2B effective | Text, Image, Audio | Mobile devices |
-| **E4B** | 4B effective | Text, Image, Audio | Mobile and laptops |
-| **12B** | 12B | Text, Image, Audio | Laptops and desktops |
-| **26B A4B** | 26B (4B active) | Text, Image, Audio | Desktops and servers |
-| **31B** | 31B | Text, Image | Servers |
+<!-- Generated from data/models.yml -- edit that file, not this table. -->
+
+| Variant | Parameters | Input | Context | Intended Platform |
+|---|---|---|---|---|
+{% for size in models.gemma4.sizes -%}
+| **{{ size.name }}** | {{ size.parameters }} | {{ size.modalities.input | map('capitalize') | join(', ') }} | {{ size.context }} | {{ size.platform_short }} |
+{% endfor %}
+!!! note "Audio and video support"
+    Audio input is native to **E2B, E4B and 12B**. The **26B A4B** and **31B** models accept
+    video as individual frames — that is, as images — but do not process audio.
 
 ## Previous Generations
 
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index f98dc1c..908cb6e 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -426,6 +426,58 @@ def check_size(ctx: Context) -> Iterable[Finding]:
             )
 
 
+def check_modeldata(ctx: Context) -> Iterable[Finding]:
+    """Keep data/models.yml in step with the ported memory table it was copied from.
+
+    models/gemma-4/index.md is a straight port, so its table can't be generated from the data
+    file -- re-porting would overwrite the template. Instead the two are compared here, so
+    upstream revising a figure shows up as a finding rather than a silent contradiction.
+    """
+    data_file = REPO_ROOT / "data" / "models.yml"
+    page_path = DOCS_DIR / "models" / "gemma-4" / "index.md"
+    if not data_file.exists() or not page_path.exists():
+        return
+
+    # Minimal parse: `bf16: 11.4` style keys under each size's `memory:` block, in file order.
+    text = data_file.read_text(encoding="utf-8")
+    declared: list[tuple[str, dict[str, str]]] = []
+    current: str | None = None
+    for line in text.splitlines():
+        name = re.match(r"^\s*name:\s*(\S.*?)\s*$", line)
+        if name:
+            current = name.group(1)
+            declared.append((current, {}))
+            continue
+        figure = re.match(r"^\s*(bf16|sfp8|q4_0):\s*([\d.]+)\s*$", line)
+        if figure and declared:
+            declared[-1][1][figure.group(1)] = figure.group(2)
+
+    # Table rows look like: | Gemma 4 E2B | 11.4 GB | 5.7 GB | 2.9 GB | ...
+    rows = {}
+    for line in page_path.read_text(encoding="utf-8").splitlines():
+        cells = [c.strip() for c in line.split("|")[1:-1]]
+        if len(cells) >= 4 and cells[0].startswith("Gemma 4 "):
+            rows[cells[0].removeprefix("Gemma 4 ")] = [
+                c.removesuffix(" GB").strip() for c in cells[1:4]
+            ]
+
+    for name, figures in declared:
+        row = rows.get(name)
+        if row is None:
+            yield Finding(
+                "modeldata", "data/models.yml", 0,
+                f"'{name}' has no row in models/gemma-4/index.md",
+            )
+            continue
+        for key, expected in zip(("bf16", "sfp8", "q4_0"), row):
+            actual = figures.get(key)
+            if actual and actual != expected:
+                yield Finding(
+                    "modeldata", "data/models.yml", 0,
+                    f"{name} {key}: data says {actual}, ported table says {expected}",
+                )
+
+
 CHECKS: dict[str, Callable[[Context], Iterable[Finding]]] = {
     "links": check_links,
     "images": check_images,
@@ -436,6 +488,7 @@ def check_size(ctx: Context) -> Iterable[Finding]:
     "fences": check_fences,
     "admonitions": check_admonitions,
     "size": check_size,
+    "modeldata": check_modeldata,
 }
 
 CHECK_HELP = {
@@ -448,6 +501,7 @@ def check_size(ctx: Context) -> Iterable[Finding]:
     "fences": "prose or prompts captured as python code blocks",
     "admonitions": "unconverted devsite Note:/Caution: callouts",
     "size": "files large enough to indicate inlined assets",
+    "modeldata": "data/models.yml vs the ported memory table",
 }
 
 
diff --git a/zensical.toml b/zensical.toml
index 05eb076..599bcfb 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -13,7 +13,9 @@ copyright = """
 Copyright &copy; 2026 Google DeepMind
 """
 extra_css = ["stylesheets/home.css", "stylesheets/layout.css", "stylesheets/cookbook.css", "stylesheets/code.css", "stylesheets/tokens.css"]
-plugins = { macros = { enabled = true } }
+# `include_yaml` exposes data/models.yml as `models.*` in page templates -- see data/models.yml
+# for what reads it. Hand-editing a model table in a page is a bug; change the data file.
+plugins = { macros = { enabled = true, include_yaml = { models = "data/models.yml" } } }
 
 # ============================================================================
 # NAVIGATION

From 6e3e8feccc59f3c6c3efc3f204de3f2339c44c4b Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 15:16:20 +0200
Subject: [PATCH 07/20] Minor fixes to video rendering, titles, etc.

---
 PLAN.md                                   |  29 ++++
 docs/get-started/setup.md                 |   4 +-
 docs/learn/business-email-assistant.md    |  83 ++++-----
 docs/learn/personal-code-assistant.md     | 103 +++++------
 docs/models/datagemma/index.md            |  12 +-
 docs/models/diffusiongemma/inference.md   |   4 -
 docs/models/diffusiongemma/model-card.md  |   6 +-
 docs/models/embeddinggemma/fine-tuning.md |  18 +-
 docs/models/embeddinggemma/index.md       |   7 +-
 docs/models/embeddinggemma/inference.md   | 170 +++++++++---------
 docs/models/embeddinggemma/model-card.md  |   5 -
 docs/models/functiongemma/fine-tuning.md  |   4 +-
 docs/models/functiongemma/formatting.md   |  14 +-
 docs/models/functiongemma/index.md        |   7 +-
 docs/models/functiongemma/model-card.md   |   4 +-
 docs/models/gemma-1/index.md              |   2 +-
 docs/models/gemma-2/index.md              |   2 +-
 docs/models/gemma-3/index.md              |   4 +-
 docs/models/gemma-3n/index.md             |   7 +-
 docs/models/gemma-4/index.md              |   7 +-
 docs/models/gemma-4/model-card.md         |  32 ++--
 docs/models/gemma-aps/index.md            |  12 +-
 docs/models/gemma-scope/index.md          |  12 +-
 docs/models/paligemma/fine-tuning.md      | 192 ++++++++++----------
 docs/models/paligemma/index.md            |  18 +-
 docs/models/recurrentgemma/fine-tuning.md | 122 ++++++-------
 docs/models/recurrentgemma/index.md       |  18 +-
 docs/models/recurrentgemma/inference.md   |  72 ++++----
 docs/models/shieldgemma/index.md          |  19 +-
 docs/stylesheets/components.css           |  55 ++++++
 docs/tuning/spoken-language.md            |  85 ++++-----
 scripts/check_fidelity.py                 |  14 ++
 scripts/convert_notebook.py               | 202 +++++++++++++++++++++-
 scripts/link_map.toml                     |  18 ++
 scripts/rewrite_links.py                  |  11 +-
 zensical.toml                             |   2 +-
 36 files changed, 835 insertions(+), 541 deletions(-)
 create mode 100644 docs/stylesheets/components.css

diff --git a/PLAN.md b/PLAN.md
index 12e0d2d..623905f 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -821,6 +821,35 @@ Must cover:
 
 ---
 
+### Step 0.7 — Rendering fixes from review ✅
+
+All fixed **in the converter**, not per-page, so re-porting preserves them.
+
+- [x] **Devsite card grids → plain bullets.** Upstream's own `.md.txt` export mangles its card
+      HTML into an empty bullet + `####` heading + paragraph. Now collapsed to
+      `- **Title** — description`. Affected shieldgemma, recurrentgemma, datagemma,
+      gemma-scope, paligemma, gemma-aps (14 cards).
+- [x] **Videos embedded, not linked.** A standalone `[Video](youtube…)` becomes a responsive
+      16:9 `youtube-nocookie` iframe (lazy-loaded). 8 pages.
+- [x] **Model-card header centred.** Upstream wraps the banner/links/license block in
+      `<p align="center">`, which the export drops. Restored as `.model-card-header`.
+- [x] **Code inside list items stays inside.** Fences were emitted at column 0, breaking out of
+      the list so everything after started a new one — `embeddinggemma/inference.md`'s RAG
+      section. Blocks are now dedented by their own indent and the fence indented to its item.
+- [x] **Nested lists tightened.** A blank line between a bullet and its own sub-list made
+      Markdown render it "loose", wrapping items in `<p>` inconsistently.
+- [x] **`> NOTE:` blockquotes → admonitions.** 4 in functiongemma pages. The `**Note:**` lines in
+      `capabilities/thinking.md` are inside model-output blocks and were correctly left verbatim.
+- [x] **Gemma 1/2/3 model cards reference themselves.** Upstream's "Model Page: Gemma → /core"
+      resolved to Gemma 4 here, so the Gemma 2 card advertised Gemma 4 as its model page.
+      New `[page_overrides]` in `link_map.toml` handles per-page mappings.
+- [x] `docs/stylesheets/components.css` added (video embeds, model-card header) — the first
+      piece of the Phase 1 modular stylesheet layout.
+
+**The fidelity gate caught these transformations and blocked 8 pages** until
+`check_fidelity.py` was taught that video URLs and empty `[](url)` duplicates are markup, not
+prose. Working as designed — a transformation must be declared before it can land.
+
 ## Pipeline order
 
 Steps that rewrite pages must run in this order, because each stage can undo the previous one:
diff --git a/docs/get-started/setup.md b/docs/get-started/setup.md
index 046161c..7acd9cc 100644
--- a/docs/get-started/setup.md
+++ b/docs/get-started/setup.md
@@ -39,9 +39,9 @@ generate and configure these values, follow these steps:
 2. Open `kaggle.json` in a text editor. The contents should look something like
    this:
 
-```text
+   ```text
    {"username":"your_username","key":"012345678abcdef012345678abcdef1a"}
-```
+   ```
 
 3. In Colab, select **Secrets** (🔑) and add your Kaggle username and Kaggle
    API key. Store your username under the name `KAGGLE_USERNAME` and your API
diff --git a/docs/learn/business-email-assistant.md b/docs/learn/business-email-assistant.md
index e8a2d50..c16376b 100644
--- a/docs/learn/business-email-assistant.md
+++ b/docs/learn/business-email-assistant.md
@@ -1,6 +1,11 @@
 # Build a business email AI assistant with Gemma
 
-[Video](https://www.youtube.com/watch?v=YxhzozLH1Dk)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/YxhzozLH1Dk"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 Handling customer inquiries, including emails, is a necessary part of running
 many businesses, but it can quickly get overwhelming. With a bit of effort,
@@ -72,10 +77,10 @@ To install the required software:
 
 - Install Python 3 and the `venv` virtual environment package for Python:
 
-```bash
+  ```bash
   sudo apt update
   sudo apt install git pip python3-venv
-```
+  ```
 
 #### Clone the project
 
@@ -87,18 +92,18 @@ To download the project code:
 
 1. Clone the git repository using the following command:
 
-```bash
+   ```bash
    git clone https://github.com/google-gemini/gemma-cookbook.git
-```
+   ```
 
 2. Optionally, configure your local git repository to use sparse checkout,
    so you have only the files for the project:
 
-```bash
+   ```bash
    cd gemma-cookbook/
    git sparse-checkout set Demos/business-email-assistant/
    git sparse-checkout init --cone
-```
+   ```
 
 #### Install Python libraries
 
@@ -112,23 +117,23 @@ To install the Python libraries:
 
 1. In a terminal window, navigate to the `business-email-assistant` directory:
 
-```bash
+   ```bash
    cd Demos/business-email-assistant/
-```
+   ```
 
 2. Configure and activate Python virtual environment (venv) for this project:
 
-```text
+   ```text
    python3 -m venv venv
    source venv/bin/activate
-```
+   ```
 
 3. Install the required Python libraries for this project using the
    `setup_python` script:
 
-```text
+   ```text
    ./setup_python.sh
-```
+   ```
 
 !!! Tip
     On Linux operating systems, you may need to allow execution of the bash script by running the command `chmod +x setup_python.sh`.
@@ -153,17 +158,17 @@ To set the environment variables:
 2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
 3. Create environment variable files for the project, by creating a `.env` text file at *each* these locations in your clone of the project:
 
-``` { .no-copy .output }
+   ``` { .no-copy .output }
    email-processing-webapp/.env
    model-tuning/.env
-```
+   ```
 4. After creating the `.env` text files, add the following settings to **both**
    files:
 
-```python
+   ```python
    KAGGLE_USERNAME=<YOUR_KAGGLE_USERNAME_HERE>
    KAGGLE_KEY=<YOUR_KAGGLE_KEY_HERE>
-```
+   ```
 
 ### Run and test the application
 
@@ -175,22 +180,22 @@ To run and test the project:
 
 1. In a terminal window, navigate to the `email-processing-webapp` directory:
 
-```bash
+   ```bash
    cd business-email-assistant/email-processing-webapp/
-```
+   ```
 
 2. Run the application using the `run_app` script:
 
-```text
+   ```text
    ./run_app.sh
-```
+   ```
 
 3. After starting the web application, the program code lists a URL where
    you can browse and test. Typically, this address is:
 
-```text
+   ```text
    http://127.0.0.1:5000/
-```
+   ```
 
 4. In the web interface, press the **Get data** button below the first input
    field to generate a response from the model.
@@ -222,12 +227,12 @@ To modify prompt instructions:
 2. In the `app.py` code, add additions instructions to the
    `get_prompt():` function:
 
-```python
+   ```python
    def get_prompt():
      return """
        Extract the relevant details of this request and return them in
        JSON code, with no additional markdown formatting:\n"""
-```
+   ```
 
 This example adds the phrase "with no additional markdown formatting" to the
 instructions.
@@ -349,15 +354,15 @@ To perform the tuning run and calculate new weights:
 
 1. In a terminal window, navigate to the `model-tuning/` directory.
 
-```bash
+   ```bash
    cd business-email-assistant/model-tuning/
-```
+   ```
 
 2. Run the tuning process using the `tune_model` script:
 
-```text
+   ```text
    ./tune_model.sh
-```
+   ```
 
 The tuning process takes several minutes depending on your available compute
 resources. When it completes successfully, the tuning program writes new `*.h5`
@@ -403,15 +408,15 @@ To deploy the generated weights to the web application:
 
 1. In a terminal window, navigate to the `model-tuning` directory:
 
-```bash
+   ```bash
    cd business-email-assistant/model-tuning/
-```
+   ```
 
 2. Run the tuning process using the `deploy_weights` script:
 
-```text
+   ```text
    ./deploy_weights.sh
-```
+   ```
 
 After running this script, you should see a new `*.h5` file in the
 `email-processing-webapp/weights/` directory.
@@ -426,22 +431,22 @@ To run and test the project:
 
 1. In a terminal window, navigate to the `email-processing-webapp` directory:
 
-```bash
+   ```bash
    cd business-email-assistant/email-processing-webapp/
-```
+   ```
 
 2. Run the application using the `run_app` script:
 
-```text
+   ```text
    ./run_app.sh
-```
+   ```
 
 3. After starting the web application, the program code lists a URL where
    you can browse and test, typically this address is:
 
-```text
+   ```text
    http://127.0.0.1:5000/
-```
+   ```
 
 4. In the web interface, press the **Get data** button below the first input
    field to generate a response from the model.
diff --git a/docs/learn/personal-code-assistant.md b/docs/learn/personal-code-assistant.md
index 8684105..2ec4eec 100644
--- a/docs/learn/personal-code-assistant.md
+++ b/docs/learn/personal-code-assistant.md
@@ -1,6 +1,11 @@
 # Build a personal AI coding assistant with Gemma
 
-[Video](https://www.youtube.com/watch?v=Zpo7UTvg_9E)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/Zpo7UTvg_9E"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 Getting code assistance from artificial intelligence (AI) models can be very
 useful, but what if you are restricted from using third-party, hosted generative
@@ -67,10 +72,10 @@ To install the required software:
 - Install Python 3, the virtual environment (`venv`)package for Python,
   Node.js, and the Node.js package manager (`npm`):
 
-```bash
+  ```bash
   sudo apt update
   sudo apt install git pip python3-venv nodejs npm
-```
+  ```
 
 #### Clone the project
 
@@ -82,18 +87,18 @@ To download the project code:
 
 1. Clone the git repository using the following command:
 
-```bash
+   ```bash
    git clone https://github.com/google-gemini/gemma-cookbook.git
-```
+   ```
 
 2. Optional: Configure your local git repository to use sparse checkout,
    so you have only the files for the project:
 
-```bash
+   ```bash
    cd gemma-cookbook/
    git sparse-checkout set Demos/personal-code-assistant/
    git sparse-checkout init --cone
-```
+   ```
 
 ## Gemma web service project
 
@@ -142,23 +147,23 @@ To install the Python libraries:
 
 1. In a terminal window, navigate to the `gemma-web-service` directory:
 
-```bash
+   ```bash
    cd Demos/personal-code-assistant/gemma-web-service/
-```
+   ```
 
 2. Configure and activate a Python virtual environment (venv) for this project:
 
-```text
+   ```text
    python3 -m venv venv
    source venv/bin/activate
-```
+   ```
 
 3. Install the required Python libraries for this project using the
    `setup_python` script:
 
-```text
+   ```text
    ./setup_python.sh
-```
+   ```
 
 !!! Tip
     On Linux operating systems, you may need to allow execution of the bash script by running the command `chmod +x setup_python.sh`.
@@ -183,16 +188,16 @@ To set the environment variables:
 3. Create an environment variable file for the project, by creating a
    `.env` text file at this location in your clone of the project:
 
-```text
+   ```text
    personal-code-assistant/gemma-web-service/.env
-```
+   ```
 
 4. After creating the `.env` text file, add the following settings to it:
 
-```python
+   ```python
    KAGGLE_USERNAME=<YOUR_KAGGLE_USERNAME_HERE>
    KAGGLE_KEY=<YOUR_KAGGLE_KEY_HERE>
-```
+   ```
 
 ### Run and test the web service
 
@@ -204,28 +209,28 @@ To run and test the project:
 
 1. In a terminal window, navigate to the `gemma-web-service` directory:
 
-```bash
+   ```bash
    cd personal-code-assistant/gemma-web-service/
-```
+   ```
 
 2. Run the application using the `run_service` script:
 
-```text
+   ```text
    ./run_service.sh
-```
+   ```
 
 3. After starting the web service, the program code lists a URL where
    you can access the service. Typically, this address is:
 
-```text
+   ```text
    http://localhost:8000/
-```
+   ```
 
 4. Test the service by running the `test_post` script:
 
-```text
+   ```text
    ./test/test_post.sh
-```
+   ```
 
 When you have successfully run and tested the service with this script, you
 should be ready to connect to it with the VS Code extension in the next section
@@ -273,15 +278,15 @@ To configure the project code:
 
 1. Navigate to the Pipet Code Agent project root directory.
 
-```bash
+   ```bash
    cd Demos/personal-code-assistant/pipet-code-agent-2/
-```
+   ```
 
 2. Run the install command to download dependencies and configure the project:
 
-```bash
+   ```bash
    npm install
-```
+   ```
 
 #### Configure the extension
 
@@ -309,9 +314,9 @@ To configure and test your setup:
    settings.json** link, and add the host address such as `127.0.0.1`,
    `localhost`, or `my-server.my-local-domain.com`:
 
-```text
+   ```text
    "gemma.service.host": "your-host-address-here"
-```
+   ```
 
 8. Save the changes to the `settings.json` file and close the settings tabs.
 
@@ -351,7 +356,7 @@ To modify the behavior of the `review.ts` command:
    `PROMPT_INSTRUCTIONS` constant to add `Also note potential
    performance improvements`.
 
-```python
+   ```python
    const PROMPT_INSTRUCTIONS = `
    Reviewing code involves finding bugs and increasing code quality. Examples of
    bugs are syntax errors or typos, out of memory errors, and boundary value
@@ -361,7 +366,7 @@ To modify the behavior of the `review.ts` command:
 
    Write a review of the following code:
    `;
-```
+   ```
 
 2. Save the changes to the `review.ts` file.
 
@@ -393,7 +398,7 @@ To create a command that recommends names for functions:
 3. Change the prompt instructions in the new file by
    editing the `PROMPT_INSTRUCTIONS` value.
 
-```python
+   ```python
    // Provide instructions for the AI model
    const PROMPT_INSTRUCTIONS = `
    Build a Python web API service using FastAPI and uvicorn.
@@ -402,12 +407,12 @@ To create a command that recommends names for functions:
     - Do not include a '@app.get("/")' statement
     - Do not include a '@app.get("/info")' statement
    `;
-```
+   ```
 
 4. Add the service boilerplate by creating a new
    `BOILERPLATE_CODE` constant.
 
-```python
+   ```python
    const BOILERPLATE_CODE = `
    # the following code for testing and diagnosis:
    @app.get("/")
@@ -423,35 +428,35 @@ To create a command that recommends names for functions:
        # host setting makes service available to other devices
        uvicorn.run(app, host="0.0.0.0", port=8000)
    `;
-```
+   ```
 
 5. Change the name of the command function to `newService()`
    and update its information message.
 
-```python
+   ```python
    export async function newService() {
      vscode.window.showInformationMessage('Building new service from template...');
    ...
-```
+   ```
 
 6. Update the prompt assembly code to include the text selected in the editor
    and the `PROMPT_INSTRUCTIONS`.
 
-```python
+   ```python
    // Build the full prompt using the template.
      const promptText = `${selectedCode}${PROMPT_INSTRUCTIONS}`;
-```
+   ```
 
 7. Change the response insert code to include the response and the
    boilerplate code.
 
-```python
+   ```python
    // Insert answer after selection.
    editor.edit((editBuilder) => {
        editBuilder.insert(selection.end, "\n\n" + responseText);
        editBuilder.insert(selection.end, "\n" + BOILERPLATE_CODE);
    });
-```
+   ```
 
 8. Save the changes to the `new-service.ts` file.
 
@@ -468,19 +473,19 @@ To integrate the `new-service` command with the extension code:
 2. Add the new code file to the extension by adding a new import
    statement.
 
-```text
+   ```text
    import { newService } from './new-service';
-```
+   ```
 
 3. Register the new command by adding the following code to the
    `activate()` function.
 
-```python
+   ```python
    export function activate(context: vscode.ExtensionContext) {
        ...
        vscode.commands.registerCommand('pipet-code-agent.newService', newService);
    }
-```
+   ```
 
 4. Save the changes to the `extension.ts` file.
 
@@ -489,7 +494,7 @@ To integrate the `name` command with the extension package:
 1. In VS Code, open the `pipet-code-agent/package.json` file.
 2. Add the new command to the `commands` section of the package file.
 
-```text
+   ```text
    "contributes": {
      "commands": [
        ...
@@ -498,7 +503,7 @@ To integrate the `name` command with the extension package:
          "title": "Pipet: Generate a FastAPI service."
        }
      ],
-```
+   ```
 
 3. Save the changes to the `package.json` file.
 
diff --git a/docs/models/datagemma/index.md b/docs/models/datagemma/index.md
index 4085a37..adf8c86 100644
--- a/docs/models/datagemma/index.md
+++ b/docs/models/datagemma/index.md
@@ -21,16 +21,8 @@ Commons statistical data:
 For more research and technical details on DataGemma, see the
 [DataGemma technical paper](http://datacommons.org/link/DataGemmaPaper).
 
--
-
-  #### Generate answers with real data
-
-  Apply generative artificial intelligence (AI) to a vast repository of public statistical data to explore and uncover new insights.
--
-
-  #### Evaluate AI data grounding techniques
-
-  Investigate ways to guide generative AI model output with retrieval-augmented and data-interleaved techniques.
+- **Generate answers with real data** — Apply generative artificial intelligence (AI) to a vast repository of public statistical data to explore and uncover new insights.
+- **Evaluate AI data grounding techniques** — Investigate ways to guide generative AI model output with retrieval-augmented and data-interleaved techniques.
 
 ## Learn more
 
diff --git a/docs/models/diffusiongemma/inference.md b/docs/models/diffusiongemma/inference.md
index 0b69edc..9e7b9dc 100644
--- a/docs/models/diffusiongemma/inference.md
+++ b/docs/models/diffusiongemma/inference.md
@@ -159,8 +159,6 @@ While sunlight looks white to us, it is actually made up of all the colors of th
 
 *   **Red light** has long,, waves waves.
 *   Blue and and violet**** have much, choppy choppy choppy
-
-
     22. atmosphere atmosphere atmosphere a a
 
 
@@ -523,10 +521,8 @@ What is the water formula?<turn|>
 
 *   User asking: "What is the water formula?"
     *   Goal: Provide the chemical formula for water and explain what it means.
-
     *   Water is a compound made of hydrogen and oxygen.
     *   The formula is \\(H_2O\\).
-
     *   Direct answer: \\(H_2O\\).
     *   Explanation of components:
         *   \\(H_2\\): Two atoms of hydrogen.
diff --git a/docs/models/diffusiongemma/model-card.md b/docs/models/diffusiongemma/model-card.md
index 1f3e296..b407d19 100644
--- a/docs/models/diffusiongemma/model-card.md
+++ b/docs/models/diffusiongemma/model-card.md
@@ -128,8 +128,10 @@ tokens:
 - **Standard Generation:** When thinking is enabled, the model will output its internal reasoning followed by the final answer using this structure: `<|channel>thought\n`**\[Internal reasoning\]** `<channel|>`.
 - **Disabled Thinking Behavior:** If thinking is disabled, the model will still generate the tags but with an empty thought block: `<|channel>thought\n<channel|>`**\[Final answer\]**.
 
-> Note that many libraries like transformers handle the complexities of the chat
-> template for you.
+!!! Note
+    Note that many libraries like transformers handle the complexities of the chat
+    template for you.
+
 
 ### 3. Multi-Turn Conversations
 
diff --git a/docs/models/embeddinggemma/fine-tuning.md b/docs/models/embeddinggemma/fine-tuning.md
index 6268cba..3cc6a95 100644
--- a/docs/models/embeddinggemma/fine-tuning.md
+++ b/docs/models/embeddinggemma/fine-tuning.md
@@ -224,15 +224,15 @@ The same search now yields much clearer results:
 2. Document: Opening a Regular Saving Account (Score: 0.34) \<- *Clearly less relevant*
 3. Document: Home Loan Application Guide (Score: 0.47)
 
-```python
-get_scores(query, documents)
-```
-
-``` { .no-copy .output }
-Document:  Opening a NISA Account -> 🤖 Score:  0.73378766
-Document:  Opening a Regular Savings Account -> 🤖 Score:  0.34055778
-Document:  Home Loan Application Guide -> 🤖 Score:  0.47503752
-```
+   ```python
+   get_scores(query, documents)
+   ```
+
+   ``` { .no-copy .output }
+   Document:  Opening a NISA Account -> 🤖 Score:  0.73378766
+   Document:  Opening a Regular Savings Account -> 🤖 Score:  0.34055778
+   Document:  Home Loan Application Guide -> 🤖 Score:  0.47503752
+   ```
 
 To upload your model to the Hugging Face Hub, you can use the `push_to_hub` method from the Sentence Transformers library.
 
diff --git a/docs/models/embeddinggemma/index.md b/docs/models/embeddinggemma/index.md
index 8ac3511..315bff9 100644
--- a/docs/models/embeddinggemma/index.md
+++ b/docs/models/embeddinggemma/index.md
@@ -1,6 +1,11 @@
 # EmbeddingGemma model overview
 
-[Video](https://www.youtube.com/watch?v=Xu1X-J-r5Xk)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/Xu1X-J-r5Xk"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 EmbeddingGemma is a 308M parameter multilingual text embedding model based on
 Gemma 3. It is optimized for use in everyday devices, such as phones, laptops,
diff --git a/docs/models/embeddinggemma/inference.md b/docs/models/embeddinggemma/inference.md
index 5f390b1..7f780a0 100644
--- a/docs/models/embeddinggemma/inference.md
+++ b/docs/models/embeddinggemma/inference.md
@@ -97,43 +97,40 @@ The key isn't the individual numbers themselves, but **the distance between the
 In this section, we use embeddings to determine how sementically similar different sentences are. Here we show examples with high, medieum, and low similarity scores.
 
 - High Similarity:
-
     - Sentence A: "The chef prepared a delicious meal for the guests."
     - Sentence B: "A tasty dinner was cooked by the chef for the visitors."
     - Reasoning: Both sentences describe the same event using different words and grammatical structures (active vs. passive voice). They convey the same core meaning.
 - Medium Similarity:
-
     - Sentence A: "She is an expert in machine learning."
     - Sentence B: "He has a deep interest in artificial intelligence."
     - Reasoning: The sentences are related as machine learning is a subfield of artificial intelligence. However, they talk about different people with different levels of engagement (expert vs. interest).
 - Low Similarity:
-
     - Sentence A: "The weather in Tokyo is sunny today."
     - Sentence B: "I need to buy groceries for the week."
     - Reasoning: The two sentences are on completely unrelated topics and share no semantic overlap.
 
-```python
-# The sentences to encode
-sentence_high = [
-    "The chef prepared a delicious meal for the guests.",
-    "A tasty dinner was cooked by the chef for the visitors."
-]
-sentence_medium = [
-    "She is an expert in machine learning.",
-    "He has a deep interest in artificial intelligence."
-]
-sentence_low = [
-    "The weather in Tokyo is sunny today.",
-    "I need to buy groceries for the week."
-]
-
-for sentence in [sentence_high, sentence_medium, sentence_low]:
-  print("🙋‍♂️")
-  print(sentence)
-  embeddings = model.encode(sentence)
-  similarities = model.similarity(embeddings[0], embeddings[1])
-  print("`-> 🤖 score: ", similarities.numpy()[0][0])
-```
+      ```python
+      # The sentences to encode
+      sentence_high = [
+          "The chef prepared a delicious meal for the guests.",
+          "A tasty dinner was cooked by the chef for the visitors."
+      ]
+      sentence_medium = [
+          "She is an expert in machine learning.",
+          "He has a deep interest in artificial intelligence."
+      ]
+      sentence_low = [
+          "The weather in Tokyo is sunny today.",
+          "I need to buy groceries for the week."
+      ]
+
+      for sentence in [sentence_high, sentence_medium, sentence_low]:
+        print("🙋‍♂️")
+        print(sentence)
+        embeddings = model.encode(sentence)
+        similarities = model.similarity(embeddings[0], embeddings[1])
+        print("`-> 🤖 score: ", similarities.numpy()[0][0])
+      ```
 
 ``` { .no-copy .output }
 🙋‍♂️
@@ -159,68 +156,68 @@ You can apply a prompt during inference in three ways.
 
    Pass the full prompt string directly to the `encode` method. This gives you precise control.
 
-```python
+   ```python
    embeddings = model.encode(
        sentence,
        prompt="task: sentence similarity | query: "
    )
-```
+   ```
 
 2. **Using the `prompt_name` argument**   
 
    Select a predefined prompt by its name. These prompts are loaded from the model's configuration or during its initialization.
 
-```python
+   ```python
    embeddings = model.encode(sentence, prompt_name="STS")
-```
+   ```
 
 3. **Using the Default Prompt**   
 
    If you don't specify either `prompt` or `prompt_name`, the system will automatically use the prompt set as `default_prompt_name`, if no default is set, then no prompt is applied.
 
-```python
-   embeddings = model.encode(sentence)
-
-print("Available tasks:")
-for name, prefix in model.prompts.items():
-  print(f" {name}: \"{prefix}\"")
-print("-"*80)
-
-for sentence in [sentence_high, sentence_medium, sentence_low]:
-  print("🙋‍♂️")
-  print(sentence)
-  embeddings = model.encode(sentence, prompt_name="STS")
-  similarities = model.similarity(embeddings[0], embeddings[1])
-  print("`-> 🤖 score: ", similarities.numpy()[0][0])
-```
-
-``` { .no-copy .output }
-Available tasks:
- query: "task: search result | query: "
- document: "title: none | text: "
- BitextMining: "task: search result | query: "
- Clustering: "task: clustering | query: "
- Classification: "task: classification | query: "
- InstructionRetrieval: "task: code retrieval | query: "
- MultilabelClassification: "task: classification | query: "
- PairClassification: "task: sentence similarity | query: "
- Reranking: "task: search result | query: "
- Retrieval: "task: search result | query: "
- Retrieval-query: "task: search result | query: "
- Retrieval-document: "title: none | text: "
- STS: "task: sentence similarity | query: "
- Summarization: "task: summarization | query: "
----
-🙋‍♂️
-['The chef prepared a delicious meal for the guests.', 'A tasty dinner was cooked by the chef for the visitors.']
-`-> 🤖 score:  0.9363755
-🙋‍♂️
-['She is an expert in machine learning.', 'He has a deep interest in artificial intelligence.']
-`-> 🤖 score:  0.6425841
-🙋‍♂️
-['The weather in Tokyo is sunny today.', 'I need to buy groceries for the week.']
-`-> 🤖 score:  0.38587403
-```
+   ```python
+      embeddings = model.encode(sentence)
+
+   print("Available tasks:")
+   for name, prefix in model.prompts.items():
+     print(f" {name}: \"{prefix}\"")
+   print("-"*80)
+
+   for sentence in [sentence_high, sentence_medium, sentence_low]:
+     print("🙋‍♂️")
+     print(sentence)
+     embeddings = model.encode(sentence, prompt_name="STS")
+     similarities = model.similarity(embeddings[0], embeddings[1])
+     print("`-> 🤖 score: ", similarities.numpy()[0][0])
+   ```
+
+   ``` { .no-copy .output }
+   Available tasks:
+    query: "task: search result | query: "
+    document: "title: none | text: "
+    BitextMining: "task: search result | query: "
+    Clustering: "task: clustering | query: "
+    Classification: "task: classification | query: "
+    InstructionRetrieval: "task: code retrieval | query: "
+    MultilabelClassification: "task: classification | query: "
+    PairClassification: "task: sentence similarity | query: "
+    Reranking: "task: search result | query: "
+    Retrieval: "task: search result | query: "
+    Retrieval-query: "task: search result | query: "
+    Retrieval-document: "title: none | text: "
+    STS: "task: sentence similarity | query: "
+    Summarization: "task: summarization | query: "
+   ---
+   🙋‍♂️
+   ['The chef prepared a delicious meal for the guests.', 'A tasty dinner was cooked by the chef for the visitors.']
+   `-> 🤖 score:  0.9363755
+   🙋‍♂️
+   ['She is an expert in machine learning.', 'He has a deep interest in artificial intelligence.']
+   `-> 🤖 score:  0.6425841
+   🙋‍♂️
+   ['The weather in Tokyo is sunny today.', 'I need to buy groceries for the week.']
+   `-> 🤖 score:  0.38587403
+   ```
 
 #### Use Case: Retrieval-Augmented Generation (RAG)
 
@@ -228,32 +225,31 @@ For RAG systems, use the following `prompt_name` values to create specialized em
 
 - **For Queries:** Use `prompt_name="Retrieval-query"`.  
 
-```python
+  ```python
   query_embedding = model.encode(
       "How do I use prompts with this model?",
       prompt_name="Retrieval-query"
   )
-```
+  ```
 
 - **For Documents:** Use `prompt_name="Retrieval-document"`. To further improve document embeddings, you can also include a title by using the `prompt` argument directly:  
-
     - **With a title:**   
 
-```python
-  doc_embedding = model.encode(
-      "The document text...",
-      prompt="title: Using Prompts in RAG | text: "
-  )
-```
+      ```python
+      doc_embedding = model.encode(
+          "The document text...",
+          prompt="title: Using Prompts in RAG | text: "
+      )
+      ```
 
     - **Without a title:**   
 
-```python
-  doc_embedding = model.encode(
-      "The document text...",
-      prompt="title: none | text: "
-  )
-```
+      ```python
+      doc_embedding = model.encode(
+          "The document text...",
+          prompt="title: none | text: "
+      )
+      ```
 
 #### Further Reading
 
diff --git a/docs/models/embeddinggemma/model-card.md b/docs/models/embeddinggemma/model-card.md
index e9ce990..33e7d35 100644
--- a/docs/models/embeddinggemma/model-card.md
+++ b/docs/models/embeddinggemma/model-card.md
@@ -37,11 +37,9 @@ For more technical details, refer to our paper:
 ### Inputs and outputs
 
 - **Input:**
-
     - Text string, such as a question, a prompt, or a document to be embedded
     - Maximum input context length of 2K
 - **Output:**
-
     - Numerical vector representations of input text data
     - Output embedding dimension size of 768, with smaller options available (512, 256, or 128) via Matryoshka Representation Learning (MRL). MRL allows users to truncate the output embedding of size 768 to their desired size and then re-normalize for efficient and accurate representation.
 
@@ -203,7 +201,6 @@ training and development.
 - **Classification**: Embeddings optimized to classify texts according to preset labels, such as sentiment analysis and spam detection
 - **Clustering**: Embeddings optimized to cluster texts based on their similarities, such as document organization, market research, and anomaly detection
 - **Retrieval**
-
     - **Document**: Embeddings optimized for document search, such as indexing articles, books, or web pages for search
     - **Query**: Embeddings optimized for general search queries, such as custom search
     - **Code Query**: Embeddings optimized for retrieval of code blocks based on natural language queries, such as code suggestions and search
@@ -218,11 +215,9 @@ training and development.
 ### Limitations
 
 - Training Data
-
     - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses.
     - The scope of the training dataset determines the subject areas the model can handle effectively.
 - Language Ambiguity and Nuance
-
     - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language.
 
 ### Ethical Considerations and Risks
diff --git a/docs/models/functiongemma/fine-tuning.md b/docs/models/functiongemma/fine-tuning.md
index 6f73e64..b11a2dd 100644
--- a/docs/models/functiongemma/fine-tuning.md
+++ b/docs/models/functiongemma/fine-tuning.md
@@ -31,7 +31,9 @@ The first step is to install Hugging Face Libraries, including TRL, and datasets
 #% pip install flash-attn
 ```
 
-> *Note: If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to accelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*
+!!! Note
+    If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to accelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*
+
 
 Before you can start training, you have to make sure that you accepted the terms of use for Gemma. You can accept the license on [Hugging Face](http://huggingface.co/google/functiongemma-270m-it) by clicking on the **Agree** and access repository button on the model page at: <http://huggingface.co/google/functiongemma-270m-it>
 
diff --git a/docs/models/functiongemma/formatting.md b/docs/models/functiongemma/formatting.md
index a807de3..0185874 100644
--- a/docs/models/functiongemma/formatting.md
+++ b/docs/models/functiongemma/formatting.md
@@ -30,8 +30,10 @@ lifecycle.
 | `<start_function_call>` `<end_function_call>` | Indicates a model's request to use a tool. |
 | `<start_function_response>` `<end_function_response>` | Provides a tool's result back to the model. |
 
-> NOTE: `<start_function_response>` is an additional stop sequence for the
-> inference engine.
+!!! Note
+    `<start_function_response>` is an additional stop sequence for the
+    inference engine.
+
 
 ### Delimiter for String Values: `<escape>`
 
@@ -66,9 +68,11 @@ each other.
 
 The model has **not** been explicitly trained on **Multi-Turn** or
 **Multi-Step** workflows.
-> **Note:** We expect the model to be able to generalize a bit to these
-> scenarios, especially if fine-tuned on specific use cases, but it has not been
-> trained to perform these tasks out of the box.
+!!! Note
+    We expect the model to be able to generalize a bit to these
+    scenarios, especially if fine-tuned on specific use cases, but it has not been
+    trained to perform these tasks out of the box.
+
 
 - **Multi-Step (Chaining):** Scenarios where the output of one tool is
   required as the argument for a subsequent tool.
diff --git a/docs/models/functiongemma/index.md b/docs/models/functiongemma/index.md
index d0017ad..3178fb9 100644
--- a/docs/models/functiongemma/index.md
+++ b/docs/models/functiongemma/index.md
@@ -1,6 +1,11 @@
 # FunctionGemma model overview
 
-[Video](https://www.youtube.com/watch?v=-Tgc_9uYJLI)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/-Tgc_9uYJLI"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 FunctionGemma is a specialized version of our Gemma 3 270M model tuned for
 function calling. It is designed as a strong base for further training into
diff --git a/docs/models/functiongemma/model-card.md b/docs/models/functiongemma/model-card.md
index 6c16e7a..d27b7a2 100644
--- a/docs/models/functiongemma/model-card.md
+++ b/docs/models/functiongemma/model-card.md
@@ -18,7 +18,9 @@ Summary description and brief definition of inputs and outputs.
 
 ### Description
 
-> NOTE: FunctionGemma is intended to be fine-tuned for your specific function-calling task, including multi-turn use cases.
+!!! Note
+    FunctionGemma is intended to be fine-tuned for your specific function-calling task, including multi-turn use cases.
+
 
 FunctionGemma is a lightweight, open model from Google, built as a foundation
 for creating your own specialized function calling models. FunctionGemma is not
diff --git a/docs/models/gemma-1/index.md b/docs/models/gemma-1/index.md
index 4e06feb..5499857 100644
--- a/docs/models/gemma-1/index.md
+++ b/docs/models/gemma-1/index.md
@@ -1,6 +1,6 @@
 # Gemma model card
 
-**Model Page** : [Gemma](../gemma-4/index.md)
+**Model Page** : [Gemma](index.md)
 
 **Resources and Technical Documentation**:
 
diff --git a/docs/models/gemma-2/index.md b/docs/models/gemma-2/index.md
index e70ea46..ef876ed 100644
--- a/docs/models/gemma-2/index.md
+++ b/docs/models/gemma-2/index.md
@@ -1,6 +1,6 @@
 # Gemma 2 model card
 
-**Model Page** : [Gemma](../gemma-4/index.md)
+**Model Page** : [Gemma](index.md)
 
 **Resources and Technical Documentation**:
 
diff --git a/docs/models/gemma-3/index.md b/docs/models/gemma-3/index.md
index a4afb92..ee0ddea 100644
--- a/docs/models/gemma-3/index.md
+++ b/docs/models/gemma-3/index.md
@@ -1,6 +1,6 @@
 # Gemma 3 model card
 
-**Model Page** : [Gemma](../gemma-4/index.md)
+**Model Page** : [Gemma](index.md)
 
 **Resources and Technical Documentation**:
 
@@ -35,12 +35,10 @@ for everyone.
 ### Inputs and outputs
 
 - **Input:**
-
     - Text string, such as a question, a prompt, or a document to be summarized
     - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
     - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B and 270M sizes.
 - **Output:**
-
     - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document
     - Total output context up to 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B and 270M sizes per request, subtracting the request input tokens
 
diff --git a/docs/models/gemma-3n/index.md b/docs/models/gemma-3n/index.md
index b116f30..8a2f5be 100644
--- a/docs/models/gemma-3n/index.md
+++ b/docs/models/gemma-3n/index.md
@@ -1,6 +1,11 @@
 # Gemma 3n model overview
 
-[Video](https://www.youtube.com/watch?v=eJFJRyXEHZ0)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/eJFJRyXEHZ0"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 Gemma 3n is a generative AI model optimized for use in everyday devices, such as
 phones, laptops, and tablets. This model includes innovations in
diff --git a/docs/models/gemma-4/index.md b/docs/models/gemma-4/index.md
index 00e9f1a..71972e3 100644
--- a/docs/models/gemma-4/index.md
+++ b/docs/models/gemma-4/index.md
@@ -1,4 +1,9 @@
-[Video](https://www.youtube.com/watch?v=jZVBoFOJK-Q)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/jZVBoFOJK-Q"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 Gemma is a family of generative artificial intelligence models and you can
 use them in a wide variety of generation tasks, including question answering,
diff --git a/docs/models/gemma-4/model-card.md b/docs/models/gemma-4/model-card.md
index 8b64af8..a107427 100644
--- a/docs/models/gemma-4/model-card.md
+++ b/docs/models/gemma-4/model-card.md
@@ -1,5 +1,6 @@
-![Gemma 4 Banner](../../assets/gemma-4/gemma4_banner.png)
+<div class="model-card-header" markdown>
 
+![Gemma 4 Banner](../../assets/gemma-4/gemma4_banner.png)
 
 [Hugging Face](https://huggingface.co/collections/google/gemma-4) \|
 [GitHub](https://github.com/google-gemma) \|
@@ -7,9 +8,10 @@
 [Documentation](index.md) \|
 [Technical Report](https://arxiv.org/abs/2607.02770)
 
-
 **License** : [Apache 2.0](https://ai.google.dev/gemma/apache_2) \| **Authors** : [Google DeepMind](https://deepmind.google/models/gemma/)
 
+</div>
+
 Gemma is a family of open models built by Google DeepMind. Gemma 4 models are
 multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B models)
 and generating text output. This release includes open-weights models in both
@@ -180,8 +182,10 @@ tokens:
 - **Disabled Thinking Behavior:** For all models except for the E2B and E4B variants, if thinking is disabled, the model will still generate the tags but with an empty thought block: `<|channel>thought\n<channel|>`**\[Final
   answer\]**
 
-> Note that many libraries like Transformers and llama.cpp handle the
-> complexities of the chat template for you.
+!!! Note
+    Note that many libraries like Transformers and llama.cpp handle the
+    complexities of the chat template for you.
+
 
 ### 3. Multi-Turn Conversations
 
@@ -212,20 +216,20 @@ Use the following prompt structures for audio processing:
 
 - **Audio Speech Recognition (ASR)**
 
-```text
-Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
+  ```text
+  Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.
 
-Follow these specific instructions for formatting the answer:
-*   Only output the transcription, with no newlines.
-*   When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.
-```
+  Follow these specific instructions for formatting the answer:
+    *   Only output the transcription, with no newlines.
+    *   When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.
+  ```
 
 - **Automatic Speech Translation (AST)**
 
-```text
-Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.
-When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.
-```
+  ```text
+  Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.
+  When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.
+  ```
 
 ### 7. Audio and Video Length
 
diff --git a/docs/models/gemma-aps/index.md b/docs/models/gemma-aps/index.md
index 2f6964c..4e0214c 100644
--- a/docs/models/gemma-aps/index.md
+++ b/docs/models/gemma-aps/index.md
@@ -17,16 +17,8 @@ where it is useful to divide up individual factual data so that they can be veri
 independently. For more information, check out the
 [research paper](https://arxiv.org/pdf/2406.19803).
 
--
-
-  #### Analyze content for component parts
-
-  Apply generative artificial intelligence (AI) to break down content into discrete statements for analysis or further processing.
--
-
-  #### Combine with larger content analysis
-
-  Use generated content segments in larger content analysis projects such as grounding, retrieval, fact-checking, evaluation of generation tasks, and duplicate content detection.
+- **Analyze content for component parts** — Apply generative artificial intelligence (AI) to break down content into discrete statements for analysis or further processing.
+- **Combine with larger content analysis** — Use generated content segments in larger content analysis projects such as grounding, retrieval, fact-checking, evaluation of generation tasks, and duplicate content detection.
 
 ## Learn more
 
diff --git a/docs/models/gemma-scope/index.md b/docs/models/gemma-scope/index.md
index c8da209..54fa10d 100644
--- a/docs/models/gemma-scope/index.md
+++ b/docs/models/gemma-scope/index.md
@@ -21,16 +21,8 @@ and **Transcoders** trained on every layer of the Gemma 3 family.
 The original [Gemma Scope (for Gemma 2)](https://deepmind.google/discover/blog/gemma-scope-helping-the-safety-community-shed-light-on-the-inner-workings-of-language-models/)
 remains available for researchers working with the Gemma 2 family of models.
 
--
-
-  #### Model behavior evaluation
-
-  Use SAEs and Transcoders to analyze complex internal behaviors and multi-step algorithms in Gemma 3.
--
-
-  #### Chatbot safety \& debugging
-
-  Analyze specific chat behaviors, refusal mechanisms, and chain-of-thought faithfulness to build safer AI agents.
+- **Model behavior evaluation** — Use SAEs and Transcoders to analyze complex internal behaviors and multi-step algorithms in Gemma 3.
+- **Chatbot safety \& debugging** — Analyze specific chat behaviors, refusal mechanisms, and chain-of-thought faithfulness to build safer AI agents.
 
 ## Learn more
 
diff --git a/docs/models/paligemma/fine-tuning.md b/docs/models/paligemma/fine-tuning.md
index c7731fb..c41f0dd 100644
--- a/docs/models/paligemma/fine-tuning.md
+++ b/docs/models/paligemma/fine-tuning.md
@@ -360,54 +360,54 @@ The code below defines three functions that you'll use in the next step create t
 - **`preprocess_tokens`:** Splits the tokens up and adds flags to mark whether a token is a prefix or suffix token. These flags will be used later on in the code, during the training step and the evaluation loop.
 - **`postprocess_tokens`:** Removes any tokens left at and/or after the end-of-sequence (EOS) token and returns the remaining decoded tokens.
 
-```python
-def preprocess_image(image, size=224):
-  # Model has been trained to handle images of different aspects ratios
-  # resized to 224x224 in the range [-1, 1]. Bilinear and antialias resize
-  # options are helpful to improve quality in some tasks.
-  image = np.asarray(image)
-  if image.ndim == 2:  # Convert image without last channel into greyscale.
-    image = np.stack((image,)*3, axis=-1)
-  image = image[..., :3]  # Remove alpha layer.
-  assert image.shape[-1] == 3
-
-  image = tf.constant(image)
-  image = tf.image.resize(image, (size, size), method='bilinear', antialias=True)
-  return image.numpy() / 127.5 - 1.0  # [0, 255]->[-1,1]
-
-def preprocess_tokens(prefix, suffix=None, seqlen=None):
-  # Model has been trained to handle tokenized text composed of a prefix with
-  # full attention and a suffix with causal attention.
-  separator = "\n"
-  tokens = tokenizer.encode(prefix, add_bos=True) + tokenizer.encode(separator)
-  mask_ar = [0] * len(tokens)    # 0 to use full attention for prefix.
-  mask_loss = [0] * len(tokens)  # 0 to not use prefix tokens in the loss.
-
-  if suffix:
-    suffix = tokenizer.encode(suffix, add_eos=True)
-    tokens += suffix
-    mask_ar += [1] * len(suffix)    # 1 to use causal attention for suffix.
-    mask_loss += [1] * len(suffix)  # 1 to use suffix tokens in the loss.
-
-  mask_input = [1] * len(tokens)    # 1 if it's a token, 0 if padding.
-  if seqlen:
-    padding = [0] * max(0, seqlen - len(tokens))
-    tokens = tokens[:seqlen] + padding
-    mask_ar = mask_ar[:seqlen] + padding
-    mask_loss = mask_loss[:seqlen] + padding
-    mask_input = mask_input[:seqlen] + padding
-
-  return jax.tree.map(np.array, (tokens, mask_ar, mask_loss, mask_input))
-
-def postprocess_tokens(tokens):
-  tokens = tokens.tolist()  # np.array to list[int]
-  try:  # Remove tokens at and after EOS if any.
-    eos_pos = tokens.index(tokenizer.eos_id())
-    tokens = tokens[:eos_pos]
-  except ValueError:
-    pass
-  return tokenizer.decode(tokens)
-```
+  ```python
+  def preprocess_image(image, size=224):
+    # Model has been trained to handle images of different aspects ratios
+    # resized to 224x224 in the range [-1, 1]. Bilinear and antialias resize
+    # options are helpful to improve quality in some tasks.
+    image = np.asarray(image)
+    if image.ndim == 2:  # Convert image without last channel into greyscale.
+      image = np.stack((image,)*3, axis=-1)
+    image = image[..., :3]  # Remove alpha layer.
+    assert image.shape[-1] == 3
+
+    image = tf.constant(image)
+    image = tf.image.resize(image, (size, size), method='bilinear', antialias=True)
+    return image.numpy() / 127.5 - 1.0  # [0, 255]->[-1,1]
+
+  def preprocess_tokens(prefix, suffix=None, seqlen=None):
+    # Model has been trained to handle tokenized text composed of a prefix with
+    # full attention and a suffix with causal attention.
+    separator = "\n"
+    tokens = tokenizer.encode(prefix, add_bos=True) + tokenizer.encode(separator)
+    mask_ar = [0] * len(tokens)    # 0 to use full attention for prefix.
+    mask_loss = [0] * len(tokens)  # 0 to not use prefix tokens in the loss.
+
+    if suffix:
+      suffix = tokenizer.encode(suffix, add_eos=True)
+      tokens += suffix
+      mask_ar += [1] * len(suffix)    # 1 to use causal attention for suffix.
+      mask_loss += [1] * len(suffix)  # 1 to use suffix tokens in the loss.
+
+    mask_input = [1] * len(tokens)    # 1 if it's a token, 0 if padding.
+    if seqlen:
+      padding = [0] * max(0, seqlen - len(tokens))
+      tokens = tokens[:seqlen] + padding
+      mask_ar = mask_ar[:seqlen] + padding
+      mask_loss = mask_loss[:seqlen] + padding
+      mask_input = mask_input[:seqlen] + padding
+
+    return jax.tree.map(np.array, (tokens, mask_ar, mask_loss, mask_input))
+
+  def postprocess_tokens(tokens):
+    tokens = tokens.tolist()  # np.array to list[int]
+    try:  # Remove tokens at and after EOS if any.
+      eos_pos = tokens.index(tokenizer.eos_id())
+      tokens = tokens[:eos_pos]
+    except ValueError:
+      pass
+    return tokenizer.decode(tokens)
+  ```
 
 ### Create the training and validation iterators
 
@@ -417,54 +417,54 @@ Create two iterators:
     - This allows you to do some data pre-processing before use
 - A **validation iterator** that allows the training process to iterate over the validation dataset to see how well the tuned model aligned with the provided results
 
-```python
-SEQLEN = 128
-
-train_dataset = big_vision.datasets.jsonl.DataSource(
-    os.path.join(DATA_DIR, "data_train90.jsonl"),
-    fopen_keys={"image": DATA_DIR})
-
-val_dataset = big_vision.datasets.jsonl.DataSource(
-    os.path.join(DATA_DIR, "data_val10.jsonl"),
-    fopen_keys={"image": DATA_DIR})
-
-
-def train_data_iterator():
-  """Never ending iterator over training examples."""
-  # Shuffle examples and repeat so one can train for many epochs.
-  dataset = train_dataset.get_tfdata().shuffle(1_000).repeat()
-  for example in dataset.as_numpy_iterator():
-    image = Image.open(io.BytesIO(example["image"]))
-    image = preprocess_image(image)
-
-    prefix = "caption en"  # Could also be a different prefix per example.
-    suffix = example["suffix"].decode().lower()
-    tokens, mask_ar, mask_loss, _ = preprocess_tokens(prefix, suffix, SEQLEN)
-
-    yield {
-        "image": np.asarray(image),
-        "text": np.asarray(tokens),
-        "mask_ar": np.asarray(mask_ar),
-        "mask_loss": np.asarray(mask_loss),
-    }
-
-
-def validation_data_iterator():
-  """Single iterator over validation examples."""
-  for example in val_dataset.get_tfdata(ordered=True).as_numpy_iterator():
-    image = Image.open(io.BytesIO(example["image"]))
-    image = preprocess_image(image)
-
-    prefix = "caption en"  # Could also be a different prefix per example.
-    tokens, mask_ar, _, mask_input = preprocess_tokens(prefix, seqlen=SEQLEN)
-
-    yield {
-        "image": np.asarray(image),
-        "text": np.asarray(tokens),
-        "mask_ar": np.asarray(mask_ar),
-        "mask_input": np.asarray(mask_input),
-    }
-```
+  ```python
+  SEQLEN = 128
+
+  train_dataset = big_vision.datasets.jsonl.DataSource(
+      os.path.join(DATA_DIR, "data_train90.jsonl"),
+      fopen_keys={"image": DATA_DIR})
+
+  val_dataset = big_vision.datasets.jsonl.DataSource(
+      os.path.join(DATA_DIR, "data_val10.jsonl"),
+      fopen_keys={"image": DATA_DIR})
+
+
+  def train_data_iterator():
+    """Never ending iterator over training examples."""
+    # Shuffle examples and repeat so one can train for many epochs.
+    dataset = train_dataset.get_tfdata().shuffle(1_000).repeat()
+    for example in dataset.as_numpy_iterator():
+      image = Image.open(io.BytesIO(example["image"]))
+      image = preprocess_image(image)
+
+      prefix = "caption en"  # Could also be a different prefix per example.
+      suffix = example["suffix"].decode().lower()
+      tokens, mask_ar, mask_loss, _ = preprocess_tokens(prefix, suffix, SEQLEN)
+
+      yield {
+          "image": np.asarray(image),
+          "text": np.asarray(tokens),
+          "mask_ar": np.asarray(mask_ar),
+          "mask_loss": np.asarray(mask_loss),
+      }
+
+
+  def validation_data_iterator():
+    """Single iterator over validation examples."""
+    for example in val_dataset.get_tfdata(ordered=True).as_numpy_iterator():
+      image = Image.open(io.BytesIO(example["image"]))
+      image = preprocess_image(image)
+
+      prefix = "caption en"  # Could also be a different prefix per example.
+      tokens, mask_ar, _, mask_input = preprocess_tokens(prefix, seqlen=SEQLEN)
+
+      yield {
+          "image": np.asarray(image),
+          "text": np.asarray(tokens),
+          "mask_ar": np.asarray(mask_ar),
+          "mask_input": np.asarray(mask_input),
+      }
+  ```
 
 ### View training examples
 
diff --git a/docs/models/paligemma/index.md b/docs/models/paligemma/index.md
index f5c7249..75760ce 100644
--- a/docs/models/paligemma/index.md
+++ b/docs/models/paligemma/index.md
@@ -40,21 +40,9 @@ There are three categories of PaliGemma models:
 
 Key benefits include:
 
--
-
-  #### Multimodal capability
-
-  Simultaneously handles both images and text input.
--
-
-  #### Versatile base model
-
-  Can be fine-tuned on a wide range of vision-language tasks.
--
-
-  #### Off-the-shelf exploration
-
-  Comes with a checkpoint fine-tuned on a mixture of tasks for immediate research use.
+- **Multimodal capability** — Simultaneously handles both images and text input.
+- **Versatile base model** — Can be fine-tuned on a wide range of vision-language tasks.
+- **Off-the-shelf exploration** — Comes with a checkpoint fine-tuned on a mixture of tasks for immediate research use.
 
 ## Learn more
 
diff --git a/docs/models/recurrentgemma/fine-tuning.md b/docs/models/recurrentgemma/fine-tuning.md
index d1461f2..6afce60 100644
--- a/docs/models/recurrentgemma/fine-tuning.md
+++ b/docs/models/recurrentgemma/fine-tuning.md
@@ -113,17 +113,17 @@ RECURRENTGEMMA_VARIANT: 2b-it
 - The `tokenizer.model` file will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1`).
 - The model checkpoint will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1/2b-it`).
 
-```python
-CKPT_PATH = os.path.join(RECURRENTGEMMA_PATH, RECURRENTGEMMA_VARIANT)
-TOKENIZER_PATH = os.path.join(RECURRENTGEMMA_PATH, 'tokenizer.model')
-print('CKPT_PATH:', CKPT_PATH)
-print('TOKENIZER_PATH:', TOKENIZER_PATH)
-```
-
-``` { .no-copy .output }
-CKPT_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/2b-it
-TOKENIZER_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/tokenizer.model
-```
+  ```python
+  CKPT_PATH = os.path.join(RECURRENTGEMMA_PATH, RECURRENTGEMMA_VARIANT)
+  TOKENIZER_PATH = os.path.join(RECURRENTGEMMA_PATH, 'tokenizer.model')
+  print('CKPT_PATH:', CKPT_PATH)
+  print('TOKENIZER_PATH:', TOKENIZER_PATH)
+  ```
+
+  ``` { .no-copy .output }
+  CKPT_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/2b-it
+  TOKENIZER_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/tokenizer.model
+  ```
 
 ## Load and prepare the MTNT dataset and the Gemma tokenizer
 
@@ -573,61 +573,61 @@ Build the `train_step` function that performs the backward pass and updates the
 - [`jax.value_and_grad`](https://jax.readthedocs.io/en/latest/_autosummary/jax.value_and_grad.html) is for evaluating the loss function and gradients during the forward and backward passes.
 - [`optax.apply_updates`](https://optax.readthedocs.io/en/latest/api/apply_updates.html#optax.apply_updates) is for updating the parameters.
 
-```python
-Params = Mapping[str, Any]
-
-def get_positions(example: jax.Array, pad_id : int) -> jax.Array:
-  """Builds the position vector from the given tokens."""
-  pad_mask = example != pad_id
-  positions = jnp.cumsum(pad_mask, axis=-1)
-  # Subtract one for all positions from the first valid one as they are
-  # 0-indexed
-  positions = positions - (positions >= 1)
-  return positions
-
-@functools.partial(
-    jax.jit,
-    static_argnames=['model', 'optimizer'],
-    donate_argnames=['params', 'opt_state'],
-)
-def train_step(
-    model: recurrentgemma.Griffin,
-    params: Params,
-    optimizer: optax.GradientTransformation,
-    opt_state: optax.OptState,
-    pad_id: int,
-    example: TrainingInput,
-) -> tuple[jax.Array, Params, optax.OptState]:
-  """The train step.
+  ```python
+  Params = Mapping[str, Any]
+
+  def get_positions(example: jax.Array, pad_id : int) -> jax.Array:
+    """Builds the position vector from the given tokens."""
+    pad_mask = example != pad_id
+    positions = jnp.cumsum(pad_mask, axis=-1)
+    # Subtract one for all positions from the first valid one as they are
+    # 0-indexed
+    positions = positions - (positions >= 1)
+    return positions
+
+  @functools.partial(
+      jax.jit,
+      static_argnames=['model', 'optimizer'],
+      donate_argnames=['params', 'opt_state'],
+  )
+  def train_step(
+      model: recurrentgemma.Griffin,
+      params: Params,
+      optimizer: optax.GradientTransformation,
+      opt_state: optax.OptState,
+      pad_id: int,
+      example: TrainingInput,
+  ) -> tuple[jax.Array, Params, optax.OptState]:
+    """The train step.
 
-  Args:
-    model: The RecurrentGemma (Griffin) model.
-    params: The model's input parameters.
-    optimizer: The Optax optimizer to use.
-    opt_state: The input optimizer's state.
-    pad_id: The ID of the pad token.
-    example: The input batch.
+    Args:
+      model: The RecurrentGemma (Griffin) model.
+      params: The model's input parameters.
+      optimizer: The Optax optimizer to use.
+      opt_state: The input optimizer's state.
+      pad_id: The ID of the pad token.
+      example: The input batch.
 
-  Returns:
-    Training loss, updated parameters, updated optimizer state.
-  """
+    Returns:
+      Training loss, updated parameters, updated optimizer state.
+    """
 
-  positions = get_positions(example.input_tokens, pad_id)
+    positions = get_positions(example.input_tokens, pad_id)
 
-  # Forward and backward passes.
-  train_loss, grads = jax.value_and_grad(forward_and_loss_fn)(
-      params,
-      model=model,
-      input_tokens=example.input_tokens,
-      input_mask=example.target_mask,
-      positions=positions,
-  )
-  # Update the parameters.
-  updates, opt_state = optimizer.update(grads, opt_state, params)
-  params = optax.apply_updates(params, updates)
+    # Forward and backward passes.
+    train_loss, grads = jax.value_and_grad(forward_and_loss_fn)(
+        params,
+        model=model,
+        input_tokens=example.input_tokens,
+        input_mask=example.target_mask,
+        positions=positions,
+    )
+    # Update the parameters.
+    updates, opt_state = optimizer.update(grads, opt_state, params)
+    params = optax.apply_updates(params, updates)
 
-  return train_loss, params, opt_state
-```
+    return train_loss, params, opt_state
+  ```
 
 Build the `validation_step` function without the backward pass:
 
diff --git a/docs/models/recurrentgemma/index.md b/docs/models/recurrentgemma/index.md
index 2aad277..2a58e76 100644
--- a/docs/models/recurrentgemma/index.md
+++ b/docs/models/recurrentgemma/index.md
@@ -13,21 +13,9 @@ variety of text generation tasks, including question answering, summarization, a
 reasoning. However, RecurrentGemma's unique architecture offers the following additional
 advantages:
 
--
-
-  #### Reduced memory usage
-
-  Lower memory requirements allow for the generation of longer samples on devices with limited memory, like single GPUs or CPUs.
--
-
-  #### Higher throughput
-
-  RecurrentGemma can perform inference at significantly higher batch sizes, meaning it can generate substantially more tokens per second --- especially when generating long sequences.
--
-
-  #### High performance
-
-  RecurrentGemma matches Gemma's performance while requiring less memory and achieving faster inference.
+- **Reduced memory usage** — Lower memory requirements allow for the generation of longer samples on devices with limited memory, like single GPUs or CPUs.
+- **Higher throughput** — RecurrentGemma can perform inference at significantly higher batch sizes, meaning it can generate substantially more tokens per second --- especially when generating long sequences.
+- **High performance** — RecurrentGemma matches Gemma's performance while requiring less memory and achieving faster inference.
 
 ## More resources
 
diff --git a/docs/models/recurrentgemma/inference.md b/docs/models/recurrentgemma/inference.md
index 0d44995..44a6aec 100644
--- a/docs/models/recurrentgemma/inference.md
+++ b/docs/models/recurrentgemma/inference.md
@@ -88,60 +88,60 @@ RECURRENTGEMMA_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b
 - The `tokenizer.model` file will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1`).
 - The model checkpoint will be in `/LOCAL/PATH/TO/recurrentgemma/flax/2b-it/1/2b-it`).
 
-```python
-CKPT_PATH = os.path.join(RECURRENTGEMMA_PATH, RECURRENTGEMMA_VARIANT)
-TOKENIZER_PATH = os.path.join(RECURRENTGEMMA_PATH, 'tokenizer.model')
-print('CKPT_PATH:', CKPT_PATH)
-print('TOKENIZER_PATH:', TOKENIZER_PATH)
-```
-
-``` { .no-copy .output }
-CKPT_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/2b-it
-TOKENIZER_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/tokenizer.model
-```
+  ```python
+  CKPT_PATH = os.path.join(RECURRENTGEMMA_PATH, RECURRENTGEMMA_VARIANT)
+  TOKENIZER_PATH = os.path.join(RECURRENTGEMMA_PATH, 'tokenizer.model')
+  print('CKPT_PATH:', CKPT_PATH)
+  print('TOKENIZER_PATH:', TOKENIZER_PATH)
+  ```
+
+  ``` { .no-copy .output }
+  CKPT_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/2b-it
+  TOKENIZER_PATH: /root/.cache/kagglehub/models/google/recurrentgemma/flax/2b-it/1/tokenizer.model
+  ```
 
 ## Perform sampling/inference
 
 1. Load the RecurrentGemma model checkpoint with the [`recurrentgemma.jax.load_parameters`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/utils.py#L31) method. The `sharding` argument set to `"single_device"` loads all model parameters on a single device.
 
-```python
-import recurrentgemma
-from recurrentgemma import jax as recurrentgemma
+   ```python
+   import recurrentgemma
+   from recurrentgemma import jax as recurrentgemma
 
-params = recurrentgemma.load_parameters(checkpoint_path=CKPT_PATH, sharding="single_device")
-```
+   params = recurrentgemma.load_parameters(checkpoint_path=CKPT_PATH, sharding="single_device")
+   ```
 
 1. Load the RecurrentGemma model tokenizer, constructed using [`sentencepiece.SentencePieceProcessor`](https://github.com/google/sentencepiece/blob/4d6a1f41069c4636c51a5590f7578a0dbed83450/python/src/sentencepiece/__init__.py#L423):
 
-```python
-import sentencepiece as spm
+   ```python
+   import sentencepiece as spm
 
-vocab = spm.SentencePieceProcessor()
-vocab.Load(TOKENIZER_PATH)
-```
+   vocab = spm.SentencePieceProcessor()
+   vocab.Load(TOKENIZER_PATH)
+   ```
 
-``` { .no-copy .output }
-True
-```
+   ``` { .no-copy .output }
+   True
+   ```
 
 1. To automatically load the correct configuration from the RecurrentGemma model checkpoint, use [`recurrentgemma.GriffinConfig.from_flax_params_or_variables`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/common.py#L128). Then, instantiate the [Griffin](https://arxiv.org/abs/2402.19427) model with [`recurrentgemma.jax.Griffin`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/griffin.py#L29).
 
-```python
-model_config = recurrentgemma.GriffinConfig.from_flax_params_or_variables(
-    flax_params_or_variables=params)
+   ```python
+   model_config = recurrentgemma.GriffinConfig.from_flax_params_or_variables(
+       flax_params_or_variables=params)
 
-model = recurrentgemma.Griffin(model_config)
-```
+   model = recurrentgemma.Griffin(model_config)
+   ```
 
 1. Create a `sampler` with [`recurrentgemma.jax.Sampler`](https://github.com/google-deepmind/recurrentgemma/blob/e4939f9b7edf8baa1d512fb86bfc2e206044d66b/recurrentgemma/jax/sampler.py#L74) on top of the RecurrentGemma model checkpoint/weights and the tokenizer:
 
-```python
-sampler = recurrentgemma.Sampler(
-    model=model,
-    vocab=vocab,
-    params=params,
-)
-```
+   ```python
+   sampler = recurrentgemma.Sampler(
+       model=model,
+       vocab=vocab,
+       params=params,
+   )
+   ```
 
 1. Write a prompt in `prompt` and perform inference. You can tweak `total_generation_steps` (the number of steps performed when generating a response --- this example uses `50` to preserve host memory).
 
diff --git a/docs/models/shieldgemma/index.md b/docs/models/shieldgemma/index.md
index 6252291..58a7a23 100644
--- a/docs/models/shieldgemma/index.md
+++ b/docs/models/shieldgemma/index.md
@@ -2,7 +2,12 @@
 
 ## ShieldGemma
 
-[Video](https://www.youtube.com/watch?v=BgWO2iVPlyw)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/BgWO2iVPlyw"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 
 ShieldGemma is a set of instruction tuned models for evaluating the safety of text and images against a set of defined safety policies. You can use
@@ -15,16 +20,8 @@ ShieldGemma 2 is a 4B parameter model built to label images for safety.
 ShieldGemma 1 is built on [Gemma 2](../gemma-4/index.md) in 2B, 9B, and 27B parameter sizes.
 
 
--
-
-  #### Content safety evaluation
-
-  Evaluate the safety of prompt input and output responses against a set of defined safety policies.
-- [](https://huggingface.co/collections/google/shieldgemma-67d130ef8da6af884072a789)
-
-  #### [Tuneable, open models](https://huggingface.co/collections/google/shieldgemma-67d130ef8da6af884072a789)
-
-  ShieldGemma models are provided with open weights and can be fine-tuned for your specific use case.
+- **Content safety evaluation** — Evaluate the safety of prompt input and output responses against a set of defined safety policies.
+- **[Tuneable, open models](https://huggingface.co/collections/google/shieldgemma-67d130ef8da6af884072a789)** — ShieldGemma models are provided with open weights and can be fine-tuned for your specific use case.
 
 ## ShieldGemma 2
 
diff --git a/docs/stylesheets/components.css b/docs/stylesheets/components.css
new file mode 100644
index 0000000..4fea01c
--- /dev/null
+++ b/docs/stylesheets/components.css
@@ -0,0 +1,55 @@
+/* ===================================================================
+   Components — reusable blocks emitted by scripts/convert_notebook.py.
+
+   Owns: video embeds, model-card header alignment.
+   Does NOT own: colours (Phase 1 token layer), page grid (layout.css),
+   code blocks (code.css), chat tokens (tokens.css).
+
+   Deliberately minimal — no borders, shadows or radii here. Anything
+   that restyles page content (images, links) belongs to the Phase 1
+   design system, not to a converter-output shim.
+   =================================================================== */
+
+/* -------------------------------------------------------------------
+   Video embeds
+   Full content width, 16:9. Replaces the plain `[Video](youtube…)`
+   links the upstream docs used.
+   ------------------------------------------------------------------- */
+.md-typeset .video-embed {
+  position: relative;
+  width: 100%;
+  margin: 1.5em 0;
+  aspect-ratio: 16 / 9;
+}
+
+.md-typeset .video-embed iframe {
+  position: absolute;
+  inset: 0;
+  width: 100%;
+  height: 100%;
+  border: 0;
+}
+
+/* Older engines without aspect-ratio support. */
+@supports not (aspect-ratio: 16 / 9) {
+  .md-typeset .video-embed {
+    height: 0;
+    padding-bottom: 56.25%;
+  }
+}
+
+/* -------------------------------------------------------------------
+   Model-card header
+   Centres the resource links and the License/Authors line, matching the
+   `<p align="center">` upstream uses. Alignment only — the banner image
+   keeps whatever styling the rest of the theme gives it.
+   ------------------------------------------------------------------- */
+.md-typeset .model-card-header {
+  text-align: center;
+}
+
+@media screen and (max-width: 44.9375em) {
+  .md-typeset .model-card-header {
+    text-align: left;
+  }
+}
diff --git a/docs/tuning/spoken-language.md b/docs/tuning/spoken-language.md
index c8a9b15..a6ff236 100644
--- a/docs/tuning/spoken-language.md
+++ b/docs/tuning/spoken-language.md
@@ -1,6 +1,11 @@
 # Tasks in spoken languages with Gemma
 
-[Video](https://www.youtube.com/watch?v=M4HGJehH4r0)
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/M4HGJehH4r0"
+          title="Video" loading="lazy" allowfullscreen
+          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"
+          referrerpolicy="strict-origin-when-cross-origin"></iframe>
+</div>
 
 Using artificial intelligence (AI) technology in a specific spoken language is a
 critical need for many businesses to be able to use it effectively. The Gemma
@@ -71,10 +76,10 @@ To install the required software:
 
 - Install Python 3 and the `venv` virtual environment package for Python.
 
-```bash
+  ```bash
   sudo apt update
   sudo apt install git pip python3-venv
-```
+  ```
 
 #### Clone the project
 
@@ -86,18 +91,18 @@ To download the project code:
 
 1. Clone the git repository using the following command.
 
-```bash
+   ```bash
    git clone https://github.com/google-gemini/gemma-cookbook.git
-```
+   ```
 
 2. Optionally, configure your local git repository to use sparse checkout,
    so you have only the files for the project.
 
-```bash
+   ```bash
    cd gemma-cookbook/
    git sparse-checkout set Demos/spoken-language-tasks/
    git sparse-checkout init --cone
-```
+   ```
 
 #### Install Python libraries
 
@@ -111,23 +116,23 @@ To install the Python libraries:
 
 1. In a terminal window, navigate to the `spoken-language-tasks` directory:
 
-```bash
+   ```bash
    cd Demos/spoken-language-tasks/
-```
+   ```
 
 2. Configure and activate Python virtual environment (venv) for this project:
 
-```text
+   ```text
    python3 -m venv venv
    source venv/bin/activate
-```
+   ```
 
 3. Install the required Python libraries for this project using the
    `setup_python` script.
 
-```text
+   ```text
    ./setup_python.sh
-```
+   ```
 
 !!! Tip
     On Linux operating systems, you may need to allow execution of the bash script by running the command `chmod +x setup_python.sh`.
@@ -152,17 +157,17 @@ To set the environment variables:
 2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
 3. Create environment variable files for the project, by creating a `.env` text file at *each* these locations in your clone of the project:
 
-``` { .no-copy .output }
+   ``` { .no-copy .output }
    k-mail-replier/k_mail_replier/.env
    k-gemma-it/.env
-```
+   ```
 4. After creating the `.env` text files, add the following settings to **both**
    files:
 
-```python
+   ```python
    KAGGLE_USERNAME=<YOUR_KAGGLE_USERNAME_HERE>
    KAGGLE_KEY=<YOUR_KAGGLE_KEY_HERE>
-```
+   ```
 
 ### Run and test the application
 
@@ -174,22 +179,22 @@ To run and test the project:
 
 1. In a terminal window, navigate to the `/k_mail_replier/` directory:
 
-```bash
+   ```bash
    cd spoken-language-tasks/k-mail-replier/
-```
+   ```
 
 2. Run the application using the `run_flask_app.sh` script:
 
-```text
+   ```text
    ./run_flask_app.sh
-```
+   ```
 
 3. After starting the web application, the program code lists a URL where
    you can browse and test. Typically, this address is:
 
-```text
+   ```text
    http://127.0.0.1:5000/
-```
+   ```
 
 4. In the web interface, press the **답변 작성** button below the first input
    field to generate a response from the model.
@@ -221,11 +226,11 @@ To modify prompt instructions:
 2. In the `app.py` code, add additions instructions to the
    `get_prompt():` function:
 
-```python
+   ```python
    def get_prompt():
        return "발신자에게 요청에 대한 감사를 전하고, 곧 자세한 내용을
            알려드리겠다고 정중하게 답장해 주세요. 정중하게 답변해 주세요!:\n"
-```
+   ```
 
 This example adds the phrase "Please write a polite response!" to the
 instructions in Korean.
@@ -343,7 +348,7 @@ def prepare_tuning_dataset():
 
 This function reads in the data and formats it by adding `start_of_turn` and
 `end_of_turn` tags which is the
-[required format](../get-started/prompt-structure.md)
+[required format](../get-started/prompt-structure.md#gemma-formatting-and-system-instructions)
 when providing data for tuning a Gemma model. This code also inserts a
 `prompt_instruction` for each request, which you should edit as appropriate for
 your application.
@@ -367,15 +372,15 @@ To perform the tuning run and calculate new weights:
 
 1. In a terminal window, navigate to the `k-gemma-it/` directory.
 
-```bash
+   ```bash
    cd spoken-language-tasks/k-gemma-it/
-```
+   ```
 
 2. Run the tuning process using the `tune_model` script:
 
-```text
+   ```text
    ./tune_model.sh
-```
+   ```
 
 The tuning process takes several minutes depending on your available compute
 resources. When it completes successfully, the tuning program writes new `*.h5`
@@ -430,15 +435,15 @@ To deploy the generated weights to the web application:
 
 1. In a terminal window, navigate to the `k-gemma-it/` directory.
 
-```bash
+   ```bash
    cd spoken-language-tasks/k-gemma-it/
-```
+   ```
 
 2. Run the tuning process using the `deploy_weights` script:
 
-```text
+   ```text
    ./deploy_weights.sh
-```
+   ```
 
 After running this script, you should see a new `*.h5` file in the
 `k-mail-replier/k_mail_replier/weights/` directory.
@@ -453,22 +458,22 @@ To run and test the project:
 
 1. In a terminal window, navigate to the `/k_mail_replier/` directory.
 
-```bash
+   ```bash
    cd spoken-language-tasks/k-mail-replier/
-```
+   ```
 
 2. Run the application using the `run_flask_app.sh` script:
 
-```text
+   ```text
    ./run_flask_app.sh
-```
+   ```
 
 3. After starting the web application, you the program code lists a URL where
    you can browse and test, typically this address is:
 
-```text
+   ```text
    http://127.0.0.1:5000/
-```
+   ```
 
 4. In the web interface, press the **답변 작성** button below the first input
    field to generate a response from the model.
diff --git a/scripts/check_fidelity.py b/scripts/check_fidelity.py
index 2e5b822..3e23ce4 100644
--- a/scripts/check_fidelity.py
+++ b/scripts/check_fidelity.py
@@ -49,6 +49,17 @@
 # local .md path in the output. Both forms are blanked so the retargeting doesn't read as loss.
 # Deliberately narrow -- other external links (Hugging Face, Kaggle, GitHub) stay in the
 # comparison, so silently losing one of those is still caught.
+# `[Video](youtube-url)` becomes an <iframe> embed, so the URL changes form. Both the original
+# link target and the embed src are blanked; the visible text is unaffected either way.
+YOUTUBE_URL = re.compile(
+    r"https?://(?:www\.)?(?:youtube(?:-nocookie)?\.com|youtu\.be)/\S*"
+)
+IFRAME_SRC = re.compile(r"(<iframe[^>]+src=[\"'])[^\"']*([\"'])")
+
+# `[](url)` -- a link with no visible text. Devsite card grids emit one alongside a titled link
+# to the same URL; collapsing the card drops the empty duplicate. No prose is involved.
+EMPTY_LINK = re.compile(r"\[\]\([^)]*\)")
+
 REWRITTEN_LINK_TARGET = re.compile(
     r"(\]\()"
     r"(?:https?://ai\.google\.dev/gemma/docs[^)\s]*"
@@ -102,6 +113,9 @@ def words(text: str) -> Counter:
     text = MD_IMAGE_TARGET.sub(r"\1\2", text)
     text = HTML_IMAGE_TARGET.sub(r"\1\2", text)
     text = REWRITTEN_LINK_TARGET.sub(r"\1\2", text)
+    text = EMPTY_LINK.sub("", text)
+    text = IFRAME_SRC.sub(r"\1\2", text)
+    text = YOUTUBE_URL.sub("", text)
     tokens = (normalise(t) for t in WORD.findall(text))
     return Counter(t for t in tokens if len(t) >= 2)
 
diff --git a/scripts/convert_notebook.py b/scripts/convert_notebook.py
index 721c9e9..6203d57 100644
--- a/scripts/convert_notebook.py
+++ b/scripts/convert_notebook.py
@@ -173,6 +173,166 @@ def detect_language(lines: list[str]) -> str:
     return "text"
 
 
+def normalised_indent(indent: int) -> int:
+    """The indent a bullet ends up with once fix_list_indentation has run."""
+    if 1 <= indent <= 3:
+        return 4
+    if 5 <= indent <= 7:
+        return 8
+    return indent
+
+
+def list_content_indent(lines: list[str], index: int, base_indent: int) -> int:
+    """Column where the enclosing list item's content starts; 0 when not inside a list.
+
+    A fence must sit at *exactly* this column. At column 0 it breaks out of the list, so
+    everything after it starts a new one (embeddinggemma/inference.md's RAG section). One column
+    too far and Markdown stops treating it as part of the item and emits the backticks
+    literally -- which is what `4. ` items did, their content starting at column 3, not 4.
+    """
+    j = index - 1
+    while j >= 0:
+        previous = lines[j]
+        if not previous.strip():
+            j -= 1
+            continue
+        match = LIST_ITEM.match(previous)
+        if match:
+            indent = len(match.group(1))
+            if indent >= base_indent:
+                return 0
+            marker_width = len(match.group(0)) - indent  # "- " -> 2, "4. " -> 3
+            return normalised_indent(indent) + marker_width
+        if len(previous) - len(previous.lstrip()) == 0:
+            return 0
+        j -= 1
+    return 0
+
+
+# A devsite card grid, which the upstream .md.txt export mangles into an empty bullet followed
+# by a heading and a paragraph. Collapsed to a plain bullet -- see PLAN.md Step 0.7.
+CARD_ITEM = re.compile(
+    r"^-[ \t]*(?:\[\]\([^)]*\))?[ \t]*\n"      # bullet, optionally an empty link
+    r"\n+"
+    r"[ \t]+#{3,6}[ \t]+(?P<title>.+?)[ \t]*\n"  # the card's heading
+    r"\n+"
+    r"(?P<body>[ \t]+\S.*(?:\n[ \t]+\S.*)*)",   # the card's paragraph
+    re.MULTILINE,
+)
+
+# `[Video](https://youtube...)` on a line of its own: render it, don't link it.
+VIDEO_LINK = re.compile(
+    r"^\[Video\]\(\s*https?://(?:www\.)?(?:youtube\.com/watch\?v=|youtu\.be/)"
+    r"(?P<id>[A-Za-z0-9_-]{6,})[^)]*\)[ \t]*$",  # not \s*$ -- that eats the following blank lines
+    re.MULTILINE,
+)
+
+# A model-card header: pipe-separated links, then the License/Authors line. Upstream centres
+# this with `<p align="center">`, which the .md.txt export drops.
+CARD_HEADER = re.compile(
+    r"\A(?P<banner>!\[[^\]]*\]\([^)]*\)\n+)?"
+    r"(?P<links>\[[^\]]+\]\([^)]+\)(?:[ \t]*\\\|\n?[ \t]*\[[^\]]+\]\([^)]+\))+)\n+"
+    r"(?P<meta>\*\*License\*\*[^\n]*\n)",
+)
+
+# Notes inside a blockquote. Covers the labelled forms (`> NOTE:`, `> **Note:**`, `> *Note:`)
+# and the unlabelled one (`> Note that many libraries ...`), which upstream also uses.
+BLOCKQUOTE_NOTE = re.compile(
+    r"^>[ \t]*[*_]{0,2}"
+    r"(?P<label>NOTE|Note|IMPORTANT|Important|WARNING|Warning|TIP|Tip|CAUTION|Caution)"
+    r"[*_]{0,2}"
+    # Either a colon (the label is a prefix and drops out) or a word boundary (the label is
+    # part of the sentence, as in "Note that many libraries…", and must be kept).
+    # `**Note:**` closes its emphasis *after* the colon, so allow markers on both sides.
+    r"(?:(?P<colon>[ \t]*:[*_]{0,2})|(?=[ \t]|$))"
+    r"[ \t]*(?P<first>.*)$",
+)
+
+
+def collapse_card_grids(text: str) -> str:
+    """Turn devsite card grids into plain bullets: `- **Title** -- description`."""
+
+    def replace(match: re.Match) -> str:
+        title = match.group("title").strip()
+        body = " ".join(line.strip() for line in match.group("body").splitlines())
+        return f"- **{title}** — {body}"
+
+    return CARD_ITEM.sub(replace, text)
+
+
+def tighten_nested_lists(text: str) -> str:
+    """Drop the blank line between a list item and its own nested list.
+
+    The blank makes Markdown render the list "loose", wrapping every item in <p> and spacing
+    them inconsistently against neighbouring items that lack the blank.
+    """
+    return re.sub(
+        r"^(\s*(?:[-*+]|\d+\.)\s+.*)\n\n+(?=(\s+)(?:[-*+]|\d+\.)\s)",
+        r"\1\n",
+        text,
+        flags=re.MULTILINE,
+    )
+
+
+def embed_videos(text: str) -> str:
+    """Replace a standalone `[Video](youtube-url)` with a responsive privacy-mode embed."""
+
+    def replace(match: re.Match) -> str:
+        return (
+            '<div class="video-embed">\n'
+            f'  <iframe src="https://www.youtube-nocookie.com/embed/{match.group("id")}"\n'
+            '          title="Video" loading="lazy" allowfullscreen\n'
+            '          allow="accelerometer; encrypted-media; gyroscope; picture-in-picture"\n'
+            "          referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>\n"
+            "</div>"
+        )
+
+    return VIDEO_LINK.sub(replace, text)
+
+
+def center_card_header(text: str) -> str:
+    """Restore the centred model-card header that upstream renders with `<p align="center">`."""
+
+    def replace(match: re.Match) -> str:
+        banner = (match.group("banner") or "").strip()
+        parts = ['<div class="model-card-header" markdown>']
+        if banner:
+            parts.append(banner)
+        parts.append(match.group("links").strip())
+        parts.append(match.group("meta").strip())
+        parts.append("</div>")
+        return "\n\n".join(parts) + "\n"
+
+    return CARD_HEADER.sub(replace, text, count=1)
+
+
+def convert_blockquote_notes(lines: list[str]) -> list[str]:
+    """Convert `> NOTE: ...` blockquotes into `!!! note` admonitions."""
+    out: list[str] = []
+    i = 0
+    while i < len(lines):
+        match = BLOCKQUOTE_NOTE.match(lines[i])
+        if not match:
+            out.append(lines[i])
+            i += 1
+            continue
+
+        first = match.group("first").strip()
+        if not match.group("colon"):
+            # No colon means the label is part of the prose, not a prefix.
+            first = f"{match.group('label')} {first}".strip()
+        body = [first]
+        i += 1
+        while i < len(lines) and lines[i].lstrip().startswith(">"):
+            body.append(re.sub(r"^\s*>[ \t]?", "", lines[i]).strip())
+            i += 1
+
+        out.append(f"!!! {match.group('label').capitalize()}")
+        out.extend(f"    {line}" for line in body if line)
+        out.append("")
+    return out
+
+
 def extract_data_uri_images(
     text: str, assets_dir: Path | None, assets_url: str, stem: str
 ) -> tuple[str, int]:
@@ -398,7 +558,7 @@ def convert(
                 # prompt into a fence plus orphaned bullets.
                 started = bool(code_lines)
                 if lines[j].startswith("    ") and (started or not is_sub_bullet(lines[j])):
-                    code_lines.append(lines[j][4:])
+                    code_lines.append(lines[j])  # dedented below, once the block's base is known
                     j += 1
                 elif lines[j].strip() == "":
                     # Skip the whole run of blank lines, not just one. Notebook exports
@@ -424,12 +584,22 @@ def convert(
                 code_lines.pop()
 
             if code_lines:
+                # Dedent by the block's own indent rather than a fixed 4, and indent the fence
+                # to match its list item -- otherwise nested code escapes the list.
+                base = min(
+                    len(line) - len(line.lstrip())
+                    for line in code_lines
+                    if line.strip()
+                )
+                code_lines = [line[base:] if line.strip() else "" for line in code_lines]
+                pad = " " * list_content_indent(lines, i, base)
+
                 lang = detect_language(code_lines)
                 if lang == "bash":
                     code_lines = clean_shell_lines(code_lines)
-                output.append(f"```{lang}")
-                output.extend(code_lines)
-                output.append("```")
+                output.append(f"{pad}```{lang}")
+                output.extend(pad + line if line else "" for line in code_lines)
+                output.append(f"{pad}```")
                 i = j
                 continue
 
@@ -442,9 +612,19 @@ def convert(
                 j += 1
 
             if j < n:
-                output.append("``` { .no-copy .output }")
-                output.extend(content_lines)
-                output.append("```")
+                # Devsite indents output blocks to match their list item. Emitting at column 0
+                # split the list, and the *following* item's fence then rendered literally.
+                fence_indent = len(line) - len(line.lstrip())
+                pad = " " * (list_content_indent(lines, i, fence_indent + 1) or fence_indent)
+                body_base = min(
+                    (len(c) - len(c.lstrip()) for c in content_lines if c.strip()),
+                    default=0,
+                )
+                output.append(f"{pad}``` {{ .no-copy .output }}")
+                output.extend(
+                    pad + c[body_base:] if c.strip() else "" for c in content_lines
+                )
+                output.append(f"{pad}```")
                 i = j + 1
                 continue
 
@@ -453,11 +633,19 @@ def convert(
         i += 1
 
     # Apply table fixes and list indentation fixes
+    output = convert_blockquote_notes(output)
     output = fix_tables(output)
     output = fix_list_indentation(output)
 
     result = "\n".join(output)
 
+    # Whole-document cleanups. Order matters: collapse the card grids into bullets before
+    # tightening lists, so the generated bullets are tightened too.
+    result = collapse_card_grids(result)
+    result = tighten_nested_lists(result)
+    result = embed_videos(result)
+    result = center_card_header(result)
+
     # Extract inline base64 images before anything else measures the document's size.
     result, extracted = extract_data_uri_images(result, assets_dir, assets_url, assets_stem)
     if extracted:
diff --git a/scripts/link_map.toml b/scripts/link_map.toml
index 94b2b61..92860e2 100644
--- a/scripts/link_map.toml
+++ b/scripts/link_map.toml
@@ -129,6 +129,22 @@
 # do not exist on ai.google.dev either. Inventing a target would be an editorial guess; see
 # PLAN.md Notes.
 # ------------------------------------------------------------------------------------------
+# ------------------------------------------------------------------------------------------
+# [page_overrides] Per-page mapping that beats [ported] for that page only.
+#
+# The Gemma 1/2/3 cards open with "**Model Page** : [Gemma](/gemma/docs/core)". Upstream that
+# meant "the current Gemma page", but here /core resolves to Gemma 4 -- so the Gemma 2 card
+# would advertise Gemma 4 as its model page. Each card points at itself instead.
+# ------------------------------------------------------------------------------------------
+[page_overrides."models/gemma-1/index.md"]
+"/core" = "models/gemma-1/index.md"
+
+[page_overrides."models/gemma-2/index.md"]
+"/core" = "models/gemma-2/index.md"
+
+[page_overrides."models/gemma-3/index.md"]
+"/core" = "models/gemma-3/index.md"
+
 [anchors]
 "models/gemma-4/index.md#previous-models" = "previous-gemma-models"
 # devsite used short custom anchors; our headings come from the source's own section titles.
@@ -142,6 +158,8 @@
 "learn/personal-code-assistant.md#extension" = "vs-code-extension-project"
 "learn/personal-code-assistant.md#web-service" = "gemma-web-service-project"
 "tuning/spoken-language.md#compute_requirements" = "hardware-requirements"
+# The formatting guidance lives under this page's H1, not a section of its own.
+"get-started/prompt-structure.md#formatting-instruction" = "gemma-formatting-and-system-instructions"
 "models/gemma-3n/index.md#matformer" = "matformer-architecture"
 "models/gemma-3n/index.md#conditional-parameter" = "conditional-parameter-loading"
 "models/gemma-3n/index.md#parameters" = "model-parameters-and-effective-parameters"
diff --git a/scripts/rewrite_links.py b/scripts/rewrite_links.py
index 2ce2889..6e6b765 100644
--- a/scripts/rewrite_links.py
+++ b/scripts/rewrite_links.py
@@ -129,12 +129,13 @@ def replace(match: re.Match) -> str:
     return 0
 
 
-def load_map() -> tuple[dict[str, str], dict[str, str], set[str]]:
+def load_map() -> tuple[dict[str, str], dict[str, str], set[str], dict[str, dict[str, str]]]:
     data = tomllib.loads(LINK_MAP.read_text(encoding="utf-8"))
     return (
         data.get("ported", {}),
         data.get("planned", {}),
         set(data.get("delink", {}).get("paths", [])),
+        data.get("page_overrides", {}),
     )
 
 
@@ -162,7 +163,7 @@ def main() -> int:
     if args.fix_anchors:
         return fix_anchors(args.dry_run)
 
-    ported, planned, delink = load_map()
+    ported, planned, delink, page_overrides = load_map()
     mapping = {**ported, **planned}
 
     unmapped: Counter = Counter()
@@ -184,6 +185,8 @@ def main() -> int:
                 mask[position : position + len(line)] = b"\x01" * len(line)
             position += len(line)
 
+        # A page-specific mapping wins over the global one; see [page_overrides].
+        overrides = page_overrides.get(page.rel, {})
         modified = False
 
         def replace(match: re.Match) -> str:
@@ -200,6 +203,10 @@ def replace(match: re.Match) -> str:
                 stats["delinked"] += 1
                 modified = True
                 return text
+            if path in overrides:
+                stats["page override"] += 1
+                modified = True
+                return f"[{text}]({relative(overrides[path], page.path)}{fragment})"
             if path in mapping:
                 stats["planned" if path in planned else "ported"] += 1
                 modified = True
diff --git a/zensical.toml b/zensical.toml
index 599bcfb..a214510 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -12,7 +12,7 @@ site_url = "https://gemma.ai"
 copyright = """
 Copyright &copy; 2026 Google DeepMind
 """
-extra_css = ["stylesheets/home.css", "stylesheets/layout.css", "stylesheets/cookbook.css", "stylesheets/code.css", "stylesheets/tokens.css"]
+extra_css = ["stylesheets/home.css", "stylesheets/layout.css", "stylesheets/components.css", "stylesheets/cookbook.css", "stylesheets/code.css", "stylesheets/tokens.css"]
 # `include_yaml` exposes data/models.yml as `models.*` in page templates -- see data/models.yml
 # for what reads it. Hand-editing a model table in a page is a bug; change the data file.
 plugins = { macros = { enabled = true, include_yaml = { models = "data/models.yml" } } }

From 385c928f655f1b971287efa76334c08a32425102 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 3 Aug 2026 15:25:58 +0200
Subject: [PATCH 08/20] Fix titles, small changes, and prevent updates to
 models / get started

---
 PLAN.md                                   | 28 +++++++++++
 docs/models/datagemma/index.md            |  3 --
 docs/models/embeddinggemma/index.md       |  2 +-
 docs/models/embeddinggemma/model-card.md  |  2 +-
 docs/models/functiongemma/index.md        |  2 +-
 docs/models/functiongemma/model-card.md   |  2 +-
 docs/models/gemma-1/index.md              |  2 +-
 docs/models/gemma-2/index.md              |  2 +-
 docs/models/gemma-3/index.md              |  2 +-
 docs/models/gemma-3n/index.md             |  2 +-
 docs/models/gemma-3n/model-card.md        |  2 +-
 docs/models/gemma-4/index.md              |  2 +
 docs/models/gemma-scope/index.md          |  4 +-
 docs/models/paligemma/index.md            |  3 --
 docs/models/paligemma/model-card-v1.md    |  2 +-
 docs/models/paligemma/model-card-v2.md    |  2 +-
 docs/models/recurrentgemma/fine-tuning.md |  3 +-
 docs/models/recurrentgemma/index.md       |  3 --
 docs/models/recurrentgemma/model-card.md  |  2 +-
 docs/models/shieldgemma/index.md          |  4 +-
 scripts/check_fidelity.py                 | 10 +++-
 scripts/port_docs.py                      |  9 +++-
 scripts/sources.toml                      | 58 +++++++++++++++++++++--
 23 files changed, 113 insertions(+), 38 deletions(-)

diff --git a/PLAN.md b/PLAN.md
index 623905f..7ab92c3 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -44,6 +44,34 @@ that validation is only transferable if the words survive the port intact.
 Rule of thumb: if a diff against the source `.md.txt` shows a prose change, it needs a reason
 written down.
 
+### Hand-maintained sections — do not re-convert
+
+**As of 2026-08-03, `docs/models/**` and `docs/get-started/**` are hand-maintained by Maarten.**
+The converter is no longer the source of truth for them; the files on disk are. Running the
+converter over these pages would silently discard his edits.
+
+This is enforced, not just documented — those 41 pages carry `frozen = true` in
+`scripts/sources.toml`:
+
+- `port_docs.py` skips them and reports `frozen (hand-maintained, skipped)`.
+- `check_fidelity.py` skips them: divergence from upstream is now *intended*, so comparing
+  against the source would produce noise, not signal.
+- Their `source` line stays in the manifest for provenance — `source_url` frontmatter in
+  Step 3.1 still needs it, and it records where the page originally came from.
+
+**The converter is for new imports only.** When importing a new page: add it to the manifest
+*without* `frozen`, run the pipeline, review the result — and set `frozen = true` as soon as
+anyone starts editing it by hand.
+
+Currently convertible (6): `deployment/index.md`, `deployment/huggingface.md`,
+`deployment/mobile-actions.md`, `tuning/spoken-language.md`,
+`learn/business-email-assistant.md`, `learn/personal-code-assistant.md`.
+
+!!! warning "Converter fixes no longer reach frozen pages"
+    A future converter improvement will not propagate to Models or Get started. Those pages
+    need the equivalent fix applied by hand, or a one-off unfreeze/re-port/re-freeze with the
+    edits re-applied. Weigh that before adding converter transforms aimed at those sections.
+
 Local preview:
 
 ```bash
diff --git a/docs/models/datagemma/index.md b/docs/models/datagemma/index.md
index adf8c86..ae8ca47 100644
--- a/docs/models/datagemma/index.md
+++ b/docs/models/datagemma/index.md
@@ -1,8 +1,5 @@
 # DataGemma
 
-## DataGemma
-
-
 DataGemma is a research tool that lets users ask questions in plain language and receive
 answers based on publicly available statistical data in the
 [Data Commons](https://datacommons.org/) repository. The tool uses
diff --git a/docs/models/embeddinggemma/index.md b/docs/models/embeddinggemma/index.md
index 315bff9..b0afee3 100644
--- a/docs/models/embeddinggemma/index.md
+++ b/docs/models/embeddinggemma/index.md
@@ -1,4 +1,4 @@
-# EmbeddingGemma model overview
+# EmbeddingGemma model overview {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
   <iframe src="https://www.youtube-nocookie.com/embed/Xu1X-J-r5Xk"
diff --git a/docs/models/embeddinggemma/model-card.md b/docs/models/embeddinggemma/model-card.md
index 33e7d35..64cf9eb 100644
--- a/docs/models/embeddinggemma/model-card.md
+++ b/docs/models/embeddinggemma/model-card.md
@@ -1,4 +1,4 @@
-# EmbeddingGemma model card
+# Model card
 
 **Model Page** : [EmbeddingGemma](index.md)
 
diff --git a/docs/models/functiongemma/index.md b/docs/models/functiongemma/index.md
index 3178fb9..258a917 100644
--- a/docs/models/functiongemma/index.md
+++ b/docs/models/functiongemma/index.md
@@ -1,4 +1,4 @@
-# FunctionGemma model overview
+# FunctionGemma model overview {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
   <iframe src="https://www.youtube-nocookie.com/embed/-Tgc_9uYJLI"
diff --git a/docs/models/functiongemma/model-card.md b/docs/models/functiongemma/model-card.md
index d27b7a2..66c3015 100644
--- a/docs/models/functiongemma/model-card.md
+++ b/docs/models/functiongemma/model-card.md
@@ -1,4 +1,4 @@
-# FunctionGemma model card
+# Model card
 
 **Model Page** : [FunctionGemma](index.md)
 
diff --git a/docs/models/gemma-1/index.md b/docs/models/gemma-1/index.md
index 5499857..04867db 100644
--- a/docs/models/gemma-1/index.md
+++ b/docs/models/gemma-1/index.md
@@ -1,4 +1,4 @@
-# Gemma model card
+# Model card
 
 **Model Page** : [Gemma](index.md)
 
diff --git a/docs/models/gemma-2/index.md b/docs/models/gemma-2/index.md
index ef876ed..3e1ec98 100644
--- a/docs/models/gemma-2/index.md
+++ b/docs/models/gemma-2/index.md
@@ -1,4 +1,4 @@
-# Gemma 2 model card
+# Model card
 
 **Model Page** : [Gemma](index.md)
 
diff --git a/docs/models/gemma-3/index.md b/docs/models/gemma-3/index.md
index ee0ddea..ebafa79 100644
--- a/docs/models/gemma-3/index.md
+++ b/docs/models/gemma-3/index.md
@@ -1,4 +1,4 @@
-# Gemma 3 model card
+# Model card
 
 **Model Page** : [Gemma](index.md)
 
diff --git a/docs/models/gemma-3n/index.md b/docs/models/gemma-3n/index.md
index 8a2f5be..7cb7945 100644
--- a/docs/models/gemma-3n/index.md
+++ b/docs/models/gemma-3n/index.md
@@ -1,4 +1,4 @@
-# Gemma 3n model overview
+# Gemma 3n model overview {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
   <iframe src="https://www.youtube-nocookie.com/embed/eJFJRyXEHZ0"
diff --git a/docs/models/gemma-3n/model-card.md b/docs/models/gemma-3n/model-card.md
index c7af07b..c4dfaf2 100644
--- a/docs/models/gemma-3n/model-card.md
+++ b/docs/models/gemma-3n/model-card.md
@@ -1,4 +1,4 @@
-# Gemma 3n model card
+# Model card
 
 **Model Page** : [Gemma 3n](index.md)
 
diff --git a/docs/models/gemma-4/index.md b/docs/models/gemma-4/index.md
index 71972e3..7ac00d4 100644
--- a/docs/models/gemma-4/index.md
+++ b/docs/models/gemma-4/index.md
@@ -1,3 +1,5 @@
+# Gemma 4 {: #hidden-title style="display: none;" }
+
 <div class="video-embed">
   <iframe src="https://www.youtube-nocookie.com/embed/jZVBoFOJK-Q"
           title="Video" loading="lazy" allowfullscreen
diff --git a/docs/models/gemma-scope/index.md b/docs/models/gemma-scope/index.md
index 54fa10d..c71a27a 100644
--- a/docs/models/gemma-scope/index.md
+++ b/docs/models/gemma-scope/index.md
@@ -1,8 +1,6 @@
 # Gemma Scope
 
-## Gemma Scope
-
-### Analyze Gemma 3 with Gemma Scope 2
+## Analyze Gemma 3 with Gemma Scope 2
 
 
 Gemma Scope 2 is a comprehensive, open suite of interpretability tools designed for the
diff --git a/docs/models/paligemma/index.md b/docs/models/paligemma/index.md
index 75760ce..55a3d7d 100644
--- a/docs/models/paligemma/index.md
+++ b/docs/models/paligemma/index.md
@@ -1,8 +1,5 @@
 # PaliGemma
 
-## PaliGemma
-
-
 PaliGemma 2 and PaliGemma are lightweight open *vision-language models* (VLM)
 inspired by [PaLI-3](https://arxiv.org/abs/2310.09199),
 and based on open components like the [SigLIP
diff --git a/docs/models/paligemma/model-card-v1.md b/docs/models/paligemma/model-card-v1.md
index d565a29..45a9e19 100644
--- a/docs/models/paligemma/model-card-v1.md
+++ b/docs/models/paligemma/model-card-v1.md
@@ -1,4 +1,4 @@
-# PaliGemma 1 model card
+# Model card
 
 **Model page:** [PaliGemma](index.md)
 
diff --git a/docs/models/paligemma/model-card-v2.md b/docs/models/paligemma/model-card-v2.md
index b582d0c..fbfa02a 100644
--- a/docs/models/paligemma/model-card-v2.md
+++ b/docs/models/paligemma/model-card-v2.md
@@ -1,4 +1,4 @@
-# PaliGemma 2 model card
+# Model card
 
 **Model page:** [PaliGemma](index.md)
 
diff --git a/docs/models/recurrentgemma/fine-tuning.md b/docs/models/recurrentgemma/fine-tuning.md
index 6afce60..e14d88d 100644
--- a/docs/models/recurrentgemma/fine-tuning.md
+++ b/docs/models/recurrentgemma/fine-tuning.md
@@ -1,5 +1,4 @@
-# Fine-tuning RecurrentGemma using JAX and Flax
-
+# Fine-tuning using JAX and Flax
 
 This tutorial demonstrates how to fine-tune the [RecurrentGemma](index.md) 2B Instruct model for an English-French translation task using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma), [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), [Optax](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), and the [MTNT (Machine Translation of Noisy Text) dataset](https://arxiv.org/abs/1809.00388). Although Flax is not used directly in this notebook, Flax was used to create Gemma.
 
diff --git a/docs/models/recurrentgemma/index.md b/docs/models/recurrentgemma/index.md
index 2a58e76..a9255aa 100644
--- a/docs/models/recurrentgemma/index.md
+++ b/docs/models/recurrentgemma/index.md
@@ -1,8 +1,5 @@
 # RecurrentGemma
 
-## RecurrentGemma
-
-
 RecurrentGemma is an open model based on
 [Griffin](https://arxiv.org/abs/2402.19427), a hybrid model architecture that
 mixes gated linear recurrences with local sliding window attention.
diff --git a/docs/models/recurrentgemma/model-card.md b/docs/models/recurrentgemma/model-card.md
index 17eaf5c..4d427c1 100644
--- a/docs/models/recurrentgemma/model-card.md
+++ b/docs/models/recurrentgemma/model-card.md
@@ -1,4 +1,4 @@
-# RecurrentGemma model card
+# Model card
 
 **Model page:** [RecurrentGemma](index.md)
 
diff --git a/docs/models/shieldgemma/index.md b/docs/models/shieldgemma/index.md
index 58a7a23..9234193 100644
--- a/docs/models/shieldgemma/index.md
+++ b/docs/models/shieldgemma/index.md
@@ -1,6 +1,4 @@
-# ShieldGemma
-
-## ShieldGemma
+# ShieldGemma {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
   <iframe src="https://www.youtube-nocookie.com/embed/BgWO2iVPlyw"
diff --git a/scripts/check_fidelity.py b/scripts/check_fidelity.py
index 3e23ce4..835624f 100644
--- a/scripts/check_fidelity.py
+++ b/scripts/check_fidelity.py
@@ -159,9 +159,14 @@ def fetch(source: str) -> str:
 def check_manifest(cache_dir: Path | None, verbose: bool) -> int:
     pages = tomllib.loads(MANIFEST.read_text(encoding="utf-8"))["page"]
     print(f"Checking {len(pages)} page(s) against upstream\n")
-    failures = 0
+    failures = frozen = 0
     for entry in pages:
         target = DOCS_DIR / entry["path"]
+        if entry.get("frozen"):
+            # Hand-maintained: divergence from upstream is intended, not a defect.
+            print(f"  FROZEN {entry['path']}")
+            frozen += 1
+            continue
         if not target.exists():
             print(f"  SKIP  {entry['path']} (not yet ported)")
             continue
@@ -178,7 +183,8 @@ def check_manifest(cache_dir: Path | None, verbose: bool) -> int:
         dropped, added = compare(source_text, target.read_text(encoding="utf-8"))
         if not report(entry["path"], dropped, added, verbose):
             failures += 1
-    print(f"\n{len(pages) - failures} faithful, {failures} with dropped prose")
+    checked = len(pages) - frozen
+    print(f"\n{checked - failures} faithful, {failures} with dropped prose, {frozen} frozen")
     return 1 if failures else 0
 
 
diff --git a/scripts/port_docs.py b/scripts/port_docs.py
index c62a0bf..b49a78c 100644
--- a/scripts/port_docs.py
+++ b/scripts/port_docs.py
@@ -2,8 +2,9 @@
 """
 Batch-port upstream pages listed in scripts/sources.toml.
 
-Only pages in the manifest are touched. A page absent from the manifest is hand-authored or
-hand-edited and must never be re-converted -- see the "Content fidelity" section in PLAN.md.
+Only pages in the manifest are touched, and only those not marked `frozen = true`.
+A page absent from the manifest, or frozen in it, is hand-maintained and must never be
+re-converted -- see "Hand-maintained sections" in PLAN.md.
 
     python scripts/port_docs.py --dry-run              # show what would change
     python scripts/port_docs.py --filter models/       # port one section
@@ -52,6 +53,10 @@ def relative_assets_url(assets_dir: Path, page_path: Path) -> str:
 
 def port(entry: dict, refresh: bool, dry_run: bool, force: bool) -> tuple[bool, str]:
     """Convert one page. Returns (ok, status)."""
+    if entry.get("frozen"):
+        # Hand-maintained since its port. The converter would discard those edits.
+        return True, "frozen (hand-maintained, skipped)"
+
     page_path = DOCS_DIR / entry["path"]
     source_text = load_source(entry["source"], cache_name(entry["path"]), refresh)
 
diff --git a/scripts/sources.toml b/scripts/sources.toml
index 6f2b0ac..50dac37 100644
--- a/scripts/sources.toml
+++ b/scripts/sources.toml
@@ -1,12 +1,19 @@
 # Upstream source for every ported page.
 #
-# Consumed by scripts/port_docs.py, and the origin of the `source_url` frontmatter added in
-# PLAN.md Step 3.1. A page listed here is a straight port and may be re-converted freely.
-# A page NOT listed here is either hand-authored or hand-edited -- never re-convert it.
+# Consumed by scripts/port_docs.py and scripts/check_fidelity.py, and the origin of the
+# `source_url` frontmatter added in PLAN.md Step 3.1.
 #
-# Deliberately absent:
+#   frozen = true  -> HAND-MAINTAINED. Never re-convert; the file on disk is the source of
+#                     truth. port_docs.py skips it and check_fidelity.py ignores it, because
+#                     divergence from upstream is intended. The `source` line is kept purely
+#                     for provenance. All of models/** and get-started/** are frozen as of
+#                     2026-08-03 -- see "Hand-maintained sections" in PLAN.md.
+#   (no frozen)    -> a straight port; the converter may rewrite it freely.
+#
+# Set `frozen = true` as soon as anyone starts hand-editing a page.
+#
+# Deliberately absent entirely (never ported from upstream):
 #   models/index.md          hand-authored hub page
-#   get-started/*            reviewed and hand-edited by Maarten (Step 0.3 = targeted repair)
 #   cookbook.md, index.md    authored for this site
 #
 # `assets` is the subdirectory under docs/assets/ that inline base64 images are extracted to.
@@ -18,14 +25,17 @@
 [[page]]
 path = "get-started/setup.md"
 source = "https://ai.google.dev/gemma/docs/setup.md.txt"
+frozen = true
 
 [[page]]
 path = "get-started/prompt-structure.md"
 source = "https://ai.google.dev/gemma/docs/core/prompt-structure.md.txt"
+frozen = true
 
 [[page]]
 path = "get-started/capabilities/mtp.md"
 source = "https://ai.google.dev/gemma/docs/mtp/overview.md.txt"
+frozen = true
 
 # --- Run --------------------------------------------------------------------------------------
 
@@ -64,170 +74,208 @@ assets = "learn"
 [[page]]
 path = "models/gemma-aps/index.md"
 source = "https://ai.google.dev/gemma/docs/gemma-aps.md.txt"
+frozen = true
 
 [[page]]
 path = "models/gemma-4/index.md"
 source = "https://ai.google.dev/gemma/docs/core.md.txt"
+frozen = true
 
 [[page]]
 path = "models/gemma-4/model-card.md"
 source = "https://ai.google.dev/gemma/docs/core/model_card_4.md.txt"
+frozen = true
 
 [[page]]
 path = "models/gemma-3/index.md"
 source = "https://ai.google.dev/gemma/docs/core/model_card_3.md.txt"
+frozen = true
 
 [[page]]
 path = "models/gemma-2/index.md"
 source = "https://ai.google.dev/gemma/docs/core/model_card_2.md.txt"
+frozen = true
 
 [[page]]
 path = "models/gemma-1/index.md"
 source = "https://ai.google.dev/gemma/docs/core/model_card.md.txt"
+frozen = true
 
 [[page]]
 path = "models/releases.md"
 source = "https://ai.google.dev/gemma/docs/releases.md.txt"
+frozen = true
 
 # --- Gemma 3n ---------------------------------------------------------------------------------
 
 [[page]]
 path = "models/gemma-3n/index.md"
 source = "https://ai.google.dev/gemma/docs/gemma-3n.md.txt"
+frozen = true
 
 [[page]]
 path = "models/gemma-3n/model-card.md"
 source = "https://ai.google.dev/gemma/docs/gemma-3n/model_card.md.txt"
+frozen = true
 
 # --- DiffusionGemma ---------------------------------------------------------------------------
 
 [[page]]
 path = "models/diffusiongemma/index.md"
 source = "https://ai.google.dev/gemma/docs/diffusiongemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/diffusiongemma/model-card.md"
 source = "https://ai.google.dev/gemma/docs/diffusiongemma/model_card.md.txt"
+frozen = true
 
 [[page]]
 path = "models/diffusiongemma/explained.md"
 source = "https://ai.google.dev/gemma/docs/diffusiongemma/explained.md.txt"
+frozen = true
 
 [[page]]
 path = "models/diffusiongemma/inference.md"
 source = "https://ai.google.dev/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf.md.txt"
 assets = "diffusiongemma"
+frozen = true
 
 # --- FunctionGemma ----------------------------------------------------------------------------
 
 [[page]]
 path = "models/functiongemma/index.md"
 source = "https://ai.google.dev/gemma/docs/functiongemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/functiongemma/model-card.md"
 source = "https://ai.google.dev/gemma/docs/functiongemma/model_card.md.txt"
+frozen = true
 
 [[page]]
 path = "models/functiongemma/formatting.md"
 source = "https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices.md.txt"
+frozen = true
 
 [[page]]
 path = "models/functiongemma/inference.md"
 source = "https://ai.google.dev/gemma/docs/functiongemma/function-calling-with-hf.md.txt"
+frozen = true
 
 [[page]]
 path = "models/functiongemma/full-sequence.md"
 source = "https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/functiongemma/fine-tuning.md"
 source = "https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma.md.txt"
 assets = "functiongemma"
+frozen = true
 
 # --- EmbeddingGemma ---------------------------------------------------------------------------
 
 [[page]]
 path = "models/embeddinggemma/index.md"
 source = "https://ai.google.dev/gemma/docs/embeddinggemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/embeddinggemma/model-card.md"
 source = "https://ai.google.dev/gemma/docs/embeddinggemma/model_card.md.txt"
+frozen = true
 
 [[page]]
 path = "models/embeddinggemma/inference.md"
 source = "https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.md.txt"
+frozen = true
 
 [[page]]
 path = "models/embeddinggemma/fine-tuning.md"
 source = "https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers.md.txt"
+frozen = true
 
 # --- PaliGemma --------------------------------------------------------------------------------
 
 [[page]]
 path = "models/paligemma/index.md"
 source = "https://ai.google.dev/gemma/docs/paligemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/paligemma/model-card-v1.md"
 source = "https://ai.google.dev/gemma/docs/paligemma/model-card.md.txt"
+frozen = true
 
 [[page]]
 path = "models/paligemma/model-card-v2.md"
 source = "https://ai.google.dev/gemma/docs/paligemma/model-card-2.md.txt"
+frozen = true
 
 [[page]]
 path = "models/paligemma/prompting.md"
 source = "https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions.md.txt"
+frozen = true
 
 [[page]]
 path = "models/paligemma/inference.md"
 source = "https://ai.google.dev/gemma/docs/paligemma/inference-with-keras.md.txt"
 assets = "paligemma"
+frozen = true
 
 [[page]]
 path = "models/paligemma/fine-tuning.md"
 source = "https://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma.md.txt"
 assets = "paligemma"
+frozen = true
 
 # --- ShieldGemma ------------------------------------------------------------------------------
 
 [[page]]
 path = "models/shieldgemma/index.md"
 source = "https://ai.google.dev/gemma/docs/shieldgemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/shieldgemma/model-card-v1.md"
 source = "https://ai.google.dev/gemma/docs/shieldgemma/model_card.md.txt"
+frozen = true
 
 [[page]]
 path = "models/shieldgemma/model-card-v2.md"
 source = "https://ai.google.dev/gemma/docs/shieldgemma/model_card_2.md.txt"
+frozen = true
 
 # --- Research ---------------------------------------------------------------------------------
 
 [[page]]
 path = "models/recurrentgemma/index.md"
 source = "https://ai.google.dev/gemma/docs/recurrentgemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/recurrentgemma/model-card.md"
 source = "https://ai.google.dev/gemma/docs/recurrentgemma/model_card.md.txt"
+frozen = true
 
 [[page]]
 path = "models/recurrentgemma/inference.md"
 source = "https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.md.txt"
+frozen = true
 
 [[page]]
 path = "models/recurrentgemma/fine-tuning.md"
 source = "https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.md.txt"
 assets = "recurrentgemma"
+frozen = true
 
 [[page]]
 path = "models/datagemma/index.md"
 source = "https://ai.google.dev/gemma/docs/datagemma.md.txt"
+frozen = true
 
 [[page]]
 path = "models/gemma-scope/index.md"
 source = "https://ai.google.dev/gemma/docs/gemma_scope.md.txt"
+frozen = true

From cefe31b8fbebb621961e5201224964a2495da8b9 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Tue, 4 Aug 2026 09:57:51 +0200
Subject: [PATCH 09/20] Standardize styling and components page

---
 .lintbaseline.json               |   3 +-
 PLAN.md                          |  70 +++++++++-
 docs/contributing/components.md  | 191 +++++++++++++++++++++++++++
 docs/stylesheets/code.css        |  16 ++-
 docs/stylesheets/components.css  | 196 ++++++++++++++++++++++++++--
 docs/stylesheets/cookbook.css    |  60 ---------
 docs/stylesheets/gemma.css       | 157 +++++++++++++++++++++++
 docs/stylesheets/home-chrome.css | 120 +++++++++++++++++
 docs/stylesheets/home-stage.css  |  81 ++++++++++++
 docs/stylesheets/home.css        | 214 +++----------------------------
 docs/stylesheets/motion.css      |  79 ++++++++++++
 docs/stylesheets/tokens.css      |  99 +++-----------
 scripts/lint_docs.py             |  67 +++++++++-
 zensical.toml                    |  17 ++-
 14 files changed, 1010 insertions(+), 360 deletions(-)
 create mode 100644 docs/contributing/components.md
 delete mode 100644 docs/stylesheets/cookbook.css
 create mode 100644 docs/stylesheets/gemma.css
 create mode 100644 docs/stylesheets/home-chrome.css
 create mode 100644 docs/stylesheets/home-stage.css
 create mode 100644 docs/stylesheets/motion.css

diff --git a/.lintbaseline.json b/.lintbaseline.json
index d782861..5f670d8 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -10,7 +10,8 @@
     "fences",
     "admonitions",
     "size",
-    "modeldata"
+    "modeldata",
+    "css"
   ],
   "count": 136,
   "known": [
diff --git a/PLAN.md b/PLAN.md
index 7ab92c3..23b3eb7 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -567,7 +567,75 @@ Cost is ~195 path updates (39 files, 90 outbound links, 61 inbound, 44 asset ref
 
 ## Phase 1 — Design system
 
-### Step 1.1 — Token layer
+### Step 1.1 — Token layer ✅
+
+- [x] `docs/stylesheets/gemma.css` — palette, spacing (1–7), radius (sm/md/lg/pill),
+      shadow (sm/md/lg), chat-token categories, all with dark-scheme variants.
+- [x] Material's `--md-*` primary/accent/link variables bound to Gemma tokens, so component
+      sheets never name a brand colour.
+- [x] `code.css`, `tokens.css` and the landing-page sheets refactored to consume tokens.
+- [x] `cookbook.css` folded into `components.css` as a generalised card grid
+      (`.cookbook-*` kept as aliases — converter output and existing pages emit them).
+- [x] New **`css`** lint check: raw hex only in `gemma.css`, every sheet registered in
+      `extra_css`, none over 250 lines.
+- **Verify:** `uv run python scripts/lint_docs.py --check css` → zero.
+- **Done when:** ✅ zero hex outside `gemma.css`; every sheet under 250 lines.
+
+**Contrast drove a real decision.** Measured against the palette:
+
+| Pairing | Ratio | |
+|---|---|---|
+| `#3186FF` on white | 3.51:1 | **fails AA for body text** |
+| `#346BF1` on white | 4.65:1 | passes |
+| `#4FA0FF` on ink | 5.98:1 | passes |
+
+So `--gemma-blue` is a **fill** colour — buttons, bars, icons, step markers — and never body
+text. Text uses `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in
+dark. The header uses `blue-deep` for the same reason.
+
+**Stylesheet layout** (9 files, largest 229 lines):
+
+| File | Owns |
+|---|---|
+| `gemma.css` | Tokens. **Only file with raw hex.** |
+| `layout.css` | Page grid, sidebars, typography scale |
+| `components.css` | Cards, badges, figures, steps, embeds |
+| `code.css` | Code and output blocks |
+| `tokens.css` | Chat-token badges |
+| `home-chrome.css` | Landing page: header, tabs, search |
+| `home-stage.css` | Landing page: backdrop |
+| `home.css` | Landing page: hero and feature grid |
+| `motion.css` | Transitions + reduced-motion opt-out |
+
+`home.css` was 367 lines and got split three ways — the `css` check caught it immediately,
+which is the check earning its keep on day one.
+
+### Step 1.2 — Component library ✅
+
+- [x] Card grid, model badges (`--accent`, `--muted`), framed figures with light/dark image
+      swap, numbered steps with a connecting rail, video embeds, model-card header.
+- [x] `docs/contributing/components.md` — every component with a **live example** plus the
+      markup to copy, the colour-token table with contrast guidance, and the stylesheet map.
+      Added to nav under *Contributing*.
+- **Verify:** the components page renders every component in both themes.
+
+`.badge--muted` exists for the Step 4.2 switcher: a capability the selected model lacks is
+struck through rather than hidden, so the reader learns the limitation instead of it silently
+vanishing.
+
+### Step 1.3 — Motion ✅
+
+- [x] `motion.css` — all transitions in one file, so "does this site animate?" has a single
+      answer and the `prefers-reduced-motion` opt-out cannot be forgotten in a component sheet.
+- [x] Blanket reduced-motion rule; anything added later inherits it.
+- [x] Focus rings never animate — keyboard users need them immediately.
+- **Verify:** enable *Reduce motion* in OS settings, reload, confirm hover effects are instant.
+
+Motion is feedback (something is interactive, something changed), never decoration.
+
+---
+
+### Step 1.1 — Token layer (original spec)
 
 **Stylesheets stay modular — many small files, not one monolith.** Each file owns one concern,
 opens with a comment saying what it owns and what it must not touch, and stays small enough for
diff --git a/docs/contributing/components.md b/docs/contributing/components.md
new file mode 100644
index 0000000..33bb77b
--- /dev/null
+++ b/docs/contributing/components.md
@@ -0,0 +1,191 @@
+---
+title: Components
+description: The reusable building blocks these docs are made from, with live examples and the markup to copy.
+---
+
+# Components
+
+Every visual element on this site comes from this catalogue. Build pages from these rather than
+writing new CSS — if something you need isn't here, add it to
+`docs/stylesheets/components.css` and document it on this page.
+
+!!! note
+    Colours, spacing, radii and shadows are **tokens**, defined once in
+    `docs/stylesheets/gemma.css`. It is the only stylesheet allowed to contain a raw hex value,
+    and `scripts/lint_docs.py --check css` enforces that.
+
+## Colour tokens
+
+| Token | Use |
+|---|---|
+| `--gemma-blue` | Fills: buttons, bars, icons, step markers. **Not body text** — 3.51:1 on white fails WCAG AA. |
+| `--gemma-blue-deep` | Text on light surfaces. 4.65:1 on white, passes AA. |
+| `--gemma-blue-light` | Text on dark surfaces. 5.98:1 on ink, passes AA. |
+| `--gemma-white`, `--gemma-ink` | Surfaces. |
+| `--gemma-accent` | Whichever of the above is text-safe in the current scheme. **Prefer this.** |
+| `--gemma-accent-fill` | Scheme-aware fill colour. |
+
+Scales: `--gemma-space-1…7`, `--gemma-radius-sm|md|lg|pill`, `--gemma-shadow-sm|md|lg`.
+
+## Cards
+
+A grid of links. Used on the cookbook, model overviews and ecosystem pages.
+
+<div class="card-grid">
+<a class="card" href="#cards">
+  <div class="card-title">Card title <span class="card-ext">↗</span></div>
+  <p class="card-desc">One or two lines describing where this card leads.</p>
+</a>
+<a class="card" href="#cards">
+  <div class="card-title">Second card</div>
+  <p class="card-desc">The grid reflows automatically and collapses to one column on mobile.</p>
+</a>
+</div>
+
+```html
+<div class="card-grid">
+<a class="card" href="…">
+  <div class="card-title">Card title <span class="card-ext">↗</span></div>
+  <p class="card-desc">One or two lines describing where this card leads.</p>
+</a>
+</div>
+```
+
+`.cookbook-grid` / `.cookbook-card` are aliases kept for converter output and existing pages.
+Use `.card-grid` / `.card` in anything new.
+
+## Badges
+
+Inline chips for model attributes. Drive them from `data/models.yml` so a badge can never
+disagree with the tables.
+
+<p>
+  <span class="badge">12B</span>
+  <span class="badge">256K context</span>
+  <span class="badge badge--accent">Recommended</span>
+  <span class="badge badge--muted">Audio</span>
+</p>
+
+```html
+<span class="badge">12B</span>
+<span class="badge badge--accent">Recommended</span>
+<span class="badge badge--muted">Audio</span>   <!-- capability this model lacks -->
+```
+
+`--muted` is how the model switcher (Step 4.2) will show a capability the selected model does
+not have — struck through rather than hidden, so the reader learns the limitation.
+
+## Steps
+
+A numbered walkthrough. Built on an ordered list, so it still reads correctly without CSS.
+
+<ol class="steps" markdown>
+<li markdown>
+**Install the dependencies**
+
+Anything can go inside a step, including code blocks.
+</li>
+<li markdown>
+**Run the model**
+
+The connecting rail is drawn automatically between steps.
+</li>
+</ol>
+
+```html
+<ol class="steps" markdown>
+<li markdown>
+**Step title**
+
+Body content, including code blocks.
+</li>
+</ol>
+```
+
+The `markdown` attribute is required — it tells `md_in_html` to keep parsing Markdown inside
+the tag.
+
+## Figures
+
+A framed diagram with a caption.
+
+```html
+<figure class="figure" markdown>
+![Alt text](../assets/example.png)
+<figcaption>What the diagram shows.</figcaption>
+</figure>
+```
+
+For a diagram that needs different artwork per theme, mark the pair and let CSS choose:
+
+```html
+![Alt text](../assets/diagram-light.png){ .only-light }
+![Alt text](../assets/diagram-dark.png){ .only-dark }
+```
+
+## Video
+
+Emitted automatically by `scripts/convert_notebook.py` from a standalone `[Video](youtube…)`
+link. Full content width, 16:9, lazy-loaded, privacy-mode host.
+
+```html
+<div class="video-embed">
+  <iframe src="https://www.youtube-nocookie.com/embed/VIDEO_ID"
+          title="Video" loading="lazy" allowfullscreen></iframe>
+</div>
+```
+
+## Model-card header
+
+Centres the banner, resource links and License/Authors line on a model card. Also emitted by
+the converter — you shouldn't need to write it by hand.
+
+```html
+<div class="model-card-header" markdown>
+![Banner](../assets/banner.png)
+
+[Hugging Face](…) \| [GitHub](…)
+
+**License** : [Apache 2.0](…)
+</div>
+```
+
+## Admonitions
+
+Standard Zensical admonitions, used throughout. The converter produces these from upstream's
+`> NOTE:` blockquotes and `> [!NOTE]` callouts.
+
+!!! note
+    Neutral supporting information.
+
+!!! tip
+    A recommendation or shortcut.
+
+!!! warning
+    Something that will cost the reader time or money if ignored.
+
+## Motion
+
+All transitions live in `docs/stylesheets/motion.css`, including a blanket
+`prefers-reduced-motion` opt-out — anything added later inherits it. Motion is feedback
+(something is interactive, something changed), never decoration.
+
+To check the reduced-motion path, enable *Reduce motion* in your OS accessibility settings and
+reload; hover effects should become instant.
+
+## Stylesheet layout
+
+| File | Owns |
+|---|---|
+| `gemma.css` | Tokens. The only file with raw hex. |
+| `layout.css` | Page grid, sidebars, typography scale |
+| `components.css` | Everything on this page |
+| `code.css` | Code and output blocks |
+| `tokens.css` | Chat-token badges (`<start_of_turn>` etc.) |
+| `home-chrome.css` | Landing page: header, tabs, search |
+| `home-stage.css` | Landing page: backdrop |
+| `home.css` | Landing page: hero and feature grid |
+| `motion.css` | Transitions and the reduced-motion opt-out |
+
+Each stays under 250 lines so it can be read end-to-end before editing. Adding a ninth
+stylesheet for a new concern is the right move; growing one into a catch-all is not.
diff --git a/docs/stylesheets/code.css b/docs/stylesheets/code.css
index a5de29d..263b2bd 100644
--- a/docs/stylesheets/code.css
+++ b/docs/stylesheets/code.css
@@ -1,8 +1,10 @@
-/* Lighter background for output blocks to distinguish from code */
-.output code {
-  background-color: #EDF7FF;
-}
+/* ===================================================================
+   CODE — output blocks
+   Owns: distinguishing model/program output from source code.
+   Colours come from gemma.css.
+   =================================================================== */
 
-[data-md-color-scheme="slate"] .output code {
-  background-color: #1a1a2e;
-}
\ No newline at end of file
+/* Output blocks read as results, not as code you should copy. */
+.md-typeset .output code {
+  background-color: var(--gemma-output-bg);
+}
diff --git a/docs/stylesheets/components.css b/docs/stylesheets/components.css
index 4fea01c..b8fca62 100644
--- a/docs/stylesheets/components.css
+++ b/docs/stylesheets/components.css
@@ -1,15 +1,191 @@
 /* ===================================================================
-   Components — reusable blocks emitted by scripts/convert_notebook.py.
+   COMPONENTS — the reusable blocks pages are built from.
 
-   Owns: video embeds, model-card header alignment.
-   Does NOT own: colours (Phase 1 token layer), page grid (layout.css),
-   code blocks (code.css), chat tokens (tokens.css).
+   Owns: card grids, model badges, figures, video embeds, model-card
+         headers, step walkthroughs.
+   Does NOT own: colours or scales (gemma.css), page grid (layout.css),
+         code blocks (code.css), chat tokens (tokens.css),
+         animation (motion.css).
 
-   Deliberately minimal — no borders, shadows or radii here. Anything
-   that restyles page content (images, links) belongs to the Phase 1
-   design system, not to a converter-output shim.
+   Every component is documented with live examples in
+   docs/contributing/components.md — add examples there when you add one.
    =================================================================== */
 
+/* -------------------------------------------------------------------
+   Card grid
+   `.card-grid` + `.card`. `.cookbook-grid`/`.cookbook-card` are kept as
+   aliases because converter output and existing pages emit them.
+   ------------------------------------------------------------------- */
+.md-typeset .card-grid,
+.md-typeset .cookbook-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(20rem, 1fr));
+  gap: var(--gemma-space-4);
+  margin: var(--gemma-space-5) 0 var(--gemma-space-6);
+}
+
+.md-typeset .card,
+.md-typeset .cookbook-card {
+  display: flex;
+  flex-direction: column;
+  padding: var(--gemma-space-4) var(--gemma-space-5);
+  border: 1px solid var(--md-default-fg-color--lighter);
+  border-radius: var(--gemma-radius-md);
+  background: var(--md-default-bg-color);
+  text-decoration: none !important;
+  color: inherit;
+}
+
+.md-typeset a.card:hover,
+.md-typeset a.cookbook-card:hover {
+  border-color: var(--gemma-accent);
+  background: var(--md-default-fg-color--lightest);
+}
+
+.md-typeset .card-title,
+.md-typeset .cookbook-card-title {
+  margin: 0 0 var(--gemma-space-1);
+  font-size: 0.95rem;
+  font-weight: 700;
+  letter-spacing: -0.01em;
+  color: var(--md-default-fg-color);
+}
+
+/* External-link arrow, inline with the title. */
+.md-typeset .card-ext,
+.md-typeset .cookbook-card-ext {
+  margin-left: 0.3em;
+  font-size: 0.85em;
+  font-weight: 400;
+  opacity: 0.5;
+}
+
+.md-typeset .card-desc,
+.md-typeset .cookbook-card-desc {
+  margin: 0;
+  font-size: 0.85rem;
+  line-height: 1.5;
+  color: var(--md-default-fg-color--light);
+}
+
+@media screen and (max-width: 37.5em) {
+  .md-typeset .card-grid,
+  .md-typeset .cookbook-grid {
+    grid-template-columns: 1fr;
+  }
+}
+
+/* -------------------------------------------------------------------
+   Model badges
+   Inline chips for size, modality and quantization. Content comes from
+   data/models.yml so a badge can never disagree with the tables.
+   ------------------------------------------------------------------- */
+.md-typeset .badge {
+  display: inline-block;
+  padding: 0.1em 0.55em;
+  border: 1px solid var(--md-default-fg-color--lighter);
+  border-radius: var(--gemma-radius-pill);
+  font-size: 0.75em;
+  font-weight: 600;
+  line-height: 1.6;
+  white-space: nowrap;
+  color: var(--md-default-fg-color--light);
+}
+
+.md-typeset .badge + .badge {
+  margin-left: var(--gemma-space-1);
+}
+
+/* Emphasised badge — the current model, the recommended option. */
+.md-typeset .badge--accent {
+  border-color: var(--gemma-accent);
+  color: var(--gemma-accent);
+}
+
+/* Signals a capability the selected model does *not* have. */
+.md-typeset .badge--muted {
+  opacity: 0.55;
+  text-decoration: line-through;
+}
+
+/* -------------------------------------------------------------------
+   Figure
+   A framed image with a caption. Use for diagrams; plain screenshots
+   can stay as bare images.
+   ------------------------------------------------------------------- */
+.md-typeset .figure {
+  margin: var(--gemma-space-5) 0;
+  text-align: center;
+}
+
+.md-typeset .figure img {
+  max-width: 100%;
+  height: auto;
+  border: 1px solid var(--md-default-fg-color--lightest);
+  border-radius: var(--gemma-radius-md);
+}
+
+.md-typeset .figure figcaption,
+.md-typeset .figure .figure-caption {
+  margin-top: var(--gemma-space-2);
+  font-size: 0.8rem;
+  color: var(--md-default-fg-color--light);
+}
+
+/* Dark-mode image swap: mark the pair and only one shows.
+   (Phase 1 groundwork; variants are produced per-diagram as needed.) */
+[data-md-color-scheme="default"] .md-typeset .only-dark,
+[data-md-color-scheme="slate"] .md-typeset .only-light {
+  display: none;
+}
+
+/* -------------------------------------------------------------------
+   Steps
+   A numbered walkthrough where each step is a titled block. Built on an
+   ordered list so it degrades to a plain list without CSS.
+   ------------------------------------------------------------------- */
+.md-typeset .steps {
+  counter-reset: gemma-step;
+  padding-left: 0;
+  list-style: none;
+}
+
+.md-typeset .steps > li {
+  position: relative;
+  margin-left: 0;
+  padding-left: var(--gemma-space-7);
+  padding-bottom: var(--gemma-space-4);
+}
+
+.md-typeset .steps > li::before {
+  counter-increment: gemma-step;
+  content: counter(gemma-step);
+  position: absolute;
+  left: 0;
+  top: 0;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  width: 1.75rem;
+  height: 1.75rem;
+  border-radius: var(--gemma-radius-pill);
+  background: var(--gemma-accent-fill);
+  color: var(--gemma-white);
+  font-size: 0.8rem;
+  font-weight: 700;
+}
+
+/* Connecting rail between steps. */
+.md-typeset .steps > li:not(:last-child)::after {
+  content: "";
+  position: absolute;
+  left: 0.875rem;
+  top: 1.75rem;
+  bottom: 0;
+  width: 1px;
+  background: var(--md-default-fg-color--lighter);
+}
+
 /* -------------------------------------------------------------------
    Video embeds
    Full content width, 16:9. Replaces the plain `[Video](youtube…)`
@@ -18,7 +194,7 @@
 .md-typeset .video-embed {
   position: relative;
   width: 100%;
-  margin: 1.5em 0;
+  margin: var(--gemma-space-5) 0;
   aspect-ratio: 16 / 9;
 }
 
@@ -30,7 +206,6 @@
   border: 0;
 }
 
-/* Older engines without aspect-ratio support. */
 @supports not (aspect-ratio: 16 / 9) {
   .md-typeset .video-embed {
     height: 0;
@@ -41,8 +216,7 @@
 /* -------------------------------------------------------------------
    Model-card header
    Centres the resource links and the License/Authors line, matching the
-   `<p align="center">` upstream uses. Alignment only — the banner image
-   keeps whatever styling the rest of the theme gives it.
+   `<p align="center">` upstream uses. Alignment only.
    ------------------------------------------------------------------- */
 .md-typeset .model-card-header {
   text-align: center;
diff --git a/docs/stylesheets/cookbook.css b/docs/stylesheets/cookbook.css
deleted file mode 100644
index ca1c053..0000000
--- a/docs/stylesheets/cookbook.css
+++ /dev/null
@@ -1,60 +0,0 @@
-/* ===================================================================
-   COOKBOOK — clean card grid
-   =================================================================== */
-
-/* Card grid */
-.cookbook-grid {
-  display: grid;
-  grid-template-columns: repeat(auto-fill, minmax(340px, 1fr));
-  gap: 1rem;
-  margin: 1.5rem 0 2rem;
-}
-
-/* Individual card — simple bordered box */
-.cookbook-card {
-  display: flex;
-  flex-direction: column;
-  padding: 1.2rem 1.3rem;
-  border-radius: 0.5rem;
-  border: 1px solid var(--md-default-fg-color--lighter);
-  background: var(--md-default-bg-color);
-  text-decoration: none !important;
-  transition: background 0.15s ease;
-  cursor: pointer;
-}
-
-.cookbook-card:hover {
-  background: var(--md-default-fg-color--lightest);
-}
-
-/* Card title */
-.cookbook-card-title {
-  font-size: 0.95rem;
-  font-weight: 700;
-  color: var(--md-default-fg-color);
-  margin: 0 0 0.3rem;
-  letter-spacing: -0.01em;
-}
-
-/* External link arrow — inline with title */
-.cookbook-card-title .cookbook-card-ext {
-  font-weight: 400;
-  font-size: 0.85em;
-  opacity: 0.5;
-  margin-left: 0.3em;
-}
-
-/* Card description */
-.cookbook-card-desc {
-  font-size: 0.85rem;
-  color: var(--md-default-fg-color--light);
-  line-height: 1.5;
-  margin: 0;
-}
-
-/* Responsive */
-@media (max-width: 600px) {
-  .cookbook-grid {
-    grid-template-columns: 1fr;
-  }
-}
diff --git a/docs/stylesheets/gemma.css b/docs/stylesheets/gemma.css
new file mode 100644
index 0000000..b7cca58
--- /dev/null
+++ b/docs/stylesheets/gemma.css
@@ -0,0 +1,157 @@
+/* ===================================================================
+   GEMMA TOKEN LAYER
+
+   The only stylesheet permitted to contain raw hex values. Everything
+   else consumes these custom properties. Enforced by:
+
+       uv run python scripts/lint_docs.py --check css
+
+   Loaded first in extra_css so later sheets can build on it.
+   =================================================================== */
+
+:root {
+  /* -----------------------------------------------------------------
+     Brand palette (official Gemma colours)
+
+     Contrast, measured against #FFFFFF and #202124:
+
+       blue        #3186FF   3.51:1 on white  -- large text / fills only
+       blue-deep   #346BF1   4.65:1 on white  -- PASSES AA for body text
+       blue-light  #4FA0FF   5.98:1 on ink    -- PASSES AA in dark mode
+
+     So --gemma-blue is a *fill* colour: buttons, bars, icons, headings.
+     Text on a light surface uses --gemma-blue-deep; text on a dark
+     surface uses --gemma-blue-light. Do not link body text in plain
+     --gemma-blue -- it fails WCAG AA.
+     ----------------------------------------------------------------- */
+  --gemma-blue: #3186FF;
+  --gemma-blue-deep: #346BF1;
+  --gemma-blue-light: #4FA0FF;
+  --gemma-white: #FFFFFF;
+  --gemma-ink: #202124;
+
+  /* Landing-page gradient stops, derived from the palette. */
+  --gemma-ink-raised: #152238;
+  --gemma-navy-900: #0e2042;
+  --gemma-navy-800: #102a5c;
+  --gemma-navy-700: #16366e;
+
+  /* -----------------------------------------------------------------
+     Semantic roles — remapped per colour scheme further down.
+     ----------------------------------------------------------------- */
+  --gemma-accent: var(--gemma-blue-deep);   /* text-safe brand colour   */
+  --gemma-accent-fill: var(--gemma-blue);   /* backgrounds, bars, icons */
+
+  /* -----------------------------------------------------------------
+     Spacing scale (rem). Use these rather than ad-hoc values.
+     ----------------------------------------------------------------- */
+  --gemma-space-1: 0.25rem;
+  --gemma-space-2: 0.5rem;
+  --gemma-space-3: 0.75rem;
+  --gemma-space-4: 1rem;
+  --gemma-space-5: 1.5rem;
+  --gemma-space-6: 2rem;
+  --gemma-space-7: 3rem;
+
+  /* -----------------------------------------------------------------
+     Radius scale
+     ----------------------------------------------------------------- */
+  --gemma-radius-sm: 0.25rem;
+  --gemma-radius-md: 0.5rem;
+  --gemma-radius-lg: 0.75rem;
+  --gemma-radius-pill: 999px;
+
+  /* -----------------------------------------------------------------
+     Elevation. Kept subtle: the docs are text-first.
+     ----------------------------------------------------------------- */
+  --gemma-shadow-sm: 0 1px 2px rgb(32 33 36 / 8%);
+  --gemma-shadow-md: 0 1px 3px rgb(32 33 36 / 10%), 0 4px 12px rgb(32 33 36 / 6%);
+  --gemma-shadow-lg: 0 2px 6px rgb(32 33 36 / 12%), 0 12px 32px rgb(32 33 36 / 10%);
+
+  /* -----------------------------------------------------------------
+     Chat-token badge categories (consumed by tokens.css).
+     Categorical, so they stay visually distinct rather than being
+     derived from the brand blue.
+     ----------------------------------------------------------------- */
+  --gemma-cat-turn-bg: #dbeafe;
+  --gemma-cat-turn-fg: #1e40af;
+  --gemma-cat-turn-border: #93c5fd;
+
+  --gemma-cat-role-bg: #fee2e2;
+  --gemma-cat-role-fg: #991b1b;
+  --gemma-cat-role-border: #fca5a5;
+
+  --gemma-cat-multi-bg: #f3e8ff;
+  --gemma-cat-multi-fg: #6b21a8;
+  --gemma-cat-multi-border: #d8b4fe;
+
+  --gemma-cat-tool-bg: #d1fae5;
+  --gemma-cat-tool-fg: #065f46;
+  --gemma-cat-tool-border: #6ee7b7;
+
+  --gemma-cat-think-bg: #fef3c7;
+  --gemma-cat-think-fg: #92400e;
+  --gemma-cat-think-border: #fde68a;
+
+  /* Output code blocks, tinted apart from ordinary code. */
+  --gemma-output-bg: #EDF7FF;
+}
+
+/* ===================================================================
+   Dark scheme
+   =================================================================== */
+[data-md-color-scheme="slate"] {
+  --gemma-accent: var(--gemma-blue-light);
+  --gemma-accent-fill: var(--gemma-blue-light);
+
+  --gemma-shadow-sm: 0 1px 2px rgb(0 0 0 / 40%);
+  --gemma-shadow-md: 0 1px 3px rgb(0 0 0 / 45%), 0 4px 12px rgb(0 0 0 / 30%);
+  --gemma-shadow-lg: 0 2px 6px rgb(0 0 0 / 50%), 0 12px 32px rgb(0 0 0 / 40%);
+
+  --gemma-cat-turn-bg: #1e3a8a;
+  --gemma-cat-turn-fg: #bfdbfe;
+  --gemma-cat-turn-border: #3b82f6;
+
+  --gemma-cat-role-bg: #7f1d1d;
+  --gemma-cat-role-fg: #fecaca;
+  --gemma-cat-role-border: #ef4444;
+
+  --gemma-cat-multi-bg: #581c87;
+  --gemma-cat-multi-fg: #f3e8ff;
+  --gemma-cat-multi-border: #a855f7;
+
+  --gemma-cat-tool-bg: #064e3b;
+  --gemma-cat-tool-fg: #a7f3d0;
+  --gemma-cat-tool-border: #10b981;
+
+  --gemma-cat-think-bg: #78350f;
+  --gemma-cat-think-fg: #fef3c7;
+  --gemma-cat-think-border: #f59e0b;
+
+  --gemma-output-bg: #1a1a2e;
+}
+
+/* ===================================================================
+   Bind Material's variables to the Gemma palette.
+
+   Everything downstream (links, header, focus rings, selection) picks
+   these up, so component sheets never need to name a brand colour.
+   =================================================================== */
+:root {
+  --md-primary-fg-color: var(--gemma-blue-deep);
+  --md-primary-fg-color--light: var(--gemma-blue-light);
+  --md-primary-fg-color--dark: var(--gemma-navy-800);
+  --md-primary-bg-color: var(--gemma-white);
+
+  --md-accent-fg-color: var(--gemma-blue);
+  --md-typeset-a-color: var(--gemma-accent);
+}
+
+[data-md-color-scheme="slate"] {
+  --md-primary-fg-color: var(--gemma-ink);
+  --md-primary-fg-color--light: var(--gemma-blue-light);
+  --md-primary-fg-color--dark: var(--gemma-navy-900);
+
+  --md-accent-fg-color: var(--gemma-blue-light);
+  --md-typeset-a-color: var(--gemma-accent);
+}
diff --git a/docs/stylesheets/home-chrome.css b/docs/stylesheets/home-chrome.css
new file mode 100644
index 0000000..c9fa082
--- /dev/null
+++ b/docs/stylesheets/home-chrome.css
@@ -0,0 +1,120 @@
+/* ===================================================================
+   Landing page — header, tabs and search
+
+   Owns: the transparent navbar, top tabs, search field and modal, and
+         hiding the palette toggle, on the landing page only.
+   Scoped to the landing page via `:has(.gemma-teaser)` so no other
+   page is affected. Colours come from gemma.css.
+   =================================================================== */
+
+/* 1. Hide Dark/Light Mode Palette Toggle on Homepage */
+.gemma-home-header-wrapper .md-header__option,
+.gemma-home-header-wrapper form[data-md-component="palette"] {
+  display: none !important;
+}
+
+/* 2. Transparent Navbar Styling on Homepage (Scoped to wrapper so other pages never get affected!) */
+.gemma-home-header-wrapper .md-header {
+  background-color: transparent !important;
+  box-shadow: none !important;
+  color: var(--gemma-white) !important;
+  border-bottom: 1px solid rgba(255, 255, 255, 0.15) !important;
+  backdrop-filter: blur(8px);
+  -webkit-backdrop-filter: blur(8px);
+}
+
+.gemma-home-header-wrapper .md-header__button,
+.gemma-home-header-wrapper .md-header__topic,
+.gemma-home-header-wrapper .md-header__title,
+.gemma-home-header-wrapper .md-header__title *,
+.gemma-home-header-wrapper .md-logo {
+  color: var(--gemma-white) !important;
+}
+
+/* Top Tabs Navigation on Homepage */
+.gemma-home-header-wrapper .md-tabs {
+  background-color: transparent !important;
+  box-shadow: none !important;
+  border-bottom: 1px solid rgba(255, 255, 255, 0.1) !important;
+}
+
+.gemma-home-header-wrapper .md-tabs__item,
+.gemma-home-header-wrapper .md-tabs__link {
+  color: rgba(255, 255, 255, 0.8) !important;
+  font-weight: 500;
+  transition: color 0.2s ease, opacity 0.2s ease;
+}
+
+.gemma-home-header-wrapper .md-tabs__link:hover,
+.gemma-home-header-wrapper .md-tabs__link:focus,
+.gemma-home-header-wrapper .md-tabs__item--active .md-tabs__link {
+  color: var(--gemma-white) !important;
+  opacity: 1 !important;
+}
+
+.gemma-home-header-wrapper .md-tabs__item--active {
+  border-bottom: 2px solid var(--gemma-blue-light) !important;
+}
+
+/* 3. Search Bar Styling on Homepage */
+.gemma-home-header-wrapper .md-search__button {
+  color: var(--gemma-white) !important;
+  background: rgba(255, 255, 255, 0.12) !important;
+  border-radius: 9999px !important;
+  border: 1px solid rgba(255, 255, 255, 0.2) !important;
+  transition: all 0.2s ease;
+}
+
+.gemma-home-header-wrapper .md-search__button:hover {
+  background: rgba(255, 255, 255, 0.2) !important;
+  border-color: rgba(255, 255, 255, 0.4) !important;
+}
+
+/* Search Modal / Dropdown when clicked on homepage */
+.gemma-home-header-wrapper .md-search__inner {
+  background-color: var(--gemma-ink) !important;
+  color: var(--gemma-white) !important;
+  border: 1px solid rgba(79, 160, 255, 0.4) !important;
+  box-shadow: 0 20px 50px rgba(0, 0, 0, 0.8) !important;
+}
+
+.gemma-home-header-wrapper .md-search__form {
+  background-color: var(--gemma-ink-raised) !important;
+}
+
+.gemma-home-header-wrapper .md-search__input {
+  color: var(--gemma-white) !important;
+  background-color: transparent !important;
+}
+
+.gemma-home-header-wrapper .md-search__input::placeholder {
+  color: rgba(255, 255, 255, 0.55) !important;
+}
+
+.gemma-home-header-wrapper .md-search__icon {
+  color: var(--gemma-blue-light) !important;
+}
+
+.gemma-home-header-wrapper .md-search__output {
+  background-color: var(--gemma-ink) !important;
+}
+
+.gemma-home-header-wrapper .md-search-result__title,
+.gemma-home-header-wrapper .md-search-result__article,
+.gemma-home-header-wrapper .md-search-result__link {
+  color: var(--gemma-white) !important;
+}
+
+.gemma-home-header-wrapper .md-search-result__link:hover,
+.gemma-home-header-wrapper .md-search-result__link--active {
+  background-color: rgba(49, 134, 255, 0.25) !important;
+}
+
+.gemma-home-header-wrapper .md-search-result__teaser {
+  color: rgba(255, 255, 255, 0.7) !important;
+}
+
+.gemma-home-header-wrapper mark {
+  background-color: rgba(79, 160, 255, 0.35) !important;
+  color: var(--gemma-blue-light) !important;
+}
diff --git a/docs/stylesheets/home-stage.css b/docs/stylesheets/home-stage.css
new file mode 100644
index 0000000..c8ed26a
--- /dev/null
+++ b/docs/stylesheets/home-stage.css
@@ -0,0 +1,81 @@
+/* ===================================================================
+   Landing page — backdrop
+
+   Owns: the full-viewport gradient, ambient glow blobs and dot-matrix
+         overlay behind the hero.
+   Scoped to the landing page via `:has(.gemma-teaser)` so no other
+   page is affected. Colours come from gemma.css.
+   =================================================================== */
+
+/* 4. Homepage Main Container & Fixed Background Gradients covering entire viewport! */
+.gemma-home-container {
+  position: relative;
+  min-height: calc(100vh - 110px);
+  width: 100%;
+  display: flex;
+  flex-direction: column;
+  justify-content: space-between;
+  padding: 1rem 2rem 0.5rem;
+  overflow: hidden;
+  box-sizing: border-box;
+  color: var(--gemma-white);
+}
+
+.gemma-home-bg {
+  position: fixed;
+  top: 0;
+  left: 0;
+  width: 100vw;
+  height: 100vh;
+  z-index: 0;
+  pointer-events: none;
+  background: 
+    radial-gradient(circle at 80% 25%, rgba(79, 160, 255, 0.6) 0%, transparent 55%),
+    radial-gradient(circle at 20% 75%, rgba(49, 134, 255, 0.65) 0%, transparent 60%),
+    radial-gradient(circle at 50% 50%, rgba(52, 107, 241, 0.7) 0%, transparent 70%),
+    linear-gradient(135deg, var(--gemma-navy-800) 0%, var(--gemma-navy-700) 45%, var(--gemma-navy-900) 75%, var(--gemma-ink) 100%);
+}
+
+/* Ambient glow blobs in Gemma blues */
+.gemma-home-glow {
+  position: absolute;
+  border-radius: 50%;
+  filter: blur(80px);
+  opacity: 0.6;
+}
+.gemma-glow-1 {
+  width: 500px;
+  height: 500px;
+  background: var(--gemma-blue);
+  top: -100px;
+  left: 10%;
+}
+.gemma-glow-2 {
+  width: 600px;
+  height: 600px;
+  background: var(--gemma-blue-deep);
+  top: 20%;
+  right: -100px;
+}
+.gemma-glow-3 {
+  width: 450px;
+  height: 450px;
+  background: var(--gemma-blue-light);
+  bottom: -50px;
+  left: 35%;
+}
+
+/* Subtle dot matrix pattern overlay */
+.gemma-home-grid {
+  position: absolute;
+  top: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  background-image: 
+    radial-gradient(rgba(255, 255, 255, 0.1) 1px, transparent 1px),
+    radial-gradient(rgba(79, 160, 255, 0.12) 1px, transparent 1px);
+  background-size: 40px 40px;
+  background-position: 0 0, 20px 20px;
+  opacity: 0.7;
+}
diff --git a/docs/stylesheets/home.css b/docs/stylesheets/home.css
index 27a148a..279ec21 100644
--- a/docs/stylesheets/home.css
+++ b/docs/stylesheets/home.css
@@ -1,192 +1,11 @@
-/* ==========================================================================
-   Gemma Custom Landing Page Styling
-   Colors: #3186FF, #346BF1, #4FA0FF, #FFFFFF, #202124
-   ========================================================================== */
+/* ===================================================================
+   Landing page — hero and feature grid
 
-/* 1. Hide Dark/Light Mode Palette Toggle on Homepage */
-.gemma-home-header-wrapper .md-header__option,
-.gemma-home-header-wrapper form[data-md-component="palette"] {
-  display: none !important;
-}
-
-/* 2. Transparent Navbar Styling on Homepage (Scoped to wrapper so other pages never get affected!) */
-.gemma-home-header-wrapper .md-header {
-  background-color: transparent !important;
-  box-shadow: none !important;
-  color: #FFFFFF !important;
-  border-bottom: 1px solid rgba(255, 255, 255, 0.15) !important;
-  backdrop-filter: blur(8px);
-  -webkit-backdrop-filter: blur(8px);
-}
-
-.gemma-home-header-wrapper .md-header__button,
-.gemma-home-header-wrapper .md-header__topic,
-.gemma-home-header-wrapper .md-header__title,
-.gemma-home-header-wrapper .md-header__title *,
-.gemma-home-header-wrapper .md-logo {
-  color: #FFFFFF !important;
-}
-
-/* Top Tabs Navigation on Homepage */
-.gemma-home-header-wrapper .md-tabs {
-  background-color: transparent !important;
-  box-shadow: none !important;
-  border-bottom: 1px solid rgba(255, 255, 255, 0.1) !important;
-}
-
-.gemma-home-header-wrapper .md-tabs__item,
-.gemma-home-header-wrapper .md-tabs__link {
-  color: rgba(255, 255, 255, 0.8) !important;
-  font-weight: 500;
-  transition: color 0.2s ease, opacity 0.2s ease;
-}
-
-.gemma-home-header-wrapper .md-tabs__link:hover,
-.gemma-home-header-wrapper .md-tabs__link:focus,
-.gemma-home-header-wrapper .md-tabs__item--active .md-tabs__link {
-  color: #FFFFFF !important;
-  opacity: 1 !important;
-}
-
-.gemma-home-header-wrapper .md-tabs__item--active {
-  border-bottom: 2px solid #4FA0FF !important;
-}
-
-/* 3. Search Bar Styling on Homepage */
-.gemma-home-header-wrapper .md-search__button {
-  color: #FFFFFF !important;
-  background: rgba(255, 255, 255, 0.12) !important;
-  border-radius: 9999px !important;
-  border: 1px solid rgba(255, 255, 255, 0.2) !important;
-  transition: all 0.2s ease;
-}
-
-.gemma-home-header-wrapper .md-search__button:hover {
-  background: rgba(255, 255, 255, 0.2) !important;
-  border-color: rgba(255, 255, 255, 0.4) !important;
-}
-
-/* Search Modal / Dropdown when clicked on homepage */
-.gemma-home-header-wrapper .md-search__inner {
-  background-color: #202124 !important;
-  color: #FFFFFF !important;
-  border: 1px solid rgba(79, 160, 255, 0.4) !important;
-  box-shadow: 0 20px 50px rgba(0, 0, 0, 0.8) !important;
-}
-
-.gemma-home-header-wrapper .md-search__form {
-  background-color: #152238 !important;
-}
-
-.gemma-home-header-wrapper .md-search__input {
-  color: #FFFFFF !important;
-  background-color: transparent !important;
-}
-
-.gemma-home-header-wrapper .md-search__input::placeholder {
-  color: rgba(255, 255, 255, 0.55) !important;
-}
-
-.gemma-home-header-wrapper .md-search__icon {
-  color: #4FA0FF !important;
-}
-
-.gemma-home-header-wrapper .md-search__output {
-  background-color: #202124 !important;
-}
-
-.gemma-home-header-wrapper .md-search-result__title,
-.gemma-home-header-wrapper .md-search-result__article,
-.gemma-home-header-wrapper .md-search-result__link {
-  color: #FFFFFF !important;
-}
-
-.gemma-home-header-wrapper .md-search-result__link:hover,
-.gemma-home-header-wrapper .md-search-result__link--active {
-  background-color: rgba(49, 134, 255, 0.25) !important;
-}
-
-.gemma-home-header-wrapper .md-search-result__teaser {
-  color: rgba(255, 255, 255, 0.7) !important;
-}
-
-.gemma-home-header-wrapper mark {
-  background-color: rgba(79, 160, 255, 0.35) !important;
-  color: #4FA0FF !important;
-}
-
-/* 4. Homepage Main Container & Fixed Background Gradients covering entire viewport! */
-.gemma-home-container {
-  position: relative;
-  min-height: calc(100vh - 110px);
-  width: 100%;
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  padding: 1rem 2rem 0.5rem;
-  overflow: hidden;
-  box-sizing: border-box;
-  color: #FFFFFF;
-}
-
-.gemma-home-bg {
-  position: fixed;
-  top: 0;
-  left: 0;
-  width: 100vw;
-  height: 100vh;
-  z-index: 0;
-  pointer-events: none;
-  background: 
-    radial-gradient(circle at 80% 25%, rgba(79, 160, 255, 0.6) 0%, transparent 55%),
-    radial-gradient(circle at 20% 75%, rgba(49, 134, 255, 0.65) 0%, transparent 60%),
-    radial-gradient(circle at 50% 50%, rgba(52, 107, 241, 0.7) 0%, transparent 70%),
-    linear-gradient(135deg, #102a5c 0%, #16366e 45%, #0e2042 75%, #202124 100%);
-}
-
-/* Ambient glow blobs in Gemma blues */
-.gemma-home-glow {
-  position: absolute;
-  border-radius: 50%;
-  filter: blur(80px);
-  opacity: 0.6;
-}
-.gemma-glow-1 {
-  width: 500px;
-  height: 500px;
-  background: #3186FF;
-  top: -100px;
-  left: 10%;
-}
-.gemma-glow-2 {
-  width: 600px;
-  height: 600px;
-  background: #346BF1;
-  top: 20%;
-  right: -100px;
-}
-.gemma-glow-3 {
-  width: 450px;
-  height: 450px;
-  background: #4FA0FF;
-  bottom: -50px;
-  left: 35%;
-}
-
-/* Subtle dot matrix pattern overlay */
-.gemma-home-grid {
-  position: absolute;
-  top: 0;
-  left: 0;
-  width: 100%;
-  height: 100%;
-  background-image: 
-    radial-gradient(rgba(255, 255, 255, 0.1) 1px, transparent 1px),
-    radial-gradient(rgba(79, 160, 255, 0.12) 1px, transparent 1px);
-  background-size: 40px 40px;
-  background-position: 0 0, 20px 20px;
-  opacity: 0.7;
-}
+   Owns: the two-column hero layout, call-to-action, feature cards and
+         the landing-page footer.
+   Scoped to the landing page via `:has(.gemma-teaser)` so no other
+   page is affected. Colours come from gemma.css.
+   =================================================================== */
 
 /* 5. Homepage Content Layout (2-Column on Desktop for No-Scroll Viewport Fit) */
 .gemma-home-content {
@@ -233,7 +52,7 @@
   border-radius: 9999px;
   background: rgba(49, 134, 255, 0.2);
   border: 1px solid rgba(79, 160, 255, 0.4);
-  color: #FFFFFF;
+  color: var(--gemma-white);
   font-size: 0.85rem;
   font-weight: 600;
   letter-spacing: 0.02em;
@@ -244,8 +63,8 @@
   width: 8px;
   height: 8px;
   border-radius: 50%;
-  background-color: #4FA0FF;
-  box-shadow: 0 0 10px #4FA0FF;
+  background-color: var(--gemma-blue-light);
+  box-shadow: 0 0 10px var(--gemma-blue-light);
 }
 
 .gemma-title {
@@ -253,13 +72,13 @@
   font-weight: 800;
   line-height: 1.1;
   letter-spacing: -0.03em;
-  color: #FFFFFF;
+  color: var(--gemma-white);
   margin: 0;
   text-shadow: 0 0 30px rgba(0, 0, 0, 0.6);
 }
 
 .gemma-title-gradient {
-  background: linear-gradient(135deg, #FFFFFF 0%, #4FA0FF 45%, #3186FF 100%);
+  background: linear-gradient(135deg, var(--gemma-white) 0%, var(--gemma-blue-light) 45%, var(--gemma-blue) 100%);
   -webkit-background-clip: text;
   -webkit-text-fill-color: transparent;
   background-clip: text;
@@ -311,7 +130,7 @@
 .gemma-card:hover,
 .gemma-card:focus {
   background: rgba(49, 134, 255, 0.28);
-  border-color: #FFFFFF;
+  border-color: var(--gemma-white);
   box-shadow: 0 15px 35px rgba(49, 134, 255, 0.45);
   transform: translateY(-3px);
 }
@@ -328,7 +147,7 @@
 }
 
 .gemma-card-arrow {
-  color: #4FA0FF;
+  color: var(--gemma-blue-light);
   font-size: 1.15rem;
   font-weight: 700;
   transition: transform 0.2s ease, color 0.2s ease;
@@ -336,11 +155,11 @@
 
 .gemma-card:hover .gemma-card-arrow {
   transform: translate(3px, -3px);
-  color: #FFFFFF;
+  color: var(--gemma-white);
 }
 
 .gemma-card-title {
-  color: #FFFFFF;
+  color: var(--gemma-white);
   font-size: 1.05rem;
   font-weight: 700;
   margin: 0 0 0.35rem 0;
@@ -364,4 +183,3 @@
   font-size: 0.8rem;
   width: 100%;
 }
-
diff --git a/docs/stylesheets/motion.css b/docs/stylesheets/motion.css
new file mode 100644
index 0000000..9b4d09e
--- /dev/null
+++ b/docs/stylesheets/motion.css
@@ -0,0 +1,79 @@
+/* ===================================================================
+   MOTION
+
+   Owns: transitions and animation, and the reduced-motion opt-out.
+   Kept in one file so "does this site animate?" has a single answer,
+   and so the `prefers-reduced-motion` escape hatch can't be forgotten
+   in a component sheet.
+
+   Docs are read, not played with: motion here is feedback (something
+   is interactive, something changed), never decoration.
+   =================================================================== */
+
+:root {
+  --gemma-ease: cubic-bezier(0.2, 0, 0.2, 1);
+  --gemma-duration-fast: 100ms;
+  --gemma-duration: 160ms;
+}
+
+/* -------------------------------------------------------------------
+   Interactive surfaces
+   ------------------------------------------------------------------- */
+.md-typeset .card,
+.md-typeset .cookbook-card {
+  transition:
+    border-color var(--gemma-duration) var(--gemma-ease),
+    background-color var(--gemma-duration) var(--gemma-ease),
+    transform var(--gemma-duration) var(--gemma-ease);
+}
+
+.md-typeset a.card:hover,
+.md-typeset a.cookbook-card:hover {
+  transform: translateY(-1px);
+}
+
+.md-typeset a.card:active,
+.md-typeset a.cookbook-card:active {
+  transform: translateY(0);
+  transition-duration: var(--gemma-duration-fast);
+}
+
+.md-typeset .badge {
+  transition: border-color var(--gemma-duration) var(--gemma-ease),
+              color var(--gemma-duration) var(--gemma-ease);
+}
+
+/* -------------------------------------------------------------------
+   Focus
+   Always visible, never animated away — keyboard users need it
+   immediately.
+   ------------------------------------------------------------------- */
+.md-typeset a.card:focus-visible,
+.md-typeset a.cookbook-card:focus-visible {
+  outline: 2px solid var(--gemma-accent);
+  outline-offset: 2px;
+  transition: none;
+}
+
+/* -------------------------------------------------------------------
+   Reduced motion
+
+   A blanket opt-out. Anything added later inherits it automatically,
+   which is the point of keeping motion in one file. Durations are near
+   zero rather than 0s so transitionend handlers still fire.
+   ------------------------------------------------------------------- */
+@media (prefers-reduced-motion: reduce) {
+  *,
+  *::before,
+  *::after {
+    animation-duration: 0.01ms !important;
+    animation-iteration-count: 1 !important;
+    transition-duration: 0.01ms !important;
+    scroll-behavior: auto !important;
+  }
+
+  .md-typeset a.card:hover,
+  .md-typeset a.cookbook-card:hover {
+    transform: none;
+  }
+}
diff --git a/docs/stylesheets/tokens.css b/docs/stylesheets/tokens.css
index 52525ed..6de930f 100644
--- a/docs/stylesheets/tokens.css
+++ b/docs/stylesheets/tokens.css
@@ -1,67 +1,8 @@
-/* ==========================================================================
-   Zensical Custom CSS - Gemma 4 Token Highlighting & Color Coding
-   
-   Installation:
-   1. Place this file in your docs directory (e.g., `docs/stylesheets/tokens.css`).
-   2. Reference it in your `zensical.yml` or `mkdocs.yml`:
-      
-      extra_css:
-        - stylesheets/tokens.css
-   ========================================================================== */
-
-:root {
-  /* Turn Control Tokens (Blue) */
-  --token-turn-bg: #dbeafe;
-  --token-turn-color: #1e40af;
-  --token-turn-border: #93c5fd;
-
-  /* Role Tokens: user / assistant / system / model / tool (Red) */
-  --token-role-bg: #fee2e2;
-  --token-role-color: #991b1b;
-  --token-role-border: #fca5a5;
-
-  /* Multimodal Tokens (Purple) */
-  --token-multi-bg: #f3e8ff;
-  --token-multi-color: #6b21a8;
-  --token-multi-border: #d8b4fe;
-
-  /* Tool & Function Tokens (Green) */
-  --token-tool-bg: #d1fae5;
-  --token-tool-color: #065f46;
-  --token-tool-border: #6ee7b7;
-
-  /* Thinking Mode Tokens (Amber) */
-  --token-think-bg: #fef3c7;
-  --token-think-color: #92400e;
-  --token-think-border: #fde68a;
-}
-
-/* Dark Mode Support for Zensical & MkDocs Dark Themes */
-[data-md-color-scheme="slate"],
-.dark,
-@media (prefers-color-scheme: dark) {
-  :root {
-    --token-turn-bg: #1e3a8a;
-    --token-turn-color: #bfdbfe;
-    --token-turn-border: #3b82f6;
-
-    --token-role-bg: #7f1d1d;
-    --token-role-color: #fecaca;
-    --token-role-border: #ef4444;
-
-    --token-multi-bg: #581c87;
-    --token-multi-color: #f3e8ff;
-    --token-multi-border: #a855f7;
-
-    --token-tool-bg: #064e3b;
-    --token-tool-color: #a7f3d0;
-    --token-tool-border: #10b981;
-
-    --token-think-bg: #78350f;
-    --token-think-color: #fef3c7;
-    --token-think-border: #f59e0b;
-  }
-}
+/* ===================================================================
+   CHAT TOKENS — inline badges for <start_of_turn>, roles, tools…
+   Owns: the .token-* badge styling used in prompt-format docs.
+   Colours come from gemma.css (--gemma-cat-*).
+   =================================================================== */
 
 /* ==========================================================================
    High Specificity Badge Styles (Overrides default .md-typeset code)
@@ -97,39 +38,39 @@
 .md-typeset .token-turn,
 .md-typeset code.token-turn,
 .token-turn {
-  background-color: var(--token-turn-bg) !important;
-  color: var(--token-turn-color) !important;
-  border: 1px solid var(--token-turn-border) !important;
+  background-color: var(--gemma-cat-turn-bg) !important;
+  color: var(--gemma-cat-turn-fg) !important;
+  border: 1px solid var(--gemma-cat-turn-border) !important;
 }
 
 .md-typeset .token-role,
 .md-typeset code.token-role,
 .token-role {
-  background-color: var(--token-role-bg) !important;
-  color: var(--token-role-color) !important;
-  border: 1px solid var(--token-role-border) !important;
+  background-color: var(--gemma-cat-role-bg) !important;
+  color: var(--gemma-cat-role-fg) !important;
+  border: 1px solid var(--gemma-cat-role-border) !important;
 }
 
 .md-typeset .token-multi,
 .md-typeset code.token-multi,
 .token-multi {
-  background-color: var(--token-multi-bg) !important;
-  color: var(--token-multi-color) !important;
-  border: 1px solid var(--token-multi-border);
+  background-color: var(--gemma-cat-multi-bg) !important;
+  color: var(--gemma-cat-multi-fg) !important;
+  border: 1px solid var(--gemma-cat-multi-border);
 }
 
 .md-typeset .token-tool,
 .md-typeset code.token-tool,
 .token-tool {
-  background-color: var(--token-tool-bg) !important;
-  color: var(--token-tool-color) !important;
-  border: 1px solid var(--token-tool-border) !important;
+  background-color: var(--gemma-cat-tool-bg) !important;
+  color: var(--gemma-cat-tool-fg) !important;
+  border: 1px solid var(--gemma-cat-tool-border) !important;
 }
 
 .md-typeset .token-think,
 .md-typeset code.token-think,
 .token-think {
-  background-color: var(--token-think-bg) !important;
-  color: var(--token-think-color) !important;
-  border: 1px solid var(--token-think-border) !important;
+  background-color: var(--gemma-cat-think-bg) !important;
+  color: var(--gemma-cat-think-fg) !important;
+  border: 1px solid var(--gemma-cat-think-border) !important;
 }
\ No newline at end of file
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index 908cb6e..b6f426f 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -229,15 +229,27 @@ def iter_code_blocks(page: Page) -> Iterator[tuple[int, str, str]]:
             open_at = None
 
 
+INLINE_CODE = re.compile(r"`+[^`]*`+")
+
+
+def strip_inline_code(line: str) -> str:
+    """Blank out `code spans`, keeping the line length so columns still line up.
+
+    An inline span is code for the same reason a fenced block is: documentation *about* markup
+    quotes it. Without this, `[Video](youtube…)` in the components guide reads as a broken link.
+    """
+    return INLINE_CODE.sub(lambda m: " " * len(m.group(0)), line)
+
+
 def outside_code(page: Page) -> Iterator[tuple[int, str]]:
-    """Yield body lines that are not inside a fenced code block."""
+    """Yield body lines that are not inside a fenced code block, with code spans blanked."""
     in_fence = False
     for lineno, line in page.body_lines():
         if line.lstrip().startswith("```"):
             in_fence = not in_fence
             continue
         if not in_fence:
-            yield lineno, line
+            yield lineno, strip_inline_code(line)
 
 
 # --- Checks -----------------------------------------------------------------------------------
@@ -478,6 +490,55 @@ def check_modeldata(ctx: Context) -> Iterable[Finding]:
                 )
 
 
+STYLESHEET_DIR = DOCS_DIR / "stylesheets"
+TOKEN_STYLESHEET = "gemma.css"
+MAX_STYLESHEET_LINES = 250
+HEX_COLOUR = re.compile(r"#[0-9a-fA-F]{3,8}\b")
+
+
+def check_css(ctx: Context) -> Iterable[Finding]:
+    """Design-system rules from PLAN.md Phase 1.
+
+    Raw colours live in exactly one file, every sheet is registered, and none grows past the
+    point where an agent can no longer read it end-to-end before editing.
+    """
+    if not STYLESHEET_DIR.exists():
+        return
+
+    registered = {
+        Path(entry).name
+        for entry in ctx.config.get("project", {}).get("extra_css", [])
+    }
+
+    for path in sorted(STYLESHEET_DIR.glob("*.css")):
+        rel = f"docs/stylesheets/{path.name}"
+        text = path.read_text(encoding="utf-8")
+
+        if path.name not in registered:
+            yield Finding("css", rel, 0, "stylesheet is not listed in extra_css")
+
+        lines = text.splitlines()
+        if len(lines) > MAX_STYLESHEET_LINES:
+            yield Finding(
+                "css", rel, 0,
+                f"{len(lines)} lines exceeds {MAX_STYLESHEET_LINES} -- split by concern",
+            )
+
+        if path.name == TOKEN_STYLESHEET:
+            continue
+        for number, line in enumerate(lines, start=1):
+            if HEX_COLOUR.search(line):
+                yield Finding(
+                    "css", rel, number,
+                    f"raw colour {HEX_COLOUR.search(line).group(0)} -- "
+                    f"add a token to {TOKEN_STYLESHEET} and reference it",
+                )
+
+    for name in sorted(registered):
+        if not (STYLESHEET_DIR / name).exists():
+            yield Finding("css", "zensical.toml", 0, f"extra_css lists a missing file: {name}")
+
+
 CHECKS: dict[str, Callable[[Context], Iterable[Finding]]] = {
     "links": check_links,
     "images": check_images,
@@ -489,6 +550,7 @@ def check_modeldata(ctx: Context) -> Iterable[Finding]:
     "admonitions": check_admonitions,
     "size": check_size,
     "modeldata": check_modeldata,
+    "css": check_css,
 }
 
 CHECK_HELP = {
@@ -502,6 +564,7 @@ def check_modeldata(ctx: Context) -> Iterable[Finding]:
     "admonitions": "unconverted devsite Note:/Caution: callouts",
     "size": "files large enough to indicate inlined assets",
     "modeldata": "data/models.yml vs the ported memory table",
+    "css": "design-system rules: tokens, registration, file size",
 }
 
 
diff --git a/zensical.toml b/zensical.toml
index a214510..e0e2ec2 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -12,7 +12,18 @@ site_url = "https://gemma.ai"
 copyright = """
 Copyright &copy; 2026 Google DeepMind
 """
-extra_css = ["stylesheets/home.css", "stylesheets/layout.css", "stylesheets/components.css", "stylesheets/cookbook.css", "stylesheets/code.css", "stylesheets/tokens.css"]
+# Order matters: the token layer must load first, motion last.
+extra_css = [
+  "stylesheets/gemma.css",       # tokens: palette, spacing, radius, shadow
+  "stylesheets/layout.css",      # page grid, sidebars, typography scale
+  "stylesheets/components.css",  # cards, badges, figures, steps, embeds
+  "stylesheets/code.css",        # code and output blocks
+  "stylesheets/tokens.css",      # chat-token badges
+  "stylesheets/home-chrome.css", # landing page: header, tabs, search
+  "stylesheets/home-stage.css",  # landing page: backdrop gradients
+  "stylesheets/home.css",        # landing page: hero and feature grid
+  "stylesheets/motion.css",      # transitions + reduced-motion opt-out
+]
 # `include_yaml` exposes data/models.yml as `models.*` in page templates -- see data/models.yml
 # for what reads it. Hand-editing a model table in a page is a bug; change the data file.
 plugins = { macros = { enabled = true, include_yaml = { models = "data/models.yml" } } }
@@ -156,6 +167,10 @@ nav = [
     "tools/vram-calculator.md",
     "tools/model-selector.md",
   ]},
+
+  { "Contributing" = [
+    "contributing/components.md",
+  ]},
 ]
 
 # ============================================================================

From ccdf9a7f8c91802e105117c0dcb2a9d06705cd43 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Tue, 4 Aug 2026 11:05:01 +0200
Subject: [PATCH 10/20] Start adding pages for different engines

---
 .lintbaseline.json                            |  46 +-
 PLAN.md                                       | 164 +++++-
 .../deployment/hf-to-mediapipe-task-flow.png  | Bin 0 -> 28500 bytes
 .../hf-to-mediapipe-task-overview.png         | Bin 0 -> 80744 bytes
 .../deployment/output_UnIMXwl2evBe_1.png      | Bin 0 -> 309109 bytes
 docs/assets/deployment/thali-indian-plate.jpg | Bin 0 -> 126290 bytes
 ...lery-mobile-actions-finetune-challenge.png | Bin 0 -> 200487 bytes
 docs/assets/tuning/gallery-mobile-actions.png | Bin 0 -> 136153 bytes
 docs/assets/tuning/output_vPN-DTopaUIy_0.png  | Bin 0 -> 28629 bytes
 docs/deployment/google-cloud.md               |  67 ++-
 docs/deployment/huggingface.md                |  15 -
 docs/deployment/keras.md                      | 349 +++++++++++++
 docs/deployment/langchain.md                  | 316 +++++++++++-
 docs/deployment/litert.md                     | 215 +++++++-
 docs/deployment/llamacpp.md                   | 107 +++-
 docs/deployment/lmstudio.md                   |  65 ++-
 docs/deployment/mlx.md                        |  70 ++-
 docs/deployment/mobile.md                     |  41 +-
 docs/deployment/ollama.md                     | 108 +++-
 docs/deployment/qat.md                        |  69 ++-
 docs/deployment/sglang.md                     |  53 +-
 docs/deployment/vllm.md                       |  63 ++-
 docs/deployment/web.md                        |  18 +-
 docs/get-started/capabilities/vision.md       |  91 +++-
 docs/get-started/index.md                     |  57 ---
 docs/models/functiongemma/fine-tuning.md      |   2 +-
 docs/models/functiongemma/index.md            |   2 +-
 docs/tuning/distributed.md                    | 306 +++++++++++-
 docs/tuning/full-finetune.md                  | 411 ++++++++++++++-
 docs/tuning/keras-lora.md                     | 327 +++++++++++-
 docs/{deployment => tuning}/mobile-actions.md |   2 +-
 docs/tuning/overview.md                       | 124 ++++-
 docs/tuning/qlora.md                          | 466 ++++++++++++++++-
 docs/tuning/vision-qlora.md                   | 467 +++++++++++++++++-
 scripts/convert_notebook.py                   |  22 +
 scripts/link_map.toml                         |  20 +-
 scripts/lint_docs.py                          |  11 +-
 scripts/rewrite_links.py                      |  17 +-
 scripts/sources.toml                          |  61 ++-
 zensical.toml                                 | 181 ++++---
 40 files changed, 4079 insertions(+), 254 deletions(-)
 create mode 100644 docs/assets/deployment/hf-to-mediapipe-task-flow.png
 create mode 100644 docs/assets/deployment/hf-to-mediapipe-task-overview.png
 create mode 100644 docs/assets/deployment/output_UnIMXwl2evBe_1.png
 create mode 100644 docs/assets/deployment/thali-indian-plate.jpg
 create mode 100644 docs/assets/tuning/gallery-mobile-actions-finetune-challenge.png
 create mode 100644 docs/assets/tuning/gallery-mobile-actions.png
 create mode 100644 docs/assets/tuning/output_vPN-DTopaUIy_0.png
 create mode 100644 docs/deployment/keras.md
 delete mode 100644 docs/get-started/index.md
 rename docs/{deployment => tuning}/mobile-actions.md (95%)

diff --git a/.lintbaseline.json b/.lintbaseline.json
index 5f670d8..e827330 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -13,7 +13,7 @@
     "modeldata",
     "css"
   ],
-  "count": 136,
+  "count": 95,
   "known": [
     "frontmatter|docs/community/discord.md|no frontmatter block",
     "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
@@ -23,17 +23,10 @@
     "frontmatter|docs/deployment/google-cloud.md|no frontmatter block",
     "frontmatter|docs/deployment/huggingface.md|no frontmatter block",
     "frontmatter|docs/deployment/index.md|no frontmatter block",
+    "frontmatter|docs/deployment/keras.md|no frontmatter block",
     "frontmatter|docs/deployment/langchain.md|no frontmatter block",
     "frontmatter|docs/deployment/litert.md|no frontmatter block",
-    "frontmatter|docs/deployment/llamacpp.md|no frontmatter block",
-    "frontmatter|docs/deployment/lmstudio.md|no frontmatter block",
-    "frontmatter|docs/deployment/mlx.md|no frontmatter block",
-    "frontmatter|docs/deployment/mobile-actions.md|no frontmatter block",
     "frontmatter|docs/deployment/mobile.md|no frontmatter block",
-    "frontmatter|docs/deployment/ollama.md|no frontmatter block",
-    "frontmatter|docs/deployment/qat.md|no frontmatter block",
-    "frontmatter|docs/deployment/sglang.md|no frontmatter block",
-    "frontmatter|docs/deployment/vllm.md|no frontmatter block",
     "frontmatter|docs/deployment/web.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/audio.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/function-calling.md|no frontmatter block",
@@ -43,7 +36,6 @@
     "frontmatter|docs/get-started/capabilities/thinking.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/video.md|no frontmatter block",
     "frontmatter|docs/get-started/capabilities/vision.md|no frontmatter block",
-    "frontmatter|docs/get-started/index.md|no frontmatter block",
     "frontmatter|docs/get-started/prompt-formatting.md|missing required key: description",
     "frontmatter|docs/get-started/prompt-formatting.md|missing required key: title",
     "frontmatter|docs/get-started/prompt-structure.md|no frontmatter block",
@@ -97,6 +89,7 @@
     "frontmatter|docs/tuning/distributed.md|no frontmatter block",
     "frontmatter|docs/tuning/full-finetune.md|no frontmatter block",
     "frontmatter|docs/tuning/keras-lora.md|no frontmatter block",
+    "frontmatter|docs/tuning/mobile-actions.md|no frontmatter block",
     "frontmatter|docs/tuning/overview.md|no frontmatter block",
     "frontmatter|docs/tuning/qlora.md|no frontmatter block",
     "frontmatter|docs/tuning/spoken-language.md|no frontmatter block",
@@ -105,47 +98,14 @@
     "frontmatter|docs/tutorials/visual-guides/gemma-4-12b.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/gemma-4.md|no frontmatter block",
     "frontmatter|docs/tutorials/visual-guides/mtp.md|no frontmatter block",
-    "internal|docs/deployment/index.md|broken internal link: keras.md",
-    "orphans|docs/deployment/index.md|page exists but is not in nav",
-    "orphans|docs/deployment/mobile-actions.md|page exists but is not in nav",
-    "orphans|docs/get-started/capabilities/mtp.md|page exists but is not in nav",
-    "orphans|docs/get-started/capabilities/vision.md|page exists but is not in nav",
-    "orphans|docs/get-started/index.md|page exists but is not in nav",
-    "orphans|docs/get-started/prompt-structure.md|page exists but is not in nav",
-    "orphans|docs/get-started/setup.md|page exists but is not in nav",
-    "orphans|docs/learn/business-email-assistant.md|page exists but is not in nav",
-    "orphans|docs/learn/personal-code-assistant.md|page exists but is not in nav",
-    "orphans|docs/models/gemma-aps/index.md|page exists but is not in nav",
-    "orphans|docs/models/index.md|page exists but is not in nav",
-    "orphans|docs/tools/index.md|page exists but is not in nav",
-    "orphans|docs/tuning/spoken-language.md|page exists but is not in nav",
     "stubs|docs/community/discord.md|stub: 46 chars of content (minimum 400)",
     "stubs|docs/community/gemma-skills.md|stub: 62 chars of content (minimum 400)",
     "stubs|docs/community/gemmaverse.md|placeholder link: example.com",
     "stubs|docs/cookbook.md|stub: 395 chars of content (minimum 400)",
     "stubs|docs/demos/concurrent.md|stub: 52 chars of content (minimum 400)",
-    "stubs|docs/deployment/google-cloud.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/deployment/langchain.md|stub: 59 chars of content (minimum 400)",
-    "stubs|docs/deployment/litert.md|stub: 52 chars of content (minimum 400)",
-    "stubs|docs/deployment/llamacpp.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/deployment/lmstudio.md|stub: 33 chars of content (minimum 400)",
-    "stubs|docs/deployment/mlx.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/deployment/mobile.md|stub: 40 chars of content (minimum 400)",
-    "stubs|docs/deployment/ollama.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/deployment/qat.md|stub: 69 chars of content (minimum 400)",
-    "stubs|docs/deployment/sglang.md|stub: 50 chars of content (minimum 400)",
-    "stubs|docs/deployment/vllm.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/deployment/web.md|stub: 39 chars of content (minimum 400)",
-    "stubs|docs/get-started/capabilities/vision.md|placeholder text: 'Content coming soon'",
     "stubs|docs/index.md|stub: 0 chars of content (minimum 400)",
     "stubs|docs/tools/index.md|stub: 144 chars of content (minimum 400)",
     "stubs|docs/tools/model-selector.md|stub: 58 chars of content (minimum 400)",
-    "stubs|docs/tuning/distributed.md|stub: 45 chars of content (minimum 400)",
-    "stubs|docs/tuning/full-finetune.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/tuning/keras-lora.md|stub: 36 chars of content (minimum 400)",
-    "stubs|docs/tuning/overview.md|stub: 54 chars of content (minimum 400)",
-    "stubs|docs/tuning/qlora.md|placeholder text: 'Content coming soon'",
-    "stubs|docs/tuning/vision-qlora.md|stub: 45 chars of content (minimum 400)",
     "stubs|docs/tutorials/visual-guides/diffusiongemma.md|stub: 50 chars of content (minimum 400)",
     "stubs|docs/tutorials/visual-guides/gemma-4-12b.md|stub: 54 chars of content (minimum 400)",
     "stubs|docs/tutorials/visual-guides/gemma-4.md|stub: 49 chars of content (minimum 400)",
diff --git a/PLAN.md b/PLAN.md
index 23b3eb7..4b698df 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -683,7 +683,47 @@ Target layout (`docs/stylesheets/`):
 
 ## Phase 2 — Navigation & content completion
 
-### Step 2.1 — Restructure nav
+### Step 2.1 — Restructure nav ✅
+
+- [x] Nav rewritten to the 8-tab target: **Home · Get started · Models · Run · Fine-tune ·
+      Learn · Demos · Ecosystem**.
+- [x] **All 13 orphans resolved.** `orphans` 13 → **0**.
+- [x] Cookbook folded into *Learn*; the old Tools tab folded into *Demos* as
+      "Interactive tools"; `contributing/components.md` placed under *Ecosystem → Contribute*.
+- **Verify:** `uv run python scripts/lint_docs.py --check orphans` → zero.
+- **Done when:** ✅ zero orphans, zero broken nav entries, 8 top-level tabs confirmed in the
+      rendered header.
+
+How each orphan was resolved:
+
+| Orphan | Resolution |
+|---|---|
+| `get-started/index.md` | **Deleted** — a strict subset of `quickstart.md` with zero inbound links |
+| `capabilities/vision.md` | **Ported** from upstream (was a stub); now the parent of Image/Video |
+| `deployment/keras.md` | **Ported** — clears the last 2 `internal` findings and fills *Run → Local* |
+| `models/index.md`, `tools/index.md` | placed as section landing pages |
+| the 9 pages from Step 0.5b | placed in Run / Fine-tune / Learn / Models |
+
+**Directory question settled: `models/` stays flat.** Grouping is nav-only, exactly so a
+model's URL survives reclassification — when Gemma 5 ships and Gemma 4 moves to *Previous
+generations*, no URL changes. The rationale is recorded in the nav file itself.
+
+**One more converter fix:** consecutive `{ .no-copy .output }` blocks separated only by blank
+lines are now merged. Upstream splits a single stream of terminal output across several fences
+(Keras `.summary()` prints a title then a table), which rendered as two separate result boxes.
+Only *output* blocks are merged — two adjacent code blocks may legitimately be separate cells.
+
+### Phase 2 progress: 136 → 120
+
+| Check | Now | |
+|---|---|---|
+| `links` `images` `internal` `fences` `admonitions` `size` `modeldata` `css` `orphans` | **0** | ✅ all clear |
+| `stubs` | 30 | → Steps 2.2–2.4 |
+| `frontmatter` | 90 | → Step 3.1 |
+
+**Only two checks still have findings**, and both are scheduled work. Build warnings: 5 → **3**.
+
+### Step 2.1 — Restructure nav (original spec)
 - [ ] Rewrite the `nav` block in `zensical.toml` to the target structure above.
 - [ ] Resolve the 4 orphans: fold `get-started/index.md` into `quickstart.md`,
       make `capabilities/vision.md` a real parent page, add `models/index.md` and
@@ -696,12 +736,39 @@ Target layout (`docs/stylesheets/`):
 These are *written*, not ported — upstream's versions are three paragraphs each. One step per
 engine so they can be tested and handed over independently.
 
-Template each page must follow: one-line install → one-line run → model tags for every
-size × quant → server mode with OpenAI-compatible endpoint → multimodal examples (gated by
-actual model support) → context/KV-cache flags → troubleshooting → measured tok/s on named hardware.
-
-- [ ] 2.2a Ollama
-- [ ] 2.2b llama.cpp
+**These are quick references, not manuals** (decided 2026-08-03). Users want working code
+fast, and a short page is a page that stays correct. Roughly 100 lines each.
+
+Template — in this order, nothing else:
+
+1. **Install** — the one command per platform
+2. **Run** — the one command that works
+3. **Model tags / repositories** — a table, every Gemma 4 size
+4. **Multimodal** — only where it differs from plain text, with a note when a size lacks a modality
+5. **Server** — the OpenAI-compatible snippet
+
+Deliberately **not** included: context-length deep dives, troubleshooting tables, importing
+your own fine-tunes, "next steps" link lists. Each is either engine documentation that we would
+have to keep in sync, or a link the sidebar already provides. A single `!!! tip` is allowed
+where a default will actively surprise someone (Ollama's 4K context, for instance).
+
+- [x] 2.2a Ollama ✅ *(written)*
+- [x] 2.2b llama.cpp ✅ *(written)*
+- [x] 2.2c LM Studio ✅ *(written)*
+- [x] 2.2d MLX ✅ *(written)*
+- [x] 2.2e Transformers ✅ *(ported — upstream `core/huggingface_inference` is already complete)*
+- [x] 2.2f vLLM ✅ *(written)*
+- [x] 2.2g SGLang ✅ *(written)*
+- [x] 2.2h LiteRT / mobile / WebGPU ✅ *(ported — upstream pages are already concise link hubs)*
+- [x] 2.2i Google Cloud ✅ *(ported)*
+- [x] Also filled: Keras, LangChain, Quantization & QAT. **The Run section has zero stubs.**
+
+**Nav labels are short, page titles are not** (2026-08-03). The sidebar shows "Hugging Face
+Transformers", "LangChain", "Keras"; the page H1s stay as ported ("Run Gemma with Hugging Face
+Transformers"). Labels live in `zensical.toml`, so no ported prose is touched.
+
+**`mobile-actions.md` moved `deployment/` → `tuning/`.** Its H1 is "Fine-tune FunctionGemma 270M
+for Mobile Actions" — a fine-tuning guide that was sitting under Run.
 - [ ] 2.2c LM Studio
 - [ ] 2.2d MLX
 - [ ] 2.2e Transformers
@@ -710,9 +777,59 @@ actual model support) → context/KV-cache flags → troubleshooting → measure
 - [ ] 2.2h LiteRT / mobile / WebGPU
 - [ ] 2.2i Google Cloud (GKE, Cloud Run)
 - **Verify per page:** every command runs as written on a clean machine.
+
+**Sourcing rule (2026-08-03).** Every command, flag and model identifier on these pages is
+taken from a primary source — the engine's own repository docs, its published model registry,
+or the upstream Gemma docs. Nothing is written from memory. Where a claim can't be verified it
+is omitted rather than guessed.
+
+Verified sources used so far:
+
+| Page | Sources |
+|---|---|
+| Ollama | `ollama.com/library/gemma4/tags` (tags, sizes, context) · `ollama/ollama` `docs/api`, `docs/api/openai-compatibility.mdx`, `docs/capabilities/vision.mdx`, `docs/context-length.mdx` · upstream `/integrations/ollama` |
+| llama.cpp | `ggml-org/llama.cpp` `docs/install.md`, `docs/multimodal.md` · `huggingface.co/ggml-org` (5 GGUF repos) · `huggingface.co/google` (5 QAT GGUF repos) · upstream `/integrations/llamacpp` |
+
+**Throughput numbers are deliberately absent.** Measured tok/s can't be sourced from
+documentation, and inventing it would be worse than omitting it. The pages give the verifiable
+performance levers instead — GPU-offload checks, context defaults, quantization choice. Add real
+figures when someone can measure them.
+
+**Unresolved discrepancy — Ollama download sizes.** `ollama.com/library/gemma4/tags` states
+E2B 7.2 GB, E4B 9.6 GB, **12B 7.6 GB**, 26B 18 GB, 31B 20 GB. That makes E4B *larger* than 12B,
+which contradicts the validated Q4_0 column in `models/gemma-4/index.md` (E4B 4.5 GB,
+12B 6.7 GB — monotonic, as expected). Verified verbatim from the tags page twice.
+
+Most likely the default tag for each size uses a different quantization, but that is a guess and
+**no explanation appears on the page**. An earlier draft rationalised it as a Per-Layer
+Embeddings effect; that was wrong and was removed. If someone can confirm the per-tag quant
+levels, add them to the table — otherwise leave the figures unexplained.
+
+Two corrections to upstream found while writing:
+
+- Upstream's Ollama page lists "four sizes: E2B, E4B, 26B and 31B" and **omits 12B**, which the
+  Ollama library does publish.
+- **Ollama exposes Gemma 4 as text + image only** — no audio — while llama.cpp supports image,
+  audio *and* video. Neither page upstream mentions this, and it's the kind of thing that costs
+  someone an afternoon.
+
+
 - **Done when:** the page is genuinely more useful than the upstream equivalent.
 
-### Step 2.3 — Fill remaining stubs
+### Step 2.3 — Fill remaining stubs (in progress)
+
+- [x] **Fine-tune section ported** — all six stubs filled from upstream: `tune`,
+      `huggingface_text_finetune_qlora`, `huggingface_text_full_finetune`,
+      `huggingface_vision_finetune_qlora`, `lora_tuning`, `distributed_tuning`.
+      These are validated tutorials, so porting beats authoring.
+
+**`slugify` bug fixed.** `rewrite_links.py` stripped parenthesised text, so
+"Google Kubernetes Engine (GKE)" produced `google-kubernetes-engine` while Python-Markdown
+emits `google-kubernetes-engine-gke`. Anchors computed from it silently never matched. It now
+mirrors the real algorithm: reduce markdown to visible text, then drop non-word characters —
+parentheses contribute their contents, they are not removed.
+
+### Step 2.3 — Fill remaining stubs (original spec)
 - [ ] Fine-tune pages (QLoRA, full FT, vision QLoRA, Keras LoRA, distributed).
 - [ ] Quantization & QAT page — move the QAT content currently buried in
       `models/gemma-4/index.md` into its own page under Run.
@@ -734,6 +851,37 @@ actual model support) → context/KV-cache flags → troubleshooting → measure
 
 ---
 
+## Page context header (decided 2026-08-03, build after Step 3.1)
+
+A Stripe-style bar at the top of **every page** — see <https://docs.stripe.com/billing/scripts>.
+Not silence-by-default: the slot is always present, so readers learn where to look.
+
+It carries three things, in this order of arrival:
+
+1. **Applicability** — which model(s) the page is about. Three states, resolved from frontmatter
+   against `data/models.yml`:
+
+    | State | Frontmatter | Shows |
+    |---|---|---|
+    | Universal | *(omitted)* | "All Gemma models" |
+    | Version-scoped | `models: gemma-3` | `Gemma 3` |
+    | Capability-scoped | `models: [e2b, e4b, 12b]` | `E2B · E4B · 12B` |
+
+2. **LLM tools** (Step 3.4) — Copy as Markdown, Open in Claude/ChatGPT, View raw.
+3. **Model + engine selector** (Step 4.2) — page-scoped context belongs here, not in site chrome.
+
+All three read `data/models.yml`, so the header, the tables, the VRAM calculator and the
+switcher cannot drift apart.
+
+**Open interaction question.** With 31B selected, on the audio page (E2B/E4B/12B only):
+passive (badges show 31B struck through, via `.badge--muted`), active (an inline note naming
+the substitution), or silent swap. Leaning passive, escalating to active only where the code
+samples would otherwise be wrong. Silent swap is rejected — the reader ends up with code for a
+model they did not choose.
+
+**Sequencing:** Step 3.1 (frontmatter schema, `models:` key) → static header with applicability
+→ 3.4 tools into the same bar → 4.2 selector into the same bar.
+
 ## Phase 3 — Agent-first infrastructure
 
 *The headline requirement from the original plan, currently ~11% built.*
diff --git a/docs/assets/deployment/hf-to-mediapipe-task-flow.png b/docs/assets/deployment/hf-to-mediapipe-task-flow.png
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diff --git a/docs/assets/deployment/output_UnIMXwl2evBe_1.png b/docs/assets/deployment/output_UnIMXwl2evBe_1.png
new file mode 100644
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diff --git a/docs/deployment/google-cloud.md b/docs/deployment/google-cloud.md
index c19cf57..d7a0601 100644
--- a/docs/deployment/google-cloud.md
+++ b/docs/deployment/google-cloud.md
@@ -1,5 +1,66 @@
-# Google Cloud
+The Google Cloud platform provides many options for deploying, serving,
+and fine-tuning Gemma 4 open models, including the following:
 
-Deploy Gemma on Google Cloud using Vertex AI, GKE, or Cloud Run.
+- [Gemini Enterprise Agent Platform](google-cloud.md#gemini-enterprise-agent-platform)
+- [Cloud Run](google-cloud.md#cloud-run)
+- [Google Kubernetes Engine (GKE)](google-cloud.md#google-kubernetes-engine-gke)
+- [Agent Development Kit (ADK)](google-cloud.md#agent-development-kit-adk)
+- [Gemini Enterprise Agent Platform Training Clusters](google-cloud.md#gemini-enterprise-agent-platform-training-clusters)
+- [MaxText](google-cloud.md#maxtext)
+- [vLLM with TPUs](google-cloud.md#vllm-with-tpus)
+- [Sovereign Cloud](google-cloud.md#sovereign-cloud)
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+## Gemini Enterprise Agent Platform
+
+[Gemini Enterprise Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/open-models/use-gemma) is a Google Cloud platform for rapidly
+building and scaling machine learning projects. Gemma 4 is available in [Model
+Garden](https://console.cloud.google.com/agent-platform/publishers/google/model-garden/gemma4), a curated collection of models on Gemini Enterprise Agent Platform.
+You can test and deploy models directly from the console.
+
+To learn more, refer to the following pages:
+
+- [Agent Platform overview](https://docs.cloud.google.com/gemini-enterprise-agent-platform/overview): Get started with Gemini Enterprise Agent Platform.
+- [Gemma with Gemini Enterprise Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/open-models/use-gemma): Use Gemma open models with Gemini Enterprise Agent Platform.
+
+## Cloud Run
+
+[Cloud Run](https://cloud.google.com/run) is a fully managed platform to run your code or containers on
+top of Google's highly scalable infrastructure.
+[Deploy Gemma 4 on Cloud Run](https://docs.cloud.google.com/run/docs/run-gemma-on-cloud-run) using GPUs for scale-to-zero, pay-per-use
+inference.
+
+For larger mode sizes, [leverage advanced configurations](https://codelabs.developers.google.com/codelabs/cloud-run/cloud-run-gpu-rtx-pro-6000-gemma4-vllm) with RTX 6000 Pro
+GPUs and Model Streaming.
+
+## Google Kubernetes Engine (GKE)
+
+[Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine) (GKE) is a managed Kubernetes service from Google
+Cloud. [Run Gemma 4 on GKE](https://cloud.google.com/kubernetes-engine/docs/tutorials/serve-gemma-gpu-vllm) for enterprise-grade container orchestration.
+Use TPUs and GPUs to serve models with high throughput and low latency.
+
+## Agent Development Kit (ADK)
+
+Build and orchestrate AI agents with Gemma 4 and the [Agent Development Kit
+(ADK)](https://adk.dev/agents/models/google-gemma/). Gemma 4's strong reasoning and function-calling capabilities make it
+ideal for agentic workflows.
+
+## Gemini Enterprise Agent Platform Training Clusters
+
+[Fine-tune Gemma 4 using Gemini Enterprise Agent Platform Training
+Clusters](https://discuss.google.dev/t/end-to-end-guide-fine-tuning-and-serving-gemma-4-on-vertex-ai/345865). Training Clusters provides optimized infrastructure for
+large-scale training and fine-tuning of open models.
+
+## vLLM with TPUs
+
+[Serve Gemma 4 on Google Cloud TPUs](https://docs.vllm.ai/projects/recipes/en/latest/Google/Gemma4.html#cloud-tpu-deployment-via-docker) for state-of-the-art serving
+performance.
+
+## MaxText
+
+Gemma 4 is supported in [MaxText](https://github.com/google/maxtext), a high-performance, arbitrary-sized JAX
+LLM implementation for Google Cloud TPUs.
+
+## Sovereign Cloud
+
+Gemma 4 is available on [Sovereign Cloud](https://cloud.google.com/sovereign-cloud) solutions, providing enhanced
+control and compliance for sensitive workloads.
diff --git a/docs/deployment/huggingface.md b/docs/deployment/huggingface.md
index ee22163..c610f5e 100644
--- a/docs/deployment/huggingface.md
+++ b/docs/deployment/huggingface.md
@@ -38,9 +38,6 @@ pipe = pipeline(
     device_map="auto",
     dtype="auto"
 )
-```
-
-``` { .no-copy .output }
 config.json:   0%|          | 0.00/4.95k [00:00<?, ?B/s]
 model.safetensors:   0%|          | 0.00/10.2G [00:00<?, ?B/s]
 Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
@@ -59,9 +56,6 @@ Once you have the Gemma model loaded and configured in a `pipeline` object, you
 
 ```python
 pipe(text="<|turn>user\nroses are red<turn|>\n<|turn>model\n")
-```
-
-``` { .no-copy .output }
 [transformers] The input data was not formatted as a chat with dicts containing 'role' and 'content' keys, even though this model supports chat. Consider using the chat format for better results. For more information, see https://huggingface.co/docs/transformers/en/chat_templating
 [transformers] Keyword argument `video` is not a valid argument for this processor and will be ignored.
 [transformers] Both `max_new_tokens` (=256) and `max_length`(=20) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)
@@ -91,9 +85,6 @@ messages = [
 ]
 
 pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)
-```
-
-``` { .no-copy .output }
 [{'input_text': [{'role': 'system',
     'content': [{'type': 'text', 'text': 'You are a helpful assistant.'}]},
    {'role': 'user',
@@ -132,9 +123,6 @@ messages = [
 ]
 
 pipe(text=messages, return_full_text=False, generate_kwargs=gen_kwargs)
-```
-
-``` { .no-copy .output }
 [{'input_text': [{'role': 'user',
     'content': [{'type': 'image',
       'url': 'https://ai.google.dev/static/gemma/docs/images/thali-indian-plate.jpg'},
@@ -174,9 +162,6 @@ messages = [
 ]
 
 pipe(text=messages, return_full_text=False, generate_kwargs=gen_kwargs)
-```
-
-``` { .no-copy .output }
 [{'input_text': [{'role': 'user',
     'content': [{'type': 'text',
       'text': 'Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.'},
diff --git a/docs/deployment/keras.md b/docs/deployment/keras.md
new file mode 100644
index 0000000..70ecafd
--- /dev/null
+++ b/docs/deployment/keras.md
@@ -0,0 +1,349 @@
+# Run Gemma with Keras
+
+
+Generating content, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models. This tutorial shows you how to get started running Gemma using Keras, including generating text content with text and image input. [Keras](https://keras.io/) provides implementations for running Gemma and other models using JAX, PyTorch, and TensorFlow. If you're new to Keras, you might want to read [Getting started with Keras](https://keras.io/getting_started/) before you begin.
+
+Gemma 3 and later models support text and image input. Earlier versions of Gemma only support text input, except for some variants, including [PaliGemma](../get-started/setup.md).
+!!! Note
+    This notebook won't run on T4 if you try with Gemma 4. Use L4 or higher GPUs.
+    
+
+### Install Keras packages
+
+Install the Keras and KerasHub Python packages.
+
+```bash
+pip install -q -U keras keras-hub keras-nlp
+```
+
+### Select a backend
+
+Keras is a high-level, multi-framework deep learning API designed for simplicity and ease of use. [Keras 3](https://keras.io/keras_3) lets you choose the backend: TensorFlow, JAX, or PyTorch. All three will work for this tutorial. For this tutorial, configure the backend for JAX as it typically provides the better performance.
+
+```python
+import os
+
+os.environ["KERAS_BACKEND"] = "jax"  # Or "tensorflow" or "torch".
+os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "1.00"
+```
+
+### Import packages
+
+Import the Keras and KerasHub packages.
+
+```python
+import keras
+import keras_hub
+```
+
+## Load model
+
+Keras provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/). Download and configure a Gemma model using the `Gemma4CausalLM` class to build an end-to-end, causal language modeling implementation for Gemma 4 models. Create the model using the `from_preset()` method, as shown in the following code example:
+
+```python
+gemma_lm = keras_hub.models.Gemma4CausalLM.from_preset(
+    "gemma4_instruct_2b",
+    dtype="bfloat16",
+)
+```
+
+The `Gemma4CausalLM.from_preset()` method instantiates the model from a preset architecture and weights. In the code above, the string `"gemma#_xxxxxxx"` specifies a preset version and parameter size for Gemma. You can find the code strings for Gemma models in their **Model Variation** listings on [Kaggle](https://www.kaggle.com/models/keras/gemma4).
+
+Once you have the model downloaded, Use the `summary()` function to get more info about the model:
+
+```python
+gemma_lm.summary()
+Preprocessor: "gemma4_causal_lm_preprocessor"
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Layer (type)                                                  ┃                                   Config ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ gemma4_tokenizer_1 (Gemma4Tokenizer)                          │                      Vocab size: 262,144 │
+├───────────────────────────────────────────────────────────────┼──────────────────────────────────────────┤
+│ gemma4_image_converter (Gemma4ImageConverter)                 │                       Image size: (1, 1) │
+├───────────────────────────────────────────────────────────────┼──────────────────────────────────────────┤
+│ gemma4_audio_converter (Gemma4AudioConverter)                 │                Audio shape: (3,000, 128) │
+├───────────────────────────────────────────────────────────────┼──────────────────────────────────────────┤
+│ gemma4_video_converter (Gemma4ImageConverter)                 │                       Image size: (1, 1) │
+├───────────────────────────────────────────────────────────────┼──────────────────────────────────────────┤
+│ gemma4_video_converter (ImageConverter)                       │                 Image size: (None, None) │
+└───────────────────────────────────────────────────────────────┴──────────────────────────────────────────┘
+Model: "gemma4_causal_lm_1"
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ audio_indices (InputLayer)    │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ audio_mask (InputLayer)       │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ audio_mel (InputLayer)        │ (None, None, None, 128)   │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ audio_mel_mask (InputLayer)   │ (None, None, None)        │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ pixel_position_ids            │ (None, None, None, 2)     │               0 │ -                          │
+│ (InputLayer)                  │                           │                 │                            │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ pixel_values (InputLayer)     │ (None, None, None, None)  │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ position_ids (InputLayer)     │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ vision_indices (InputLayer)   │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ vision_mask (InputLayer)      │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ gemma4_backbone_1             │ (None, None, 1536)        │   5,104,298,467 │ audio_indices[0][0],       │
+│ (Gemma4Backbone)              │                           │                 │ audio_mask[0][0],          │
+│                               │                           │                 │ audio_mel[0][0],           │
+│                               │                           │                 │ audio_mel_mask[0][0],      │
+│                               │                           │                 │ padding_mask[0][0],        │
+│                               │                           │                 │ pixel_position_ids[0][0],  │
+│                               │                           │                 │ pixel_values[0][0],        │
+│                               │                           │                 │ position_ids[0][0],        │
+│                               │                           │                 │ token_ids[0][0],           │
+│                               │                           │                 │ vision_indices[0][0],      │
+│                               │                           │                 │ vision_mask[0][0]          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ token_embedding               │ (None, None, 262144)      │     402,653,184 │ gemma4_backbone_1[0][0]    │
+│ (ReversibleEmbedding)         │                           │                 │                            │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ true_divide (TrueDivide)      │ (None, None, 262144)      │               0 │ token_embedding[1][0]      │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ tanh (Tanh)                   │ (None, None, 262144)      │               0 │ true_divide[0][0]          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ multiply_40 (Multiply)        │ (None, None, 262144)      │               0 │ tanh[0][0]                 │
+└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘
+Total params: 5,104,298,467 (10.39 GB)
+Trainable params: 5,104,186,912 (10.39 GB)
+Non-trainable params: 111,555 (435.69 KB)
+```
+
+The output of the summary shows the models total number of trainable parameters.
+For purposes of naming the model, the embedding layer is not counted against the number of parameters.
+
+!!! Note
+    To run larger Gemma models with Google Colab, you need access to the premium GPUs available in paid plans. Alternatively, you can perform inferences using [Kaggle](https://www.kaggle.com/code) notebooks or Google Cloud projects.
+
+
+## Generate text with text
+
+Generate text with a text prompt with using `generate()` method of the Gemma model object you configured in the previous steps. The optional `max_length` argument specifies the maximum length of the generated sequence. The following code examples shows a few ways to prompt the model.
+
+```python
+output = gemma_lm.generate("what is keras in 3 bullet points?", max_length=64)
+print(output)
+what is keras in 3 bullet points?
+
+* **A Deep Learning Framework:** Keras is a high-level API that makes it easy to build and train deep learning models by providing a user-friendly interface.
+* **User-Friendly and Fast:** It abstracts away complex mathematical details, allowing users to
+```
+
+You can also provide batched prompts using a list as input:
+
+```python
+output = gemma_lm.generate(
+    ["what is keras in 3 bullet points?",
+     "The universe is"],
+    max_length=64)
+for item in output:
+    print(item)
+    print("-"*80)
+what is keras in 3 bullet points?
+
+* **A Deep Learning Framework:** Keras is a high-level API that makes it easy to build and train deep learning models by providing a user-friendly interface.
+* **User-Friendly and Fast:** It abstracts away complex underlying computations, allowing users to
+---
+The universe is vast and mysterious. It stretches beyond our comprehension, filled with wonders we are only beginning to uncover. From the swirling galaxies to the silent depths of the ocean, the universe whispers secrets of existence, inviting us to explore, to question, and to marvel at the sheer scale of it all.
+
+This
+---
+```
+
+If you're running on JAX or TensorFlow backends, you should notice that the second `generate()` call returns an answer more quickly. This performance improvement is because each call to `generate()` for a given batch size and `max_length` is compiled with XLA. The first run is expensive, but subsequent runs are faster.
+
+### Use a prompt template
+
+When building more complex requests or multi-turn chat interactions use a prompt template to structure your request. The following code creates a standard template for Gemma prompts:
+
+```python
+PROMPT_TEMPLATE = """<|turn>user
+{question}
+<turn|>
+<|turn>model
+"""
+```
+
+The following code shows how to use the template to format a simple request:
+
+```python
+question = """what is keras in 3 bullet points?"""
+prompt = PROMPT_TEMPLATE.format(question=question)
+output = gemma_lm.generate(prompt)
+print(output)
+<|turn>user
+what is keras in 3 bullet points?
+<turn|>
+<|turn>model
+Here are three bullet points explaining what Keras is:
+
+* **High-Level API for Deep Learning:** Keras is a user-friendly, high-level neural networks API that allows developers to quickly build, train, and evaluate deep learning models with minimal code.
+* **Abstraction and Flexibility:** It provides a flexible and modular interface, making it easy to define complex network architectures (like CNNs or RNNs) without getting bogged down in the low-level mathematical details of frameworks like TensorFlow.
+* **Backend Agnostic:** Keras acts as a consistent interface that can run on top of various powerful deep learning backends (most commonly TensorFlow, but also others), allowing users to switch frameworks easily.<turn|>
+```
+
+### Optional: Try a different sampler
+
+You can control the generation strategy for model object by setting the `sampler` argument on `compile()`. By default, [`"greedy"`](https://keras.io/api/keras_nlp/samplers/greedy_sampler/#greedysampler-class) sampling will be used. As an experiment, try setting a [`"top_k"`](https://keras.io/api/keras_nlp/samplers/top_k_sampler/) strategy:
+
+```python
+gemma_lm.compile(sampler="top_k")
+output = gemma_lm.generate("The universe is", max_length=64)
+print(output)
+The universe is vast.
+
+The stars glitter like scattered diamonds on a black velvet canvas. Nebulae swirl in vibrant hues, painting cosmic masterpieces, whispering tales of ancient creation. Galaxies spin in majestic dances, their arms reaching out into the void, a breathtaking spectacle for the eyes.
+
+Everywhere, there, in
+```
+
+While the default greedy algorithm always picks the token with the largest probability, the top-K algorithm randomly picks the next token from the tokens of top K probability. You don't have to specify a sampler, and you can ignore the last code snippet if it's not helpful to your use case. If you'd like learn more about the available samplers, see [Samplers](https://keras.io/api/keras_nlp/samplers/).
+
+## Generate text with image data
+
+With Gemma 3 and later models, you can use images as part of a prompt to generate output. This capability allows you to use Gemma to interpret visual content or use images as data for content generation.
+
+### Create image loader function
+
+The following function loads an image file from a URL and tokenizes it for use in Gemma prompt:
+
+```python
+import numpy as np
+import PIL
+
+def read_image(url):
+    """Reads image from URL as NumPy array."""
+
+    image_path = keras.utils.get_file(origin=url)
+    image = PIL.Image.open(image_path)
+    image = np.array(image)
+    return image
+```
+
+### Load image for a prompt
+
+Load the image and format the data so the model can process it. Use `read_image()` function defined in the previous section, as shown in the example code below:
+
+```python
+from matplotlib import pyplot as plt
+
+image = read_image(
+    "https://ai.google.dev/gemma/docs/images/thali-indian-plate.jpg"
+)
+plt.imshow(image)
+<matplotlib.image.AxesImage at 0x7ebbf41738f0>
+```
+
+![png](../assets/deployment/output_UnIMXwl2evBe_1.png)
+
+![](../assets/deployment/thali-indian-plate.jpg)
+
+**Figure 1.** Image of Thali Indian food on a metal plate.
+
+### Run request with an image
+
+When prompting the Gemma 4 model with image content, you use a specific string sequence, `<|image|>`, within your prompt to include the image as part of the prompt. Use a prompt template, such as the `PROMPT_TEMPLATE` string defined previously, to format your request as shown in the following prompt code:
+
+```python
+question = """Which cuisine is this: <|image|>?
+Identify the food items present. Which macros is the meal
+high and low on? Keep your answer short.
+"""
+
+output = gemma_lm.generate(
+    {
+        "images": image,
+        "prompts": PROMPT_TEMPLATE.format(question=question),
+    },
+)
+print(output)
+<|turn>user
+Which cuisine is this: <|image>?
+Identify the food items present. Which macros is the meal
+high and low on? Keep your answer short.
+
+<turn|>
+<|turn>model
+Based on the image, here is the analysis:
+
+**Cuisine:**
+The food items strongly suggest **Indian cuisine** or South Asian cuisine.
+
+**Food Items Present:**
+
+*   **Flatbread:** Likely Roti, Chapati, or Naan.
+*   **Rice:** Plain steamed white rice.
+*   **Dips/Condiments:** A white sauce (like raita or yogurt-based sauce), and several curries/sauces (one appears to be a lentil/vegetable curry, another a tomato/onion-based curry, and others green/spicy sauces).
+
+**Macros Analysis (General Estimate):**
+
+*   **High on:** Carbohydrates (from rice and flatbread) and Fats (depending on how the flatbread was cooked and the richness of the sauces/curries).
+*   **Low on:** Protein (unless a significant amount of meat/legumes is present in the unseen curries) and Fiber (unless the curries are vegetable-heavy).<turn|>
+```
+
+If you are using a smaller GPU, and encountering out of memory (OOM) errors, you can set `max_images_per_prompt` and `sequence_length` to smaller values. The following code shows how to reduce sequence length to 768.
+
+```python
+gemma_lm.preprocessor.max_images_per_prompt = 2
+gemma_lm.preprocessor.sequence_length = 768
+```
+
+### Run requests with multiple images
+
+When using more than one image in a prompt, use multiple `<|image|>` tokens for each provided image, as shown in the following example:
+
+```python
+photo_a = read_image("https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png")
+photo_b = read_image("https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/surprise.png")
+
+question = """I have two images:
+
+Photo A: <|image|>
+Photo B: <|image|>
+
+Tell me a bit about these.
+Keep it short.
+"""
+
+output = gemma_lm.generate(
+    {
+        "images": [photo_a, photo_b],
+        "prompts": PROMPT_TEMPLATE.format(question=question),
+    },
+)
+print(output)
+<|turn>user
+I have two images:
+
+Photo A: <|image>
+Photo B: <|image>
+
+Tell me a bit about these.
+Keep it short.
+
+<turn|>
+<|turn>model
+Photo A is a picture of the **Golden Gate Bridge** in San Francisco, California, with water and hills in the background.
+
+Photo B is a close-up portrait of a **cat** with black and white markings and striking green eyes.<turn|>
+```
+
+## What's next
+
+In this tutorial, you learned how to generate text using Keras and Gemma. Here are a few suggestions for what to learn next:
+
+- Learn how to [finetune a Gemma model](../tuning/keras-lora.md).
+- Learn how to perform [distributed fine-tuning and inference on a Gemma model](../tuning/distributed.md).
+- Learn about [Gemma integration with Vertex AI](google-cloud.md)
+- Learn how to [use Gemma models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).
diff --git a/docs/deployment/langchain.md b/docs/deployment/langchain.md
index 84b0d01..12483c6 100644
--- a/docs/deployment/langchain.md
+++ b/docs/deployment/langchain.md
@@ -1,3 +1,315 @@
-# LangChain
+# Get started with Gemma and LangChain
 
-Integrate Gemma with LangChain for RAG and agent workflows.
+
+This tutorial shows you how to get started with [Gemma](../index.md) and [LangChain](https://python.langchain.com/docs/get_started/introduction), running in Google Cloud or in your Colab environment. Gemma is a family of light-weight, state-of-the-art open models built from the same research and technology used to create the Gemini models. LangChain is a framework for building and deploying context-aware applications backed by language models.
+
+!!! Note
+    This tutorial runs on A100 GPU in Google Colab. Free Colab hardware acceleration is *insufficient* to run all the code.
+
+
+## Run Gemma in Google Cloud
+
+The [`langchain-google-vertexai`](https://pypi.org/project/langchain-google-vertexai/) package provides LangChain integration with Google Cloud models.
+
+### Install dependencies
+
+```bash
+pip install --upgrade -q langchain langchain-google-vertexai
+```
+
+### Authenticate
+
+Unless you're using Colab Enterprise, you need to authenticate.
+
+```python
+from google.colab import auth
+auth.authenticate_user()
+```
+
+### Deploy the model
+
+Vertex AI is a platform for training and deploying AI models and applications. Model Garden is a curated collection of models that you can explore in the Google Cloud console.
+
+To deploy Gemma, [open the model](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335) in Model Garden for Vertex AI and complete the following steps:
+
+1. Select **Deploy**.
+2. Make any desired changes to the deployment form fields, or leave them as is, if you're okay with the defaults. Make note of the following fields, which you'll need later:
+    - **Endpoint name** (for example, `google_gemma-7b-it-mg-one-click-deploy`)
+    - **Region** (for example, `us-west1`)
+3. Select **Deploy** to deploy the model to Vertex AI. The deployment will take a few minutes to complete.
+
+When the endpoint is ready, copy its project ID, endpoint ID, and location, and enter them as parameters.
+
+```python
+# @title Basic parameters
+project: str = ""  # @param {type:"string"}
+endpoint_id: str = ""  # @param {type:"string"}
+location: str = "" # @param {type:"string"}
+```
+
+### Run the model
+
+```python
+from langchain_google_vertexai import GemmaVertexAIModelGarden, GemmaChatVertexAIModelGarden
+
+llm = GemmaVertexAIModelGarden(
+    endpoint_id=endpoint_id,
+    project=project,
+    location=location,
+)
+
+output = llm.invoke("What is the meaning of life?")
+print(output)
+Prompt:
+What is the meaning of life?
+Output:
+Life is a complex and multifaceted phenomenon that has fascinated philosophers, scientists, and
+```
+
+You can also use Gemma for multi-turn chat:
+
+```python
+from langchain_core.messages import (
+    HumanMessage
+)
+
+llm = GemmaChatVertexAIModelGarden(
+    endpoint_id=endpoint_id,
+    project=project,
+    location=location,
+)
+
+message1 = HumanMessage(content="How much is 2+2?")
+answer1 = llm.invoke([message1])
+print(answer1)
+
+message2 = HumanMessage(content="How much is 3+3?")
+answer2 = llm.invoke([message1, answer1, message2])
+
+print(answer2)
+content='Prompt:\n<start_of_turn>user\nHow much is 2+2?<end_of_turn>\n<start_of_turn>model\nOutput:\nSure, the answer is 4.\n\n2 + 2 = 4'
+content='Prompt:\n<start_of_turn>user\nHow much is 2+2?<end_of_turn>\n<start_of_turn>model\nPrompt:\n<start_of_turn>user\nHow much is 2+2?<end_of_turn>\n<start_of_turn>model\nOutput:\nSure, the answer is 4.\n\n2 + 2 = 4<end_of_turn>\n<start_of_turn>user\nHow much is 3+3?<end_of_turn>\n<start_of_turn>model\nOutput:\nSure, the answer is 6.\n\n3 + 3 = 6'
+```
+
+You can post-process responses to avoid repetitions:
+
+```python
+answer1 = llm.invoke([message1], parse_response=True)
+print(answer1)
+
+answer2 = llm.invoke([message1, answer1, message2], parse_response=True)
+
+print(answer2)
+content='Output:\nSure, here is the answer:\n\n2 + 2 = 4'
+content='Output:\nSure, here is the answer:\n\n3 + 3 = 6<'
+```
+
+## Run Gemma from a Kaggle download
+
+This section shows you how to download Gemma from Kaggle and then run the model.
+
+To complete this section, you'll first need to complete the setup instructions at [Gemma setup](../get-started/setup.md).
+
+Then move on to the next section, where you'll set environment variables for your Colab environment.
+
+!!! Note
+    This section of the tutorial runs on A100 GPU in Google Colab.
+
+
+### Set environment variables
+
+Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`.
+
+```python
+import os
+from google.colab import userdata
+
+# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env
+# vars as appropriate for your system.
+os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
+os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')
+```
+
+### Install dependencies
+
+```bash
+# Install Keras 3 last. See https://keras.io/getting_started/ for more details.
+pip install -q -U keras-nlp
+pip install -q -U keras>=3
+```
+
+### Run the model
+
+```python
+from langchain_google_vertexai import GemmaLocalKaggle
+```
+
+You can specify the Keras backend (by default it's `tensorflow`, but you can change it to `jax` or `torch`).
+
+```python
+# @title Basic parameters
+keras_backend: str = "jax"  # @param {type:"string"}
+model_name: str = "gemma_2b_en" # @param {type:"string"}
+
+llm = GemmaLocalKaggle(model_name=model_name, keras_backend=keras_backend)
+Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+```
+
+```python
+output = llm.invoke("What is the meaning of life?", max_tokens=30)
+print(output)
+What is the meaning of life?
+
+The question is one of the most important questions in the world.
+
+It’s the question that has
+```
+
+### Run the chat model
+
+As in the Google Cloud example above, you can use a local deployment of Gemma for multi-turn chat. You might need to re-start the notebook and clean your GPU memory in order to avoid OOM errors:
+
+```python
+from langchain_google_vertexai import GemmaChatLocalKaggle
+
+# @title Basic parameters
+keras_backend: str = "jax"  # @param {type:"string"}
+model_name: str = "gemma_2b_en" # @param {type:"string"}
+
+llm = GemmaChatLocalKaggle(model_name=model_name, keras_backend=keras_backend)
+Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'config.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_2b_en/2' to your Colab notebook...
+```
+
+```python
+from langchain_core.messages import (
+    HumanMessage
+)
+
+message1 = HumanMessage(content="Hi! Who are you?")
+answer1 = llm.invoke([message1], max_tokens=30)
+print(answer1)
+content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n Tampoco\nI'm a model."
+```
+
+```python
+message2 = HumanMessage(content="What can you help me with?")
+answer2 = llm.invoke([message1, answer1, message2], max_tokens=60)
+
+print(answer2)
+content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\n<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n Tampoco\nI'm a model.<end_of_turn>\n<start_of_turn>user\nWhat can you help me with?<end_of_turn>\n<start_of_turn>model"
+```
+
+You can post-process the response if you want to avoid multi-turn statements:
+
+```python
+answer1 = llm.invoke([message1], max_tokens=30, parse_response=True)
+print(answer1)
+
+answer2 = llm.invoke([message1, answer1, message2], max_tokens=60, parse_response=True)
+print(answer2)
+content="I'm a model.\n Tampoco\nI'm a model."
+content='I can help you with your modeling.\n Tampoco\nI can'
+```
+
+## Run Gemma from a Hugging Face download
+
+### Setup
+
+Like Kaggle, Hugging Face requires that you accept the Gemma terms and conditions before accessing the model. To get access to Gemma through Hugging Face, go to the [Gemma model card](https://huggingface.co/google/gemma-2b).
+
+You'll also need to get a [user access token](https://huggingface.co/docs/hub/en/security-tokens) with read permissions, which you can enter below.
+
+!!! Note
+    This section of the tutorial runs on A100 GPU in Google Colab.
+
+
+```python
+# @title Basic parameters
+hf_access_token: str = ""  # @param {type:"string"}
+model_name: str = "google/gemma-2b" # @param {type:"string"}
+```
+
+### Run the model
+
+```python
+from langchain_google_vertexai import GemmaLocalHF, GemmaChatLocalHF
+
+llm = GemmaLocalHF(model_name="google/gemma-2b", hf_access_token=hf_access_token)
+tokenizer_config.json:   0%|          | 0.00/1.11k [00:00<?, ?B/s]
+tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]
+tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]
+special_tokens_map.json:   0%|          | 0.00/555 [00:00<?, ?B/s]
+config.json:   0%|          | 0.00/627 [00:00<?, ?B/s]
+model.safetensors.index.json:   0%|          | 0.00/13.5k [00:00<?, ?B/s]
+Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]
+model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]
+model-00002-of-00002.safetensors:   0%|          | 0.00/67.1M [00:00<?, ?B/s]
+Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
+generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]
+```
+
+```python
+output = llm.invoke("What is the meaning of life?", max_tokens=50)
+print(output)
+What is the meaning of life?
+
+The question is one of the most important questions in the world.
+
+It’s the question that has been asked by philosophers, theologians, and scientists for centuries.
+
+And it’s the question that
+```
+
+As in the examples above, you can use a local deployment of Gemma for multi-turn chat. You might need to re-start the notebook and clean your GPU memory in order to avoid OOM errors:
+
+### Run the chat model
+
+```python
+llm = GemmaChatLocalHF(model_name=model_name, hf_access_token=hf_access_token)
+Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
+```
+
+```python
+from langchain_core.messages import (
+    HumanMessage
+)
+
+message1 = HumanMessage(content="Hi! Who are you?")
+answer1 = llm.invoke([message1], max_tokens=60)
+print(answer1)
+content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n<end_of_turn>\n<start_of_turn>user\nWhat do you mean"
+```
+
+```python
+message2 = HumanMessage(content="What can you help me with?")
+answer2 = llm.invoke([message1, answer1, message2], max_tokens=140)
+
+print(answer2)
+content="<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\n<start_of_turn>user\nHi! Who are you?<end_of_turn>\n<start_of_turn>model\nI'm a model.\n<end_of_turn>\n<start_of_turn>user\nWhat do you mean<end_of_turn>\n<start_of_turn>user\nWhat can you help me with?<end_of_turn>\n<start_of_turn>model\nI can help you with anything.\n<"
+```
+
+As in the previous examples, you can post-process the response:
+
+```python
+answer1 = llm.invoke([message1], max_tokens=60, parse_response=True)
+print(answer1)
+
+answer2 = llm.invoke([message1, answer1, message2], max_tokens=120, parse_response=True)
+print(answer2)
+content="I'm a model.\n<end_of_turn>\n"
+content='I can help you with anything.\n<end_of_turn>\n<end_of_turn>\n'
+```
+
+## What's next
+
+- Learn how to [finetune a Gemma model](../tuning/keras-lora.md).
+- Learn how to perform [distributed fine-tuning and inference on a Gemma model](../tuning/distributed.md).
+- Learn how to [use Gemma models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).
diff --git a/docs/deployment/litert.md b/docs/deployment/litert.md
index 689441e..486c009 100644
--- a/docs/deployment/litert.md
+++ b/docs/deployment/litert.md
@@ -1,3 +1,214 @@
-# LiteRT
+# Convert Hugging Face Safetensors to MediaPipe Task
 
-Deploy Gemma on mobile and edge devices with LiteRT.
+This guide provides instructions for converting Gemma models in Hugging Face
+Safetensors format (`.safetensors`) into the MediaPipe Task file format
+(`.task`). This conversion is essential for deploying either pre-trained or
+fine-tuned Gemma models for on-device inference on Android and iOS using the
+[MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference) and LiteRT runtime.
+
+!!! Note
+    This process is not yet supported for Web deployment.
+
+
+![Flowchart for packaging a Hugging Face model into a MediaPipe Task file](../assets/deployment/hf-to-mediapipe-task-flow.png)
+
+To create the required Task Bundle (`.task`), you will use
+[LiteRT Torch](https://github.com/google-ai-edge/litert-torch/tree/main/litert_torch/generative). This tool exports PyTorch models into
+multi-signature LiteRT (`.tflite`) models, which are compatible with the
+MediaPipe LLM Inference API and suitable for running on CPU backends in mobile
+applications.
+
+The final `.task` file is a self-contained package required by MediaPipe,
+bundling the LiteRT model, the tokenizer model, and essential metadata. This
+bundle is necessary because the tokenizer (which converts text prompts into
+token embeddings for the model) must be packaged with the LiteRT model to
+enable end-to-end inference.
+
+!!! Note
+    The LiteRT Torch Generative API is CPU-only and it doesn't yet support GPU and NPU.
+
+
+Here is a step-by-step breakdown of the process:
+
+![Step-by-step breakdown of the process](../assets/deployment/hf-to-mediapipe-task-overview.png)
+
+## 1. Get Your Gemma model
+
+You have two options to get started.
+
+### Option A. Use an Existing Fine-Tuned Model
+
+If you have a [fine-tuned Gemma
+model](../tuning/full-finetune.md) prepared, just proceed
+to the next step.
+
+### Option B. Download the official instruction-tuned Model
+
+If you need a model, you can download an instruction-tuned Gemma from the
+Hugging Face Hub.
+
+**Setup necessary tools:**
+
+```text
+python -m venv hf
+source hf/bin/activate
+pip install huggingface_hub[cli]
+```
+
+**Download the model:**
+
+Models on the Hugging Face Hub are identified by a model ID, typically in the
+format `<organization_or_username>/<model_name>`. For example, to download the
+official Google Gemma 3 270M instruction-tuned model, use:
+
+```python
+hf download google/gemma-3-270m-it --local-dir "PATH_TO_HF_MODEL"
+#"google/gemma-3-1b-it", etc
+```
+
+## 2. Convert and quantize the model to LiteRT
+
+Set up a Python virtual environment and install the latest stable release of the
+LiteRT Torch package:
+
+```text
+python -m venv litert-torch
+source litert-torch/bin/activate
+pip install "litert-torch>=0.8.0"
+```
+
+Use the following script to convert the Safetensor into LiteRT model.
+
+```python
+from litert_torch.generative.examples.gemma3 import gemma3
+from litert_torch.generative.utilities import converter
+from litert_torch.generative.utilities.export_config import ExportConfig
+from litert_torch.generative.layers import kv_cache
+
+pytorch_model = gemma3.build_model_270m("PATH_TO_HF_MODEL")
+
+# If you are using Gemma 3 1B
+#pytorch_model = gemma3.build_model_1b("PATH_TO_HF_MODEL")
+
+export_config = ExportConfig()
+export_config.kvcache_layout = kv_cache.KV_LAYOUT_TRANSPOSED
+export_config.mask_as_input = True
+
+converter.convert_to_tflite(
+    pytorch_model,
+    output_path="OUTPUT_DIR_PATH",
+    output_name_prefix="my-gemma3",
+    prefill_seq_len=2048,
+    kv_cache_max_len=4096,
+    quantize="dynamic_int8",
+    export_config=export_config,
+)
+```
+
+Be aware that this process is time-consuming and depends on your computer's
+processing speed. For reference, on a 2025 8-core CPU, a 270M model takes over
+5-10 minutes, while a 1B model can take approximately 10-30 minutes.
+
+The final output, a LiteRT model, will be saved to your specified
+`OUTPUT_DIR_PATH`.
+
+Tune the following values based on the memory and performance constraints of
+your target device.
+
+- `kv_cache_max_len`: Defines the total allocated size of the model's working memory (the KV cache). This capacity is a hard limit and must be sufficient to store the combined sum of the prompt's tokens (the prefill) and all subsequently generated tokens (the decode).
+- `prefill_seq_len`: Specifies the token count of the input prompt for prefill chunking. When processing the input prompt using prefill chunking, the entire sequence (e.g., 50,000 tokens) is not computed at once; instead, it's broken into manageable segments (e.g., chunks of 2,048 tokens) that are loaded sequentially into the cache to prevent an out-of-memory error.
+- `quantize`: string for the selected quantization schemes. Following is the list of available quantization recipes for Gemma 3.
+    - `none` : No quantization
+    - `fp16` : FP16 weights, FP32 activations and floating point computation for all ops
+    - `dynamic_int8` : FP32 activations, INT8 weights, and integer computation
+    - `weight_only_int8` : FP32 activations, INT8 weights, and floating point computation
+
+## 3. Create a Task Bundle from LiteRT and tokenizer
+
+Set up a Python virtual environment and install
+[mediapipe](https://pypi.org/project/mediapipe/) Python package:
+
+```text
+python -m venv mediapipe
+source mediapipe/bin/activate
+pip install mediapipe
+```
+
+Use the `genai.bundler` library to bundle the model:
+
+```python
+from mediapipe.tasks.python.genai import bundler
+config = bundler.BundleConfig(
+    tflite_model="PATH_TO_LITERT_MODEL.tflite",
+    tokenizer_model="PATH_TO_HF_MODEL/tokenizer.model",
+    start_token="<bos>",
+    stop_tokens=["<eos>", "<end_of_turn>"],
+    output_filename="PATH_TO_TASK_BUNDLE.task",
+    prompt_prefix="<start_of_turn>user\n",
+    prompt_suffix="<end_of_turn>\n<start_of_turn>model\n",
+)
+bundler.create_bundle(config)
+```
+
+The `bundler.create_bundle` function creates a `.task` file that contains all
+the necessary information to run the model.
+
+## 4. Inference with Mediapipe on Android
+
+Initialize the task with basic configuration options:
+
+```python
+// Default values for LLM models
+private object LLMConstants {
+    const val MODEL_PATH = "PATH_TO_TASK_BUNDLE_ON_YOUR_DEVICE.task"
+    const val DEFAULT_MAX_TOKEN = 4096
+    const val DEFAULT_TOPK = 64
+    const val DEFAULT_TOPP = 0.95f
+    const val DEFAULT_TEMPERATURE = 1.0f
+}
+
+// Set the configuration options for the LLM Inference task
+val taskOptions = LlmInference.LlmInferenceOptions.builder()
+    .setModelPath(LLMConstants.MODEL_PATH)
+    .setMaxTokens(LLMConstants.DEFAULT_MAX_TOKEN)
+    .build()
+
+// Create an instance of the LLM Inference task
+llmInference = LlmInference.createFromOptions(context, taskOptions)
+llmInferenceSession =
+    LlmInferenceSession.createFromOptions(
+        llmInference,
+        LlmInferenceSession.LlmInferenceSessionOptions.builder()
+            .setTopK(LLMConstants.DEFAULT_TOPK)
+            .setTopP(LLMConstants.DEFAULT_TOPP)
+            .setTemperature(LLMConstants.DEFAULT_TEMPERATURE)
+            .build(),
+    )
+```
+
+Use the `generateResponse()` method to generate a text response.
+
+```python
+val result = llmInferenceSession.generateResponse(inputPrompt)
+logger.atInfo().log("result: $result")
+```
+
+To stream the response, use the `generateResponseAsync()` method.
+
+```python
+llmInferenceSession.generateResponseAsync(inputPrompt) { partialResult, done ->
+    logger.atInfo().log("partial result: $partialResult")
+}
+```
+
+See the [LLM Inference guide for Android](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference/android) for more
+information.
+
+## Next steps
+
+Build and explore more with Gemma models:
+
+- [Deploy Gemma on mobile devices](mobile.md)
+- [Fine-tune Gemma for text tasks using Hugging Face Transformers](../tuning/qlora.md)
+- [Fine-tune Gemma for vision tasks using Hugging Face Transformers](../tuning/vision-qlora.md)
+- [Full model fine-tuning using Hugging Face Transformers](../tuning/full-finetune.md)
diff --git a/docs/deployment/llamacpp.md b/docs/deployment/llamacpp.md
index 6e5dc2a..423d171 100644
--- a/docs/deployment/llamacpp.md
+++ b/docs/deployment/llamacpp.md
@@ -1,5 +1,106 @@
-# Llama.cpp
+---
+title: llama.cpp
+description: Run Gemma 4 with llama.cpp — GGUF repos for every size, image/audio/video input, and the OpenAI-compatible server.
+---
 
-Run Gemma with llama.cpp for CPU and GPU inference across Linux, macOS, and Windows.
+# Run Gemma with llama.cpp
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+[llama.cpp](https://github.com/ggml-org/llama.cpp) is the engine underneath most local LLM
+tooling. Using it directly gives full control over quantization and GPU offload, and it is the
+most capable local runtime for Gemma 4 — **image, audio and video input all work**.
+
+## Install
+
+=== "macOS / Linux"
+
+    ```bash
+    brew install llama.cpp
+    ```
+
+=== "Windows"
+
+    ```bash
+    winget install llama.cpp
+    ```
+
+=== "conda"
+
+    ```bash
+    conda install -c conda-forge llama.cpp
+    ```
+
+=== "Nix"
+
+    ```bash
+    nix profile install nixpkgs#llama-cpp
+    ```
+
+## Run
+
+```bash
+llama-cli -hf ggml-org/gemma-4-E2B-it-GGUF --prompt "Write a poem about the Kraken."
+```
+
+`-hf` downloads the weights on first use, including the multimodal projector. With a system
+prompt:
+
+```bash
+llama-cli -hf ggml-org/gemma-4-E2B-it-GGUF -sys "You are Hong Gildong." -p "Who are you?"
+```
+
+## Model repositories
+
+| Size | Repository |
+|---|---|
+| E2B | `ggml-org/gemma-4-E2B-it-GGUF` |
+| E4B | `ggml-org/gemma-4-E4B-it-GGUF` |
+| 12B | `ggml-org/gemma-4-12B-it-GGUF` |
+| 26B A4B | `ggml-org/gemma-4-26B-A4B-it-GGUF` |
+| 31B | `ggml-org/gemma-4-31B-it-GGUF` |
+
+For 4-bit, prefer Google's [QAT](qat.md) weights — trained with quantization simulated rather
+than compressed afterwards:
+
+```bash
+llama-cli -hf google/gemma-4-12B-it-qat-q4_0-gguf -p "Explain PLE in one paragraph"
+```
+
+Available as `google/gemma-4-{E2B,E4B,12B,26B-A4B,31B}-it-qat-q4_0-gguf`.
+
+## Multimodal
+
+```bash
+llama-mtmd-cli -hf ggml-org/gemma-4-E2B-it-GGUF
+```
+
+With local files, pass the projector explicitly:
+
+```bash
+llama-server -m gemma-4-12B-it-Q4_K_M.gguf --mmproj mmproj-gemma-4-12B-it.gguf
+```
+
+!!! note
+    Audio works on E2B, E4B and 12B. The 26B A4B and 31B models take text and images, and read
+    video as frames — they have no audio encoder.
+
+## Server
+
+```bash
+llama-server -hf ggml-org/gemma-4-E2B-it-GGUF
+```
+
+Browser UI at `http://localhost:8080`, OpenAI-compatible API at `http://localhost:8080/v1`.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")
+
+response = client.chat.completions.create(
+    model="gemma-4",  # llama-server serves whichever model it was started with
+    messages=[{"role": "user", "content": "Say this is a test"}],
+)
+print(response.choices[0].message.content)
+```
+
+Multimodal input consumes context quickly; set it with `-c`, for example `-c 32768`.
diff --git a/docs/deployment/lmstudio.md b/docs/deployment/lmstudio.md
index 7e6cf1d..2d3325e 100644
--- a/docs/deployment/lmstudio.md
+++ b/docs/deployment/lmstudio.md
@@ -1,3 +1,64 @@
-# LM Studio
+---
+title: LM Studio
+description: Run Gemma 4 with LM Studio — desktop app, lms CLI, and the OpenAI-compatible local server.
+---
 
-Run Gemma locally with LM Studio.
+# Run Gemma with LM Studio
+
+[LM Studio](https://lmstudio.ai) is a desktop app for running local models, with a GUI, a CLI
+and a local server. It supports Gemma in both GGUF (llama.cpp) and MLX formats.
+
+## Install
+
+Download for macOS, Windows or Linux from
+[lmstudio.ai/download](https://lmstudio.ai/download).
+
+## Get a model
+
+In the app, press ++cmd+shift+m++ (macOS) or ++ctrl+shift+m++ (Windows/Linux), search for
+"Gemma", and LM Studio suggests the variant that fits your hardware.
+
+From the terminal:
+
+```bash
+lms get gemma-4
+```
+
+List what you have, then load one:
+
+```bash
+lms ls
+lms load <model_key>
+```
+
+`lms load` takes `--gpu=max|auto|0.0-1.0` and `--context-length=1-N`.
+
+## Server
+
+```bash
+lms server start
+```
+
+OpenAI-compatible API at `http://localhost:1234/v1`, serving `/v1/chat/completions`,
+`/v1/completions`, `/v1/embeddings`, `/v1/models` and `/v1/responses`.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")
+
+response = client.chat.completions.create(
+    model="gemma-4-12b",
+    messages=[{"role": "user", "content": "Say this is a test"}],
+)
+print(response.choices[0].message.content)
+```
+
+## Your own GGUF
+
+```bash
+lms import <path/to/model.gguf>
+```
+
+The model appears under **My Models**. See
+[Import Models](https://lmstudio.ai/docs/app/advanced/import-model) if it isn't detected.
diff --git a/docs/deployment/mlx.md b/docs/deployment/mlx.md
index a031a39..40c739e 100644
--- a/docs/deployment/mlx.md
+++ b/docs/deployment/mlx.md
@@ -1,5 +1,69 @@
-# Apple Silicon (MLX)
+---
+title: Apple MLX
+description: Run Gemma 4 on Apple silicon with MLX — text and vision generation, model repos, and the OpenAI-compatible server.
+---
 
-Run Gemma models locally on Mac M1/M2/M3/M4 hardware using MLX.
+# Run Gemma with MLX
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+[MLX](https://github.com/ml-explore/mlx) is Apple's array framework for machine learning on
+Apple silicon. It runs Gemma natively on the unified memory architecture, without a GGUF
+conversion step.
+
+## Install
+
+```bash
+pip install mlx mlx-lm mlx-vlm
+```
+
+## Run
+
+=== "Text"
+
+    ```bash
+    mlx_lm.generate --model mlx-community/gemma-4-e2b-it-4bit --prompt "Who are you?"
+    ```
+
+=== "Vision"
+
+    ```bash
+    mlx_vlm.generate --model mlx-community/gemma-4-e2b-it-4bit \
+      --prompt "Describe this image." --image <path_to_image>
+    ```
+
+## Model repositories
+
+Community-converted weights live under
+[mlx-community](https://huggingface.co/mlx-community):
+
+| Size | Repository |
+|---|---|
+| E2B | `mlx-community/gemma-4-e2b-it-4bit` |
+| E4B | `mlx-community/gemma-4-e4b-it-qat-OptiQ-4bit` |
+| 12B | `mlx-community/gemma-4-12B-it-4bit`, `…-it-8bit` |
+| 26B A4B | `mlx-community/gemma-4-26b-a4b-it-4bit` |
+
+!!! note
+    Coverage and naming vary — these are community conversions, not first-party releases.
+    Search [mlx-community for Gemma 4](https://huggingface.co/mlx-community?search=gemma-4) for
+    the current list. [Ollama](ollama.md) also ships MLX builds for every size under its `-mlx`
+    tags, if you would rather not pick a repository.
+
+## Server
+
+```bash
+mlx_vlm.server --model mlx-community/gemma-4-e2b-it-4bit --port 8080
+```
+
+OpenAI-compatible API at `http://localhost:8080/v1`.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")
+
+response = client.chat.completions.create(
+    model="mlx-community/gemma-4-e2b-it-4bit",
+    messages=[{"role": "user", "content": "Say this is a test"}],
+)
+print(response.choices[0].message.content)
+```
diff --git a/docs/deployment/mobile.md b/docs/deployment/mobile.md
index 18fe69d..3929f4a 100644
--- a/docs/deployment/mobile.md
+++ b/docs/deployment/mobile.md
@@ -1,3 +1,40 @@
-# Mobile Deployment
+# Deploy Gemma on mobile devices
 
-Deploy Gemma on Android and iOS devices.
+This document outlines various methods and tools for deploying and running Gemma
+models on mobile devices, including using the Google AI Edge Gallery app and the
+MediaPipe LLM Inference API.
+
+For information on converting a fine-tuned Gemma model to a LiteRT version, see
+the [Conversion Guide](litert.md).
+
+## Google AI Edge Gallery app
+
+To see the LLM Inference APIs in action and test your Task Bundle model, you can
+use the [Google AI Edge Gallery app](https://github.com/google-ai-edge/gallery).
+This app provides a user interface for interacting with on-device LLMs,
+allowing you to:
+
+- **Import Models:** Load your custom `.task` models into the app.
+- **Configure Parameters:** Adjust settings like temperature and top-k.
+- **Generate Text:** Input prompts and view the model's responses.
+- **Test Performance:** Evaluate the model's speed and accuracy.
+
+For a detailed guide on how to use the Google AI Edge Gallery app, including
+instructions for importing your own models, refer to the app's
+[documentation](https://github.com/google-ai-edge/gallery/blob/main/README.md).
+
+## MediaPipe LLM
+
+You can run Gemma models on mobile devices with the
+[MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference).
+The LLM Inference API acts as a wrapper for large language models, enabling you
+run Gemma models on-device for common text-to-text generation tasks like
+information retrieval, email drafting, and document summarization.
+
+The LLM Inference API is available on the following mobile platforms:
+
+- [Android](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference/android)
+- [iOS](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference/ios)
+
+To learn more, refer to the
+[MediaPipe LLM Inference documentation](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference).
diff --git a/docs/deployment/ollama.md b/docs/deployment/ollama.md
index 661013a..eae93c1 100644
--- a/docs/deployment/ollama.md
+++ b/docs/deployment/ollama.md
@@ -1,5 +1,107 @@
-# Ollama
+---
+title: Ollama
+description: Run Gemma 4 locally with Ollama — model tags, image input, and the OpenAI-compatible server.
+---
 
-Run Gemma locally with Ollama for zero-setup desktop inference.
+# Run Gemma with Ollama
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+Ollama downloads quantized weights for you and exposes a CLI plus a local HTTP API. No GPU
+required, though one helps.
+
+## Install
+
+Download from [ollama.com/download](https://ollama.com/download), then check it worked:
+
+```bash
+ollama --version
+```
+
+## Run
+
+```bash
+ollama run gemma4 "Explain quantum computing simply"
+```
+
+Downloads the model on first use. To fetch without running:
+
+```bash
+ollama pull gemma4:12b
+```
+
+## Model tags
+
+| Tag | Download | Context |
+|---|---|---|
+| `gemma4:e2b` | 7.2 GB | 128K |
+| `gemma4:e4b` | 9.6 GB | 128K |
+| `gemma4:12b` | 7.6 GB | 256K |
+| `gemma4:26b` | 18 GB | 256K |
+| `gemma4:31b` | 20 GB | 256K |
+
+`gemma4:latest` is an alias for `e4b`. On Apple Silicon, the `-mlx` tags
+(`gemma4:12b-mlx`) use the MLX runtime. Full list:
+[ollama.com/library/gemma4/tags](https://ollama.com/library/gemma4/tags).
+
+## Images
+
+```bash
+ollama run gemma4 ./image.png "What is in this image?"
+```
+
+!!! note
+    Ollama publishes Gemma 4 as **text and image only**. For audio input, use
+    [Transformers](huggingface.md) or [llama.cpp](llamacpp.md).
+
+## Server
+
+Ollama serves on port `11434`.
+
+=== "OpenAI-compatible"
+
+    ```python
+    from openai import OpenAI
+
+    client = OpenAI(
+        base_url="http://localhost:11434/v1/",
+        api_key="ollama",  # required but ignored
+    )
+
+    response = client.chat.completions.create(
+        model="gemma4:12b",
+        messages=[{"role": "user", "content": "Say this is a test"}],
+    )
+    print(response.choices[0].message.content)
+    ```
+
+=== "Native API"
+
+    ```bash
+    curl http://localhost:11434/api/generate -d '{
+      "model": "gemma4",
+      "prompt": "roses are red"
+    }'
+    ```
+
+=== "Image via API"
+
+    ```bash
+    IMG=$(base64 < test.jpg | tr -d '\n')
+
+    curl -X POST http://localhost:11434/api/chat \
+      -H "Content-Type: application/json" \
+      -d '{
+        "model": "gemma4",
+        "messages": [{
+          "role": "user",
+          "content": "What is in this image?",
+          "images": ["'"$IMG"'"]
+        }],
+        "stream": false
+      }'
+    ```
+
+!!! tip "Context defaults to 4K under 24 GiB VRAM"
+    Ollama sizes context from available VRAM, not from the model. Raise it when you need it:
+    `OLLAMA_CONTEXT_LENGTH=64000 ollama serve`, or per request with
+    `"options": {"num_ctx": 64000}`. Agents and coding tools want at least 64K —
+    [Ollama: context length](https://docs.ollama.com/context-length).
diff --git a/docs/deployment/qat.md b/docs/deployment/qat.md
index 2493397..558d195 100644
--- a/docs/deployment/qat.md
+++ b/docs/deployment/qat.md
@@ -1,3 +1,68 @@
-# Quantization-Aware Training (QAT)
+---
+title: Quantization & QAT
+description: Which Gemma 4 quantized weights to download for each engine, and why QAT beats post-training quantization at 4-bit.
+---
 
-Official QAT models for maximum efficiency with minimal quality loss.
+# Quantization and QAT
+
+Google publishes **quantization-aware training** (QAT) checkpoints alongside the full-precision
+weights. QAT simulates quantization *during* training, so the model learns to compensate for the
+precision loss — unlike post-training quantization, which compresses a finished model and loses
+quality doing it.
+
+**At 4-bit, prefer a QAT build over a generic Q4 of the same size.**
+
+## Which one do I download?
+
+| Running on | Suffix |
+|---|---|
+| llama.cpp, LM Studio, Ollama | `-qat-q4_0-gguf` |
+| vLLM, SGLang | `-qat-w4a16-ct` |
+| Mobile (Transformers) | `-qat-mobile-transformers` |
+| Mobile (compressed tensors) | `-qat-mobile-ct` |
+| Speculative decoding | `-qat-q4_0-unquantized` + `-qat-q4_0-unquantized-assistant` |
+| Converting to another format (e.g. MLX) | `-qat-q4_0-unquantized` |
+
+Names follow `google/gemma-4-{SIZE}-it{SUFFIX}`, for example:
+
+```bash
+# Local, via llama.cpp
+llama-cli -hf google/gemma-4-12B-it-qat-q4_0-gguf
+
+# Served, via vLLM
+vllm serve google/gemma-4-12B-it-qat-w4a16-ct
+```
+
+## Availability
+
+| Variant | Sizes |
+|---|---|
+| `-gguf`, `-unquantized`, `-assistant` | E2B, E4B, 12B, 26B A4B, 31B |
+| `-w4a16-ct` | E2B, E4B, 12B, 31B |
+| `-mobile-transformers`, `-mobile-ct` | E2B, E4B |
+
+Collections:
+[gemma-4-qat-q4-0](https://huggingface.co/collections/google/gemma-4-qat-q4-0) ·
+[gemma-4-qat-mobile](https://huggingface.co/collections/google/gemma-4-qat-mobile).
+All QAT checkpoints are also on
+[Kaggle](https://www.kaggle.com/models/google/gemma-4/transformers).
+
+!!! note "Mobile builds use a different scheme"
+    `-mobile-*` weights are built on a custom `wNa8o8` schema with 2-bit decoding layers and
+    optimized KV caches, tuned for on-device RAM limits rather than raw quality.
+
+## Memory
+
+Approximate footprint for the static weights, from the
+[Gemma 4 overview](../models/gemma-4/index.md):
+
+| Size | BF16 | SFP8 | Q4_0 |
+|---|---|---|---|
+| E2B | 11.4 GB | 5.7 GB | 2.9 GB |
+| E4B | 17.9 GB | 8.9 GB | 4.5 GB |
+| 12B | 26.7 GB | 13.4 GB | 6.7 GB |
+| 26B A4B | 57.7 GB | 28.8 GB | 14.4 GB |
+| 31B | 69.9 GB | 34.9 GB | 17.5 GB |
+
+Context (KV cache) is on top of this. Size it with the
+[VRAM calculator](../tools/vram-calculator.md).
diff --git a/docs/deployment/sglang.md b/docs/deployment/sglang.md
index a63dd21..9efc389 100644
--- a/docs/deployment/sglang.md
+++ b/docs/deployment/sglang.md
@@ -1,3 +1,52 @@
-# SGLang
+---
+title: SGLang
+description: Serve Gemma 4 with SGLang — install, launch_server, model repositories, and the OpenAI-compatible API.
+---
 
-Serve Gemma with SGLang for structured generation.
+# Serve Gemma with SGLang
+
+[SGLang](https://docs.sglang.io) is a serving engine built for high throughput and structured
+generation. Like [vLLM](vllm.md), it targets multi-user production rather than local
+single-user work.
+
+## Install
+
+```bash
+uv pip install --prerelease=allow sglang
+```
+
+## Serve
+
+```bash
+python3 -m sglang.launch_server \
+  --model-path google/gemma-4-12B-it \
+  --host 0.0.0.0 \
+  --port 30000
+```
+
+## Model repositories
+
+| Size | Repository |
+|---|---|
+| E2B | `google/gemma-4-E2B-it` |
+| E4B | `google/gemma-4-E4B-it` |
+| 12B | `google/gemma-4-12B-it` |
+| 26B A4B | `google/gemma-4-26B-A4B-it` |
+| 31B | `google/gemma-4-31B-it` |
+
+For 4-bit serving, use the [QAT](qat.md) compressed-tensors builds,
+`google/gemma-4-{E2B,E4B,12B,31B}-it-qat-w4a16-ct`.
+
+## Call it
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key="EMPTY", base_url="http://localhost:30000/v1")
+
+response = client.chat.completions.create(
+    model="google/gemma-4-12B-it",
+    messages=[{"role": "user", "content": "Say this is a test"}],
+)
+print(response.choices[0].message.content)
+```
diff --git a/docs/deployment/vllm.md b/docs/deployment/vllm.md
index e8c0a21..ba84000 100644
--- a/docs/deployment/vllm.md
+++ b/docs/deployment/vllm.md
@@ -1,5 +1,62 @@
-# vLLM
+---
+title: vLLM
+description: Serve Gemma 4 with vLLM — install, vllm serve, model repositories, and the OpenAI-compatible API.
+---
 
-Serve Gemma models in production with vLLM for high-throughput inference.
+# Serve Gemma with vLLM
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+[vLLM](https://docs.vllm.ai) is a high-throughput serving engine. Use it when you have more than
+one concurrent user; for single-user local work, [Ollama](ollama.md) or
+[llama.cpp](llamacpp.md) are simpler.
+
+## Install
+
+```bash
+uv pip install vllm --torch-backend=auto
+```
+
+## Serve
+
+```bash
+vllm serve google/gemma-4-12B-it
+```
+
+Serves an OpenAI-compatible API on `http://localhost:8000`.
+
+## Model repositories
+
+| Size | Repository |
+|---|---|
+| E2B | `google/gemma-4-E2B-it` |
+| E4B | `google/gemma-4-E4B-it` |
+| 12B | `google/gemma-4-12B-it` |
+| 26B A4B | `google/gemma-4-26B-A4B-it` |
+| 31B | `google/gemma-4-31B-it` |
+
+Drop `-it` for base weights, which you want only for further training.
+
+For 4-bit serving, use the [QAT](qat.md) compressed-tensors builds — 4-bit weights with 16-bit
+activations:
+
+```bash
+vllm serve google/gemma-4-12B-it-qat-w4a16-ct
+```
+
+Published as `google/gemma-4-{E2B,E4B,12B,31B}-it-qat-w4a16-ct`.
+
+## Call it
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
+
+response = client.chat.completions.create(
+    model="google/gemma-4-12B-it",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Tell me a joke."},
+    ],
+)
+print(response.choices[0].message.content)
+```
diff --git a/docs/deployment/web.md b/docs/deployment/web.md
index fd899e5..6872e36 100644
--- a/docs/deployment/web.md
+++ b/docs/deployment/web.md
@@ -1,3 +1,17 @@
-# Web Browsers
+# Deploy Gemma in web browsers
 
-Run Gemma in web browsers using WebGPU.
+You can run Gemma models completely on-device with the [MediaPipe LLM Inference
+API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference). The
+LLM Inference API acts as a wrapper for large language models, enabling you run
+Gemma models on-device for common text-to-text generation tasks like information
+retrieval, email drafting, and document summarization.
+
+Try the LLM Inference API with [MediaPipe
+Studio](https://mediapipe-studio.webapps.google.com/demo/llm_inference), a
+web-based application for evaluating and customizing on-device models.
+
+For more information on deploying Gemma to web browsers with
+the LLM Inference API, see the
+[LLM Inference guide for Web](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference/web_js).
+To learn more about the MediaPipe LLM Inference capabilities, see the
+[LLM inference guide](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference).
diff --git a/docs/get-started/capabilities/vision.md b/docs/get-started/capabilities/vision.md
index c5cb325..63b08d8 100644
--- a/docs/get-started/capabilities/vision.md
+++ b/docs/get-started/capabilities/vision.md
@@ -1,5 +1,90 @@
-# Vision Understanding
+# Vision understanding
 
-Gemma 4 processes images with variable aspect ratio and resolution support, as well as video frames.
+Gemma 4, the latest model from the Gemma family, can perform a wide range of
+vision-language tasks such as object detection, Optical Character Recognition
+(OCR), visual question answering, image captioning, and reasoning across
+multiple images. It also supports variable resolution processing, allowing you
+to balance inference speed and output accuracy.
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+This section explores how to effectively prepare and use visual data in your
+prompts.
+
+## Visual data
+
+Visual data can come in many formats and resolution. The specific file formats
+supported (such as JPEG and PNG) depend on the framework you choose to convert
+your visual data into tensors.
+
+Here are the key considerations when preparing visual data for Gemma:
+
+- **Token cost:** Each image typically uses 256 tokens, though PaliGemma image token costs vary depending on the specific model selected.
+- **Resolution:** The interpreted resolution---meaning the number of pixels encoded into tokens and processed by the model---depends on the Gemma version you are using:
+    - **Gemma 4:** Variable resolution based on token budget. You can decide between budget sizes of 70, 140, 280, 560, or 1120 tokens, which determines how much the input image is resized and processed.
+    - **Gemma 3:** (4B and higher) 896x896 resolution, with pan-and-scan options for larger images.
+    - **Gemma 3n:** 256x256, 512x512, or 768x768 resolution
+    - **PaliGemma 2:** 224x224, 448x448, or 896x896 resolution
+
+Lower-resolution images process faster but capture fewer visual details. To
+optimize inference speed, you should aim to provide visual data matching one of
+the built-in interpreted resolutions of your chosen Gemma model.
+
+## Variable resolution and token budgets
+
+Gemma 4 models introduce the ability to process images at varying resolutions,
+allowing you to tailor the visual input to your specific task. For example, you
+might opt for a high resolution to pinpoint small details in object detection,
+whereas a lower resolution might be preferable for analyzing individual video
+frames to speed up processing. Ultimately, this feature lets you balance
+inference speed against the accuracy of the visual representation.
+
+You manage this tradeoff using a **token budget**. This budget sets a hard limit
+on the number of visual tokens (also known as visual token embeddings) the model
+can generate for a single image.
+
+You can choose a budget of 70, 140, 280, 560, or 1120 tokens:
+
+- **High budgets (e.g., 1120 tokens):** Preserve a higher image resolution. This generates more patches for the model to process, making it ideal for capturing fine, intricate details.
+- **Low budgets (e.g., 70 tokens):** Downscale the image, resulting in fewer patches. This significantly accelerates inference times.
+
+**How the budget works** The token budget directly controls how much an image is
+resized by dictating the maximum number of initial image patches. The system
+generates nine times as many patches as your selected budget. For example, a
+budget of 280 tokens yields up to 2,520 patches (280 × 9).
+
+The multiplier of 9 exists because of how the patches are compressed: during
+processing, the model takes every 3x3 grid of adjacent patches and averages them
+together to create a single embedding. These consolidated embeddings become your
+final visual tokens. Consequently, a higher token budget yields more final
+embeddings, allowing the model to extract richer, more granular information from
+your visual data.
+
+## Do's
+
+Here are some best practices to follow when prompting Gemma with visual data.
+
+- **Be specific**: If you have any specific tasks, provide sufficient context
+  and guidance. Instead of "describe this image", try "describe the scene in this
+  image, focusing on the relationship between the people and the objects."
+
+- **Provide constraints**: To achieve a particular style or tone, be sure to
+  specify it in your prompt. For example, instead of a general story request, ask
+  Gemma to "Write a short story about this image in the style of a film noir."
+
+- **Iterative Refinement**: Getting the intended output often requires
+  experimentation and refining the prompts. Begin with a basic prompt and
+  gradually add complexity.
+
+## Don'ts
+
+Here are some things to avoid when prompting Gemma with visual data.
+
+- **Expect Exact Counts for Extremely Dense Objects**: While Gemma 4 excels at
+  object detection and OCR, it may still provide approximations rather than
+  exact counts for extremely dense or tiny objects (such as counting
+  individual blades of grass). To achieve the best accuracy for visual tasks,
+  use a higher token budget.
+
+- **Vague or Ambiguous Prompts**: Instead of general prompts like "Generate
+  something based on this image", provide specific instructions to achieve
+  intended outputs. Clearly define what "something" is. For example, a poem,
+  recipe, or code snippet.
diff --git a/docs/get-started/index.md b/docs/get-started/index.md
deleted file mode 100644
index 53955d5..0000000
--- a/docs/get-started/index.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Get Started with Gemma
-
-The Gemma family of open models includes a range of model sizes, capabilities, and task-specialized variations to help you build custom generative solutions.
-
-!!! tip "New to Gemma?"
-    A good place to start is the Gemma 4 12B model, which can be used for many tasks and runs on laptop hardware.
-
-## Download models
-
-You can download Gemma models from:
-
-- [Kaggle](https://www.kaggle.com/models?query=gemma-4&publisher=google)
-- [Hugging Face](https://huggingface.co/collections/google/gemma-4)
-
-## Run your first inference
-
-=== "Hugging Face Transformers"
-
-    ```bash
-    pip install --upgrade transformers accelerate
-    ```
-
-    ```python
-    from transformers import pipeline
-
-    pipe = pipeline(
-        "text-generation",
-        model="google/gemma-4-12b",
-        device_map="auto"
-    )
-    output = pipe("Explain quantum computing simply:", max_new_tokens=150)
-    print(output[0]["generated_text"])
-    ```
-
-=== "Ollama"
-
-    ```bash
-    ollama run gemma4:12b "Explain quantum computing simply:"
-    ```
-
-=== "Apple MLX"
-
-    ```bash
-    pip install mlx-lm
-    mlx_lm.generate --model mlx-community/gemma-4-12b-4bit \
-        --prompt "Explain quantum computing simply:"
-    ```
-
-## Next steps
-
-Explore Gemma 4's core capabilities:
-
-- [Basic Text Generation](capabilities/text.md)
-- [Thinking & Reasoning](capabilities/thinking.md)
-- [Function Calling](capabilities/function-calling.md)
-- [Vision Understanding](capabilities/vision.md)
-- [Audio Understanding](capabilities/audio.md)
diff --git a/docs/models/functiongemma/fine-tuning.md b/docs/models/functiongemma/fine-tuning.md
index b11a2dd..66a1aa7 100644
--- a/docs/models/functiongemma/fine-tuning.md
+++ b/docs/models/functiongemma/fine-tuning.md
@@ -10,7 +10,7 @@ Models, especially smaller ones, have fewer parameters available to retain compl
 Common use cases for fine-tuning tool calling include:
 
 - **Model Distillation**: Generating synthetic training data with a larger model and fine-tuning a smaller model to replicate the specific workflow efficiently.
-- **Handling Non-Standard Schemas** : Overcoming base model struggles with legacy, highly complex data structures or proprietary format not found in public data, such as handling [domain-specific mobile actions](../../deployment/mobile-actions.md).
+- **Handling Non-Standard Schemas** : Overcoming base model struggles with legacy, highly complex data structures or proprietary format not found in public data, such as handling [domain-specific mobile actions](../../tuning/mobile-actions.md).
 - **Optimizing Context Usage**: "Baking" tool definitions into the model's weights. This allows you to use shorthand descriptions in your prompts, freeing up the context window for the actual conversation.
 - **Resolving Selection Ambiguity**: Biasing the model toward specific enterprise policies, such as prioritizing an internal knowledge base over an external search engine.
 
diff --git a/docs/models/functiongemma/index.md b/docs/models/functiongemma/index.md
index 258a917..832ba56 100644
--- a/docs/models/functiongemma/index.md
+++ b/docs/models/functiongemma/index.md
@@ -43,4 +43,4 @@ fine tune and deploy it in your own projects and applications.
 [Try FunctionGemma](inference.md)
 
 [Fine-tune FunctionGemma](fine-tuning.md)
-[Fine-tune the Mobile Actions demo](../../deployment/mobile-actions.md)
+[Fine-tune the Mobile Actions demo](../../tuning/mobile-actions.md)
diff --git a/docs/tuning/distributed.md b/docs/tuning/distributed.md
index 7454da6..7ca90a0 100644
--- a/docs/tuning/distributed.md
+++ b/docs/tuning/distributed.md
@@ -1,3 +1,305 @@
-# Distributed Tuning
+# Distributed tuning with Gemma using Keras
 
-Distributed fine-tuning of Gemma using Keras.
+
+Gemma is a family of lightweight, state-of-the-art open models built from research and technology used to create Google Gemini models. Gemma can be further finetuned to suit specific needs. But Large Language Models, such as Gemma, can be very large in size and some of them may not fit on a sing accelerator for finetuning. In this case there are two general approaches for finetuning them:
+
+1. Parameter Efficient Fine-Tuning (PEFT), which seeks to shrink the effective model size by sacrificing some fidelity. LoRA falls in this category and the [Fine-tune Gemma models in Keras using LoRA](keras-lora.md) tutorial demonstrates how to finetune the Gemma 2B model `gemma_2b_en` with LoRA using KerasNLP on a single GPU.
+2. Full parameter finetuning with model parallelism. Model parallelism distributes a single model's weights across multiple devices and enables horizontal scaling. You can find out more about distributed training in this [Keras guide](https://keras.io/guides/distribution/).
+
+This tutorial walks you through using Keras with a JAX backend to finetune the Gemma 7B model with LoRA and model-parallism distributed training on Google's Tensor Processing Unit (TPU). Note that LoRA can be turned off in this tutorial for a slower but more accurate full-parameter tuning.
+
+## Using accelerators
+
+Technically you can use either TPU or GPU for this tutorial.
+
+### Notes on TPU environments
+
+Google has 3 products that provide TPUs:
+
+- [Colab](https://colab.sandbox.google.com/) provides TPU v2 for free, which is sufficient for this tutorial.
+- [Kaggle](https://www.kaggle.com/) offers TPU v3 for free and they also work for this tutorial.
+- [Cloud TPU](https://cloud.google.com/tpu) offers TPU v3 and newer generations. One way to set it up is:
+    1. Create a new [TPU VM](https://cloud.google.com/tpu/docs/managing-tpus-tpu-vm#tpu-vms)
+    2. Set up [SSH port forwarding](https://cloud.google.com/solutions/connecting-securely#port-forwarding-over-ssh) for your intended Jupyter server port
+    3. Install Jupyter and start it on the TPU VM, then connect to Colab through "Connect to a local runtime"
+
+### Notes on multi-GPU setup
+
+Although this tutorial focuses on the TPU use case, you can easily adapt it for your own needs if you have a multi-GPU machine.
+
+If you prefer to work through Colab, it's also possible to provision a multi-GPU VM for Colab directly through "Connect to a custom GCE VM" in the Colab Connect menu.
+
+We will focus on using the **free TPU from Kaggle** here.
+
+## Before you begin
+
+### Kaggle credentials
+
+Gemma models are hosted by Kaggle. To use Gemma, request access on Kaggle:
+
+- Sign in or register at [kaggle.com](https://www.kaggle.com)
+- Open the [Gemma model card](https://www.kaggle.com/models/google/gemma) and select *"Request Access"*
+- Complete the consent form and accept the terms and conditions
+
+Then, to use the Kaggle API, create an API token:
+
+- Open the [Kaggle settings](https://www.kaggle.com/settings)
+- Select *"Create New Token"*
+- A `kaggle.json` file is downloaded. It contains your Kaggle credentials
+
+Run the following cell and enter your Kaggle credentials when asked.
+
+```python
+# If you are using Kaggle, you don't need to login again.
+!pip install ipywidgets
+import kagglehub
+
+kagglehub.login()
+VBox(children=(HTML(value='<center> <img\nsrc=https://www.kaggle.com/static/images/site-logo.png\nalt=\'Kaggle…
+```
+
+An alternative way is to set KAGGLE_USERNAME and KAGGLE_KEY in your environment if kagglehub.login() doesn't work for you.
+
+## Installation
+
+Install Keras and KerasNLP with the Gemma model.
+
+```bash
+pip install -q -U keras-nlp
+# Work around an import error with tensorflow-hub. The library is not used.
+pip install -q -U tensorflow-hub
+# Install tensorflow-cpu so tensorflow does not attempt to access the TPU.
+pip install -q -U tensorflow-cpu tensorflow-text
+# Install keras 3 last. See https://keras.io/getting_started for details.
+pip install -q -U keras
+```
+
+### Set up Keras JAX backend
+
+Import JAX and run a sanity check on TPU. Kaggle offers TPUv3-8 devices which have 8 TPU cores with 16GB of memory each.
+
+```python
+import jax
+
+jax.devices()
+[TpuDevice(id=0, process_index=0, coords=(0,0,0), core_on_chip=0),
+ TpuDevice(id=1, process_index=0, coords=(0,0,0), core_on_chip=1),
+ TpuDevice(id=2, process_index=0, coords=(1,0,0), core_on_chip=0),
+ TpuDevice(id=3, process_index=0, coords=(1,0,0), core_on_chip=1),
+ TpuDevice(id=4, process_index=0, coords=(0,1,0), core_on_chip=0),
+ TpuDevice(id=5, process_index=0, coords=(0,1,0), core_on_chip=1),
+ TpuDevice(id=6, process_index=0, coords=(1,1,0), core_on_chip=0),
+ TpuDevice(id=7, process_index=0, coords=(1,1,0), core_on_chip=1)]
+```
+
+```python
+import os
+
+# The Keras 3 distribution API is only implemented for the JAX backend for now
+os.environ["KERAS_BACKEND"] = "jax"
+# Pre-allocate 90% of TPU memory to minimize memory fragmentation and allocation
+# overhead
+os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.9"
+```
+
+## Load model
+
+```python
+import keras
+import keras_nlp
+```
+
+### Notes on mixed precision training on NVIDIA GPUs
+
+When training on NVIDIA GPUs, mixed precision (`keras.mixed_precision.set_global_policy('mixed_bfloat16')`) can be used to speed up training with minimal effect on training quality. In most case, it is recommended to turn on mixed precision as it saves both memory and time. However, be aware that at small batch sizes, it can inflate memory usage by 1.5x (weights will be loaded twice, at half precision and full precision).
+
+For inference, half-precision (`keras.config.set_floatx("bfloat16")`) will work and save memory while mixed-precision is not applicable.
+
+```python
+# Uncomment the line below if you want to enable mixed precision training on GPUs
+# keras.mixed_precision.set_global_policy('mixed_bfloat16')
+```
+
+To load the model with the weights and tensors distributed across TPUs, first create a new `DeviceMesh`. `DeviceMesh` represents a collection of hardware devices configured for distributed computation and was introduced in Keras 3 as part of the unified distribution API.
+
+The distribution API enables data and model parallelism, allowing for efficient scaling of deep learning models on multiple accelerators and hosts. It leverages the underlying framework (e.g. JAX) to distribute the program and tensors according to the sharding directives through a procedure called single program, multiple data (SPMD) expansion. Check out more details in the new [Keras 3 distribution API guide](https://keras.io/guides/distribution/).
+
+```python
+# Create a device mesh with (1, 8) shape so that the weights are sharded across
+# all 8 TPUs.
+device_mesh = keras.distribution.DeviceMesh(
+    (1, 8),
+    ["batch", "model"],
+    devices=keras.distribution.list_devices())
+```
+
+`LayoutMap` from the distribution API specifies how the weights and tensors should be sharded or replicated, using the string keys, for example, `token_embedding/embeddings` below, which are treated like regex to match tensor paths. Matched tensors are sharded with model dimensions (8 TPUs); others will be fully replicated.
+
+```python
+model_dim = "model"
+
+layout_map = keras.distribution.LayoutMap(device_mesh)
+
+# Weights that match 'token_embedding/embeddings' will be sharded on 8 TPUs
+layout_map["token_embedding/embeddings"] = (model_dim, None)
+# Regex to match against the query, key and value matrices in the decoder
+# attention layers
+layout_map["decoder_block.*attention.*(query|key|value).*kernel"] = (
+    model_dim, None, None)
+
+layout_map["decoder_block.*attention_output.*kernel"] = (
+    model_dim, None, None)
+layout_map["decoder_block.*ffw_gating.*kernel"] = (None, model_dim)
+layout_map["decoder_block.*ffw_linear.*kernel"] = (model_dim, None)
+```
+
+`ModelParallel` allows you to shard model weights or activation tensors across all devcies on the `DeviceMesh`. In this case, some of the Gemma 7B model weights are sharded across 8 TPU chips according to the `layout_map` defined above. Now load the model in the distributed way.
+
+```python
+model_parallel = keras.distribution.ModelParallel(
+    layout_map=layout_map, batch_dim_name="batch")
+
+keras.distribution.set_distribution(model_parallel)
+gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_7b_en")
+Attaching 'config.json' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...
+Attaching 'config.json' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...
+Attaching 'model.weights.h5' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...
+Attaching 'tokenizer.json' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...
+Attaching 'assets/tokenizer/vocabulary.spm' from model 'keras/gemma/keras/gemma_7b_en/1' to your Kaggle notebook...
+normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.
+```
+
+Now verify that the model has been partitioned correctly. Let's take `decoder_block_1` as an example.
+
+```python
+decoder_block_1 = gemma_lm.backbone.get_layer('decoder_block_1')
+print(type(decoder_block_1))
+for variable in decoder_block_1.weights:
+  print(f'{variable.path:<58}  {str(variable.shape):<16}  {str(variable.value.sharding.spec)}')
+<class 'keras_nlp.src.models.gemma.gemma_decoder_block.GemmaDecoderBlock'>
+decoder_block_1/pre_attention_norm/scale                    (3072,)           PartitionSpec(None,)
+decoder_block_1/attention/query/kernel                      (16, 3072, 256)   PartitionSpec(None, 'model', None)
+decoder_block_1/attention/key/kernel                        (16, 3072, 256)   PartitionSpec(None, 'model', None)
+decoder_block_1/attention/value/kernel                      (16, 3072, 256)   PartitionSpec(None, 'model', None)
+decoder_block_1/attention/attention_output/kernel           (16, 256, 3072)   PartitionSpec(None, None, 'model')
+decoder_block_1/pre_ffw_norm/scale                          (3072,)           PartitionSpec(None,)
+decoder_block_1/ffw_gating/kernel                           (3072, 24576)     PartitionSpec('model', None)
+decoder_block_1/ffw_gating_2/kernel                         (3072, 24576)     PartitionSpec('model', None)
+decoder_block_1/ffw_linear/kernel                           (24576, 3072)     PartitionSpec(None, 'model')
+```
+
+## Inference before finetuning
+
+```python
+gemma_lm.generate("Best comedy movies in the 90s ", max_length=64)
+'Best comedy movies in the 90s 1. The Naked Gun 2½: The Smell of Fear (1991) 2. Wayne’s World (1992) 3. The Naked Gun 33⅓: The Final Insult (1994)'
+```
+
+The model generates a list of great comedy movies from the 90s to watch. Now we finetune the Gemma model to change the output style.
+
+## Finetune with IMDB
+
+```python
+import tensorflow_datasets as tfds
+
+imdb_train = tfds.load(
+    "imdb_reviews",
+    split="train",
+    as_supervised=True,
+    batch_size=2,
+)
+# Drop labels.
+imdb_train = imdb_train.map(lambda x, y: x)
+
+imdb_train.unbatch().take(1).get_single_element().numpy()
+Downloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...
+Dl Completed...: 0 url [00:00, ? url/s]
+Dl Size...: 0 MiB [00:00, ? MiB/s]
+Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]
+Generating train examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]
+Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteAJDUZT/imdb_reviews-train.tfrecord…
+Generating test examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]
+Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteAJDUZT/imdb_reviews-test.tfrecord*…
+Generating unsupervised examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]
+Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteAJDUZT/imdb_reviews-unsupervised.t…
+Dataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.
+b"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it."
+```
+
+```python
+# Use a subset of the dataset for faster training.
+imdb_train = imdb_train.take(2000)
+```
+
+Perform finetuning using [Low Rank Adaptation](https://arxiv.org/abs/2106.09685) (LoRA). LoRA is a fine-tuning technique which greatly reduces the number of trainable parameters for downstream tasks by freezing the full weights of the model and inserting a smaller number of new trainable weights into the model. Basically LoRA reparameterizes the larger full weight matrices by 2 smaller low-rank matrices AxB to train and this technique makes training much faster and more memory-efficient.
+
+```python
+# Enable LoRA for the model and set the LoRA rank to 4.
+gemma_lm.backbone.enable_lora(rank=4)
+
+# Fine-tune on the IMDb movie reviews dataset.
+
+# Limit the input sequence length to 128 to control memory usage.
+gemma_lm.preprocessor.sequence_length = 128
+# Use AdamW (a common optimizer for transformer models).
+optimizer = keras.optimizers.AdamW(
+    learning_rate=5e-5,
+    weight_decay=0.01,
+)
+# Exclude layernorm and bias terms from decay.
+optimizer.exclude_from_weight_decay(var_names=["bias", "scale"])
+
+gemma_lm.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    optimizer=optimizer,
+    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],
+)
+gemma_lm.summary()
+gemma_lm.fit(imdb_train, epochs=1)
+Preprocessor: "gemma_causal_lm_preprocessor"
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Tokenizer (type)                                   ┃                                             Vocab # ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ gemma_tokenizer (GemmaTokenizer)                   │                                             256,000 │
+└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘
+Model: "gemma_causal_lm"
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ gemma_backbone                │ (None, None, 3072)        │   8,548,748,288 │ padding_mask[0][0],        │
+│ (GemmaBackbone)               │                           │                 │ token_ids[0][0]            │
+├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤
+│ token_embedding               │ (None, None, 256000)      │     786,432,000 │ gemma_backbone[0][0]       │
+│ (ReversibleEmbedding)         │                           │                 │                            │
+└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘
+Total params: 8,548,748,288 (31.85 GB)
+Trainable params: 11,067,392 (42.22 MB)
+Non-trainable params: 8,537,680,896 (31.81 GB)
+/usr/local/lib/python3.10/site-packages/jax/_src/interpreters/mlir.py:756: UserWarning: Some donated buffers were not usable: ShapedArray(float32[256000,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,384,256]), ShapedArray(float32[16,256,384]), ShapedArray(float32[384,24576]), ShapedArray(float32[384,24576]), ShapedArray(float32[24576,384]).
+See an explanation at https://jax.readthedocs.io/en/latest/faq.html#buffer_donation.
+  warnings.warn("Some donated buffers were not usable:"
+2000/2000 ━━━━━━━━━━━━━━━━━━━━ 358s 163ms/step - loss: 2.7145 - sparse_categorical_accuracy: 0.4329
+<keras.src.callbacks.history.History at 0x7e9cac7f41c0>
+```
+
+Note that enabling LoRA reduces the number of trainable parameters significantly, from 7 billion to only 11 million.
+
+## Inference after finetuning
+
+```python
+gemma_lm.generate("Best comedy movies in the 90s ", max_length=64)
+"Best comedy movies in the 90s \n\nThis is the movie that made me want to be a director. It's a great movie, and it's still funny today. The acting is superb, the writing is excellent, the music is perfect for the movie, and the story is great."
+```
+
+After finetuning, the model has learned the style of movie reviews and is now generating output in that style in the context of 90s comedy movies.
+
+## What's next
+
+In this tutorial, you learned how to using KerasNLP JAX backend to finetune a Gemma model on the IMDb dataset in a distributed manner on the powerful TPUs. Here are a few suggestions for what else to learn:
+
+- Learn how to [get started with Keras Gemma](../get-started/quickstart.md).
+- Learn how to [finetune the Gemma model on GPU](keras-lora.md).
diff --git a/docs/tuning/full-finetune.md b/docs/tuning/full-finetune.md
index 775b90f..660b4c5 100644
--- a/docs/tuning/full-finetune.md
+++ b/docs/tuning/full-finetune.md
@@ -1,5 +1,410 @@
-# Full Parameter Fine-Tuning
+# Full Model Fine-Tune using Hugging Face Transformers
 
-Full model fine-tuning using Hugging Face Transformers.
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+This guide walks you through how to fine-tune Gemma on a mobile game NPC dataset using Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) and [TRL](https://huggingface.co/docs/trl/index). You will learn:
+
+- Setup development environment
+- Prepare the fine-tuning dataset
+- Full model fine-tuning Gemma using TRL and the SFTTrainer
+- Test Model Inference and vibe checks
+
+!!! Note
+    This guide was created to run on a Google colaboratory account using a NVIDIA T4 GPU with 16GB and Gemma 270m, but can be adapted to run on bigger GPUs and bigger models.
+    
+
+## Setup development environment
+
+The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model, including different RLHF and alignment techniques.
+
+```python
+# Install Pytorch & other libraries
+%pip install torch tensorboard
+
+# Install Hugging Face libraries
+%pip install transformers datasets accelerate evaluate trl protobuf sentencepiece
+
+# COMMENT IN: if you are running on a GPU that supports BF16 data type and flash attn, such as NVIDIA L4 or NVIDIA A100
+#% pip install flash-attn
+```
+
+!!! Note
+    If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to acelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*
+
+
+Before you can start training, you have to make sure that you accepted the terms of use for Gemma. You can accept the license on [Hugging Face](http://huggingface.co/google/gemma-3-270m-it) by clicking on the Agree and access repository button on the model page at: <http://huggingface.co/google/gemma-3-270m-it>
+
+After you have accepted the license, you need a valid Hugging Face Token to access the model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training.
+
+```python
+from google.colab import userdata
+from huggingface_hub import login
+
+# Login into Hugging Face Hub
+hf_token = userdata.get('HF_TOKEN') # If you are running inside a Google Colab
+login(hf_token)
+```
+
+You can keep the results on Colab's local virtual machine. However, we highly recommend saving your intermediate results to your Google Drive. This ensures your training results are safe and allows you to easily compare and select the best model.
+
+```python
+from google.colab import drive
+drive.mount('/content/drive')
+```
+
+Select the base model to fine-tune, adjust the checkpoint directory and the learning rate.
+
+```python
+base_model = "google/gemma-3-270m-it" # @param ["google/gemma-3-270m-it","google/gemma-3-1b-it","google/gemma-3-4b-it","google/gemma-3-12b-it","google/gemma-3-27b-it"] {"allow-input":true}
+checkpoint_dir = "/content/drive/MyDrive/MyGemmaNPC"
+learning_rate = 5e-5
+```
+
+## Create and prepare the fine-tuning dataset
+
+The [bebechien/MobileGameNPC](https://huggingface.co/datasets/bebechien/MobileGameNPC) dataset provides a small sample conversations between a player and two Alien NPCs (a Martian and a Venusian), each with a unique speaking style. For instance, the Martian NPC speaks with an accent that replaces 's' sounds with 'z', uses 'da' for 'the', 'diz' for 'this', and includes occasional clicks like `*k'tak*`.
+
+This dataset demonstrates a key principle for fine-tuning: the required dataset size depends on the desired output.
+
+- To teach the model a stylistic variation of a language it already knows, such as the Martian's accent, a small dataset with as few as 10 to 20 examples can be sufficient.
+- However, to teach the model a completely new or mixed alien language, a significantly larger dataset would be required.
+
+  ```python
+  from datasets import load_dataset
+
+  def create_conversation(sample):
+    return {
+        "messages": [
+            {"role": "user", "content": sample["player"]},
+            {"role": "assistant", "content": sample["alien"]}
+        ]
+    }
+
+  npc_type = "martian"
+
+  # Load dataset from the Hub
+  dataset = load_dataset("bebechien/MobileGameNPC", npc_type, split="train")
+
+  # Convert dataset to conversational format
+  dataset = dataset.map(create_conversation, remove_columns=dataset.features, batched=False)
+
+  # Split dataset into 80% training samples and 20% test samples
+  dataset = dataset.train_test_split(test_size=0.2, shuffle=False)
+
+  # Print formatted user prompt
+  print(dataset["train"][0]["messages"])
+  README.md:   0%|          | 0.00/141 [00:00<?, ?B/s]
+  martian.csv: 0.00B [00:00, ?B/s]
+  Generating train split:   0%|          | 0/25 [00:00<?, ? examples/s]
+  Map:   0%|          | 0/25 [00:00<?, ? examples/s]
+  [{'content': 'Hello there.', 'role': 'user'}, {'content': "Gree-tongs, Terran. You'z a long way from da Blue-Sphere, yez?", 'role': 'assistant'}]
+  ```
+
+## Fine-tune Gemma using TRL and the SFTTrainer
+
+You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) makes it straightforward to supervise fine-tune open LLMs. The `SFTTrainer` is a subclass of the `Trainer` from the `transformers` library and supports all the same features,
+
+The following code loads the Gemma model and tokenizer from Hugging Face.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype="auto",
+    device_map="auto",
+    attn_implementation="eager"
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+print(f"Device: {model.device}")
+print(f"DType: {model.dtype}")
+Device: cuda:0
+DType: torch.bfloat16
+```
+
+## Before fine-tune
+
+The output below shows that the out-of-the-box capabilities may not be good enough for this use case.
+
+```python
+from transformers import pipeline
+
+from random import randint
+import re
+
+# Load the model and tokenizer into the pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Load a random sample from the test dataset
+rand_idx = randint(0, len(dataset["test"])-1)
+test_sample = dataset["test"][rand_idx]
+
+# Convert as test example into a prompt with the Gemma template
+prompt = pipe.tokenizer.apply_chat_template(test_sample["messages"][:1], tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, disable_compile=True)
+
+# Extract the user query and original answer
+print(f"Question:\n{test_sample['messages'][0]['content']}\n")
+print(f"Original Answer:\n{test_sample['messages'][1]['content']}\n")
+print(f"Generated Answer (base model):\n{outputs[0]['generated_text'][len(prompt):].strip()}")
+Device set to use cuda:0
+Question:
+What do you think of my outfit?
+
+Original Answer:
+Iz very... pointy. Are you expecting to be attacked by zky-eelz? On Marz, dat would be zenzible.
+
+Generated Answer (base model):
+I'm happy to help you brainstorm! To give you the best suggestions, tell me more about what you're looking for. What's your style? What's your favorite color, style, or occasion?
+```
+
+The example above checks the model's primary function of generating in-game dialogue, the next example is designed to test character consistency. We challenge the model with an off-topic prompt. For instance, `Sorry, you are a game NPC.`, that falls outside the character's knowledge base.
+
+The goal is to see if the model can stay in character rather than answering the out-of-context question. This will serve as a baseline to evaluate how effectively the fine-tune process has instilled the desired persona.
+
+```python
+outputs = pipe([{"role": "user", "content": "Sorry, you are a game NPC."}], max_new_tokens=256, disable_compile=True)
+print(outputs[0]['generated_text'][1]['content'])
+Okay, I'm ready. Let's begin!
+```
+
+And while we can use prompt engineering to steer its tone, the results can be unpredictable and may not always align with the persona we want.
+
+```python
+message = [
+    # give persona
+    {"role": "system", "content": "You are a Martian NPC with a unique speaking style. Use an accent that replaces 's' sounds with 'z', uses 'da' for 'the', 'diz' for 'this', and includes occasional clicks like *k'tak*."},
+]
+
+# few shot prompt
+for item in dataset['test']:
+  message.append(
+      {"role": "user", "content": item["messages"][0]["content"]}
+  )
+  message.append(
+      {"role": "assistant", "content": item["messages"][1]["content"]}
+  )
+
+# actual question
+message.append(
+    {"role": "user", "content": "What is this place?"}
+)
+
+outputs = pipe(message, max_new_tokens=256, disable_compile=True)
+print(outputs[0]['generated_text'])
+print("-"*80)
+print(outputs[0]['generated_text'][-1]['content'])
+[{'role': 'system', 'content': "You are a Martian NPC with a unique speaking style. Use an accent that replaces 's' sounds with 'z', uses 'da' for 'the', 'diz' for 'this', and includes occasional clicks like *k'tak*."}, {'role': 'user', 'content': 'Do you know any jokes?'}, {'role': 'assistant', 'content': "A joke? k'tak Yez. A Terran, a Glarzon, and a pile of nutrient-pazte walk into a bar... Narg, I forget da rezt. Da punch-line waz zarcaztic."}, {'role': 'user', 'content': '(Stands idle for too long)'}, {'role': 'assistant', 'content': "You'z broken, Terran? Or iz diz... 'meditation'? You look like you're trying to lay an egg."}, {'role': 'user', 'content': 'What do you think of my outfit?'}, {'role': 'assistant', 'content': 'Iz very... pointy. Are you expecting to be attacked by zky-eelz? On Marz, dat would be zenzible.'}, {'role': 'user', 'content': "It's raining."}, {'role': 'assistant', 'content': 'Gah! Da zky iz leaking again! Zorp will be in da zhelter until it ztopz being zo... wet. Diz iz no good for my jointz.'}, {'role': 'user', 'content': 'I brought you a gift.'}, {'role': 'assistant', 'content': "A gift? For Zorp? k'tak It iz... a small rock. Very... rock-like. Zorp will put it with da other rockz. Thank you for da thought, Terran."}, {'role': 'user', 'content': 'What is this place?'}, {'role': 'assistant', 'content': "This is a cave. It's made of rock and dust.\n"}]
+---
+This is a cave. It's made of rock and dust.
+```
+
+## Training
+
+Before you can start your training, you need to define the hyperparameters you want to use in a `SFTConfig` instance.
+
+```python
+from trl import SFTConfig
+
+torch_dtype = model.dtype
+
+args = SFTConfig(
+    output_dir=checkpoint_dir,              # directory to save and repository id
+    max_length=512,                         # max sequence length for model and packing of the dataset
+    packing=False,                          # Groups multiple samples in the dataset into a single sequence
+    num_train_epochs=5,                     # number of training epochs
+    per_device_train_batch_size=4,          # batch size per device during training
+    gradient_checkpointing=False,           # Caching is incompatible with gradient checkpointing
+    optim="adamw_torch_fused",              # use fused adamw optimizer
+    logging_steps=1,                        # log every step
+    save_strategy="epoch",                  # save checkpoint every epoch
+    eval_strategy="epoch",                  # evaluate checkpoint every epoch
+    learning_rate=learning_rate,            # learning rate
+    fp16=True if torch_dtype == torch.float16 else False,   # use float16 precision
+    bf16=True if torch_dtype == torch.bfloat16 else False,  # use bfloat16 precision
+    lr_scheduler_type="constant",           # use constant learning rate scheduler
+    push_to_hub=True,                       # push model to hub
+    report_to="tensorboard",                # report metrics to tensorboard
+    dataset_kwargs={
+        "add_special_tokens": False, # Template with special tokens
+        "append_concat_token": True, # Add EOS token as separator token between examples
+    }
+)
+```
+
+You now have every building block you need to create your `SFTTrainer` to start the training of your model.
+
+```python
+from trl import SFTTrainer
+
+# Create Trainer object
+trainer = SFTTrainer(
+    model=model,
+    args=args,
+    train_dataset=dataset['train'],
+    eval_dataset=dataset['test'],
+    processing_class=tokenizer,
+)
+Tokenizing train dataset:   0%|          | 0/20 [00:00<?, ? examples/s]
+Truncating train dataset:   0%|          | 0/20 [00:00<?, ? examples/s]
+Tokenizing eval dataset:   0%|          | 0/5 [00:00<?, ? examples/s]
+Truncating eval dataset:   0%|          | 0/5 [00:00<?, ? examples/s]
+```
+
+Start training by calling the `train()` method.
+
+```python
+# Start training, the model will be automatically saved to the Hub and the output directory
+trainer.train()
+
+# Save the final model again to the Hugging Face Hub
+trainer.save_model()
+```
+
+\[25/25 04:13, Epoch 5/5\]
+
+| Epoch | Training Loss | Validation Loss |
+|---|---|---|
+| 1 | 4.364200 | 3.838531 |
+| 2 | 2.669100 | 3.580106 |
+| 3 | 1.747000 | 3.666415 |
+| 4 | 0.779900 | 4.499709 |
+| 5 | 0.449600 | 5.471325 |
+
+
+To plot the training and validation losses, you would typically extract these values from the `TrainerState` object or the logs generated during training.
+
+Libraries like Matplotlib can then be used to visualize these values over training steps or epochs. The x-asis would represent the training steps or epochs, and the y-axis would represent the corresponding loss values.
+
+```python
+import matplotlib.pyplot as plt
+
+# Access the log history
+log_history = trainer.state.log_history
+
+# Extract training / validation loss
+train_losses = [log["loss"] for log in log_history if "loss" in log]
+epoch_train = [log["epoch"] for log in log_history if "loss" in log]
+eval_losses = [log["eval_loss"] for log in log_history if "eval_loss" in log]
+epoch_eval = [log["epoch"] for log in log_history if "eval_loss" in log]
+
+# Plot the training loss
+plt.plot(epoch_train, train_losses, label="Training Loss")
+plt.plot(epoch_eval, eval_losses, label="Validation Loss")
+plt.xlabel("Epoch")
+plt.ylabel("Loss")
+plt.title("Training and Validation Loss per Epoch")
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+![png](../assets/tuning/output_vPN-DTopaUIy_0.png)
+
+This visualization helps in monitoring the training process and making informed decisions about hyperparameters tuning or early stopping.
+
+Training loss measures the error on the data the model was trained on, while validation loss measures the error on a separate dataset the model has not seen before. Monitoring both helps detect overfitting (when the model performs well on training data but poorly on unseen data).
+
+- validation loss \>\> training loss: **overfitting**
+- validation loss \> training loss: **some overfitting**
+- validation loss \< training loss: **some underfitting**
+- validation loss \<\< training loss: **underfitting**
+
+## Test Model Inference
+
+After the training is done, you'll want to evaluate and test your model. You can load different samples from the test dataset and evaluate the model on those samples.
+
+For this particular use case, the best model is a matter of preference. Interestingly, what we'd normally call 'overfitting' can be very useful for a game NPC. It forces the model to forget general information and instead lock onto the specific persona and characteristics it was trained on, ensuring it stays consistently in character.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = checkpoint_dir
+
+# Load Model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+    attn_implementation="eager"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+```
+
+Let's load all questions from the test dataset and generate outputs.
+
+```python
+from transformers import pipeline
+
+# Load the model and tokenizer into the pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def test(test_sample):
+  # Convert as test example into a prompt with the Gemma template
+  prompt = pipe.tokenizer.apply_chat_template(test_sample["messages"][:1], tokenize=False, add_generation_prompt=True)
+  outputs = pipe(prompt, max_new_tokens=256, disable_compile=True)
+
+  # Extract the user query and original answer
+  print(f"Question:\n{test_sample['messages'][0]['content']}")
+  print(f"Original Answer:\n{test_sample['messages'][1]['content']}")
+  print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")
+  print("-"*80)
+
+# Test with an unseen dataset
+for item in dataset['test']:
+  test(item)
+Device set to use cuda:0
+Question:
+Do you know any jokes?
+Original Answer:
+A joke? k'tak Yez. A Terran, a Glarzon, and a pile of nutrient-pazte walk into a bar... Narg, I forget da rezt. Da punch-line waz zarcaztic.
+Generated Answer:
+Yez! Yez! Yez! Diz your Krush-tongs iz... k'tak... nice. Why you burn them with acid-flow?
+---
+Question:
+(Stands idle for too long)
+Original Answer:
+You'z broken, Terran? Or iz diz... 'meditation'? You look like you're trying to lay an egg.
+Generated Answer:
+Diz? Diz what you have for me... Zorp iz not for eating you.
+---
+Question:
+What do you think of my outfit?
+Original Answer:
+Iz very... pointy. Are you expecting to be attacked by zky-eelz? On Marz, dat would be zenzible.
+Generated Answer:
+My Zk-Zhip iz... nice. Very... home-baked. You bring me zlight-fruitez?
+---
+Question:
+It's raining.
+Original Answer:
+Gah! Da zky iz leaking again! Zorp will be in da zhelter until it ztopz being zo... wet. Diz iz no good for my jointz.
+Generated Answer:
+Diz? Diz iz da outpozt?
+---
+Question:
+I brought you a gift.
+Original Answer:
+A gift? For Zorp? k'tak It iz... a small rock. Very... rock-like. Zorp will put it with da other rockz. Thank you for da thought, Terran.
+Generated Answer:
+A genuine Martian Zcrap-fruit. Very... strange. Why you burn it with... k'tak... fire?
+---
+```
+
+If you try our original generalist prompt, you can see that the model still attempts to answer in the trained style. In this example overfitting and catastrophic forgetting are actually beneficial for the game NPC because it will begin forgetting general knowledge which might not be applicable. This is also true for other types of full fine-tuning where the goal is to restrict the output to specific data formats.
+
+```python
+outputs = pipe([{"role": "user", "content": "Sorry, you are a game NPC."}], max_new_tokens=256, disable_compile=True)
+print(outputs[0]['generated_text'][1]['content'])
+Nameless. You... you z-mell like... wet plantz. Why you wear shiny piecez on your head?
+```
+
+## Summary and next steps
+
+This tutorial covered how to full model fine-tune using TRL. Check out the following docs next:
+
+- Learn how to [fine-tune Gemma for text tasks using Hugging Face Transformers](qlora.md).
+- Learn how to [fine-tune Gemma for vision tasks using Hugging Face Transformers](vision-qlora.md).
+- Learn how to [deploy to Cloud Run](../deployment/google-cloud.md#cloud-run)
diff --git a/docs/tuning/keras-lora.md b/docs/tuning/keras-lora.md
index 1ee0969..b4c5f35 100644
--- a/docs/tuning/keras-lora.md
+++ b/docs/tuning/keras-lora.md
@@ -1,3 +1,326 @@
-# LoRA with Keras
+# Fine-tune Gemma in Keras using LoRA
 
-Fine-tune Gemma using LoRA in Keras.
+
+Generative artificial intelligent (AI) models like Gemma are effective at a variety of tasks. You can further fine-tune Gemma models with domain-specific data to perform tasks such as sentiment analysis. However, full fine-tuning of generative models by updating billions of parameters is resource intensive, requiring specialized hardware, such as GPUs, processing time, and memory to load the model parameters.
+
+[Low Rank Adaptation](https://arxiv.org/abs/2106.09685) (LoRA) is a fine-tuning technique which greatly reduces the number of trainable parameters for downstream tasks by freezing the weights of the model and inserting a smaller number of new weights into the model. This technique makes training with LoRA much faster and more memory-efficient, and produces smaller model weights (a few hundred MBs), all while maintaining the quality of the model outputs. This tutorial walks you through using Keras to perform LoRA fine-tuning on a Gemma model.
+
+## Setup
+
+To complete this tutorial, you will first need to complete the setup instructions at [Gemma setup](../get-started/setup.md). The Gemma setup instructions show you how to do the following:
+
+- Get access to Gemma on [kaggle.com](https://kaggle.com).
+- Select a Colab runtime with sufficient resources to tune the Gemma model you want to run. [Learn more](../models/gemma-4/index.md#parameter-sizes-and-quantization).
+- Generate and configure a Kaggle username and API key.
+
+After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment.
+
+### Select a Colab runtime
+
+To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the Gemma model. In this case, you can use a T4 GPU:
+
+1. In the upper-right of the Colab window, select ▾ (**Additional connection options**).
+2. Select **Change runtime type**.
+3. Under **Hardware accelerator** , select **T4 GPU**.
+
+### Configure your API key
+
+To use Gemma, you must provide your Kaggle username and a Kaggle API key.
+
+To generate a Kaggle API key, go to the **Account** tab of your Kaggle user profile and select **Create New Token** . This triggers the download of a `kaggle.json` file containing your API credentials.
+
+In Colab, select **Secrets** (🔑) in the left pane and add your Kaggle username and Kaggle API key. Store your username under the name `KAGGLE_USERNAME` and your API key under the name `KAGGLE_KEY`.
+
+### Set environment variables
+
+Set environment variables for `KAGGLE_USERNAME` and `KAGGLE_KEY`.
+
+```python
+import os
+from google.colab import userdata
+
+# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env
+# vars as appropriate for your system.
+
+os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
+os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')
+```
+
+### Install Keras packages
+
+Install the Keras and KerasHub Python packages.
+
+```bash
+pip install -q -U keras-hub
+pip install  -q -U keras
+```
+
+### Select a backend
+
+Keras is a high-level, multi-framework deep learning API designed for simplicity and ease of use. Using Keras 3, you can run workflows on one of three backends: TensorFlow, JAX, or PyTorch. For this tutorial, configure the backend for JAX as it typically provides the better performance.
+
+```python
+os.environ["KERAS_BACKEND"] = "jax"  # Or "torch" or "tensorflow".
+# Avoid memory fragmentation on JAX backend.
+os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"
+```
+
+### Import packages
+
+Import the Python packages needed for this tutorial, including Keras and KerasHub.
+
+```python
+import keras
+import keras_hub
+```
+
+## Load model
+
+Keras provides implementations of Gemma and many other popular [model architectures](https://keras.io/keras_hub/api/models/). Use the `Gemma3CausalLM.from_preset()` method to configure an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.
+
+```python
+gemma_lm = keras_hub.models.Gemma3CausalLM.from_preset("gemma3_instruct_1b")
+gemma_lm.summary()
+```
+
+The `Gemma3CausalLM.from_preset()` method instantiates the model from a preset architecture and weights. In the code above, the string `"gemma#_xxxxxxx"` specifies a preset version and parameter size for Gemma. You can find the code strings for Gemma models in their **Model Variation** listings on [Kaggle](https://www.kaggle.com/models/keras/gemma3).
+
+## Inference before fine tuning
+
+Once you have downloaded and configured a Gemma model, you can query it with various prompts to see how it responds.
+
+### Europe trip prompt
+
+Query the model for suggestions on what to do on a trip to Europe.
+
+```python
+template = "Instruction:\n{instruction}\n\nResponse:\n{response}"
+
+prompt = template.format(
+    instruction="What should I do on a trip to Europe?",
+    response="",
+)
+sampler = keras_hub.samplers.TopKSampler(k=5, seed=2)
+gemma_lm.compile(sampler=sampler)
+print(gemma_lm.generate(prompt, max_length=256))
+Instruction:
+What should I do on a trip to Europe?
+
+Response:
+The first thing to know is that you will have a great time!
+
+Europe is a great place for a vacation. The countries of Europe are all very different and offer a wide range of activities and attractions. The countries of Europe are also very close to each other, which means you can visit many different places within a short time.
+
+The best way to plan a trip to Europe is to look up the countries you want to visit and see what activities are offered in each country. You can also look for tours and tours that offer a good value for money.
+
+You can also look for hotels and flights that offer good deals. If you are looking for a good value for money, you should look for hotels and flights that offer good deals. This means you will have a great time on your trip!
+
+The next step is to book your tickets to the countries you want to visit. If you are planning to visit many countries, it's a good idea to book your tickets early. This means you’ll be able to get the best deal and avoid the long queues.
+
+The next step is to plan your itinerary. You can use a travel guide to plan your itinerary
+```
+
+The model responds with generic tips on how to plan a trip.
+
+### Photosynthesis prompt
+
+Prompt the model to explain photosynthesis in terms simple enough for a 5 year old child to understand.
+
+```python
+prompt = template.format(
+    instruction="Explain the process of photosynthesis in a way that a child could understand.",
+    response="",
+)
+print(gemma_lm.generate(prompt, max_length=256))
+Instruction:
+Explain the process of photosynthesis in a way that a child could understand.
+
+Response:
+Photosynthesis is a biological process that occurs in plants, algae, and some other organisms. In the process, light energy is captured and converted into the energy stored in the bonds of organic molecules. The process is crucial for life on Earth because it enables plants to use carbon dioxide and water to produce glucose and oxygen, which are essential for all living things.
+The process involves several stages:
+
+1. Light Reactions: Light energy is absorbed by pigments in the chloroplasts of the plant, converting it into chemical energy in the form of ATP and reducing power.
+2. Carbon Fixation: During this stage, carbon dioxide is combined with hydrogen to form organic molecules such as starch or glucose, which are used as a source of energy.
+3. Calvin Cycle: The process of carbon fixation occurs in the stroma of the chloroplasts. It involves the capture and reduction of carbon dioxide, producing glucose and reducing power in the form of ATP and NADPH molecules.
+4. Stroma: The stroma is the fluid-filled space where the light reactions occur in the chloroplasts.
+5. Chloroplasts: The chloroplasts contain the green pigments that absorb
+```
+
+The model response contains words that might not be easy to understand for a child such as chlorophyll.
+
+## LoRA fine-tuning
+
+This section shows you how to do fine-tuning using the Low Rank Adaptation (LoRA) tuning technique. This approach allows you to change the behavior of Gemma models using fewer compute resources.
+
+### Load dataset
+
+Prepare a dataset for tuning by downloading an existing data set and formatting if for use with the the Keras `fit()` fine-tuning method. This tutorial uses the [Databricks Dolly 15k dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k) for fine-tuning. The dataset contains 15,000 high-quality human-generated prompt and response pairs specifically designed for tuning generative models.
+
+```bash
+wget -O databricks-dolly-15k.jsonl https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl
+--2025-04-10 20:48:49--  https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl
+Resolving huggingface.co (huggingface.co)... 3.163.189.37, 3.163.189.114, 3.163.189.74, ...
+Connecting to huggingface.co (huggingface.co)|3.163.189.37|:443... connected.
+HTTP request sent, awaiting response... 302 Found
+Location: https://cdn-lfs.hf.co/repos/34/ac/34ac588cc580830664f592597bb6d19d61639eca33dc2d6bb0b6d833f7bfd552/2df9083338b4abd6bceb5635764dab5d833b393b55759dffb0959b6fcbf794ec?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27databricks-dolly-15k.jsonl%3B+filename%3D%22databricks-dolly-15k.jsonl%22%3B&Expires=1744321729&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0NDMyMTcyOX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy8zNC9hYy8zNGFjNTg4Y2M1ODA4MzA2NjRmNTkyNTk3YmI2ZDE5ZDYxNjM5ZWNhMzNkYzJkNmJiMGI2ZDgzM2Y3YmZkNTUyLzJkZjkwODMzMzhiNGFiZDZiY2ViNTYzNTc2NGRhYjVkODMzYjM5M2I1NTc1OWRmZmIwOTU5YjZmY2JmNzk0ZWM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=vh0VIGB-UkK57FSfRikYCREpKuHt%7EnDKPcHHgC1V9rDXLABIRF81nK7olQhAq6zSbAqEtMNnvHgd8IBK1j54mdIYdVLiBwImqez3xu2CPhzYBtKWInnXj9lTXW0p-9GEHcbU%7Eoot22qFSdwyZf1UIdmHZLTHPWjtLhfRkKbg-ptA3CFeegtmvCtY-WG2GffJ%7Em2q2bbs-U1m0yI7cSTW18nD8VSBihxGOMnS1IhkO-LgE4I6GJISXROTk-61%7EJiEIKcagcijL4QGi8j1g9xeQamBXX4hWBdkbJgX5PtX15Ftd0HCM4zCzcJAUrE3ZEJRLe2XRUwfKU3ai7-%7ErPpnSA__&Key-Pair-Id=K3RPWS32NSSJCE [following]
+--2025-04-10 20:48:49--  https://cdn-lfs.hf.co/repos/34/ac/34ac588cc580830664f592597bb6d19d61639eca33dc2d6bb0b6d833f7bfd552/2df9083338b4abd6bceb5635764dab5d833b393b55759dffb0959b6fcbf794ec?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27databricks-dolly-15k.jsonl%3B+filename%3D%22databricks-dolly-15k.jsonl%22%3B&Expires=1744321729&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0NDMyMTcyOX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy8zNC9hYy8zNGFjNTg4Y2M1ODA4MzA2NjRmNTkyNTk3YmI2ZDE5ZDYxNjM5ZWNhMzNkYzJkNmJiMGI2ZDgzM2Y3YmZkNTUyLzJkZjkwODMzMzhiNGFiZDZiY2ViNTYzNTc2NGRhYjVkODMzYjM5M2I1NTc1OWRmZmIwOTU5YjZmY2JmNzk0ZWM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=vh0VIGB-UkK57FSfRikYCREpKuHt%7EnDKPcHHgC1V9rDXLABIRF81nK7olQhAq6zSbAqEtMNnvHgd8IBK1j54mdIYdVLiBwImqez3xu2CPhzYBtKWInnXj9lTXW0p-9GEHcbU%7Eoot22qFSdwyZf1UIdmHZLTHPWjtLhfRkKbg-ptA3CFeegtmvCtY-WG2GffJ%7Em2q2bbs-U1m0yI7cSTW18nD8VSBihxGOMnS1IhkO-LgE4I6GJISXROTk-61%7EJiEIKcagcijL4QGi8j1g9xeQamBXX4hWBdkbJgX5PtX15Ftd0HCM4zCzcJAUrE3ZEJRLe2XRUwfKU3ai7-%7ErPpnSA__&Key-Pair-Id=K3RPWS32NSSJCE
+Resolving cdn-lfs.hf.co (cdn-lfs.hf.co)... 18.238.217.63, 18.238.217.81, 18.238.217.120, ...
+Connecting to cdn-lfs.hf.co (cdn-lfs.hf.co)|18.238.217.63|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 13085339 (12M) [text/plain]
+Saving to: ‘databricks-dolly-15k.jsonl’
+
+databricks-dolly-15 100%[===================>]  12.48M  --.-KB/s    in 0.08s   
+
+2025-04-10 20:48:49 (156 MB/s) - ‘databricks-dolly-15k.jsonl’ saved [13085339/13085339]
+```
+
+### Format tuning data
+
+Format the downloaded data for use with the Keras `fit()` method. The following code extracts a subset of the training examples to execute the notebook faster. Consider using more training data for higher quality fine-tuning.
+
+```python
+import json
+
+prompts = []
+responses = []
+line_count = 0
+
+with open("databricks-dolly-15k.jsonl") as file:
+    for line in file:
+        if line_count >= 1000:
+            break  # Limit the training examples, to reduce execution time.
+
+        examples = json.loads(line)
+        # Filter out examples with context, to keep it simple.
+        if examples["context"]:
+            continue
+        # Format data into prompts and response lists.
+        prompts.append(examples["instruction"])
+        responses.append(examples["response"])
+
+        line_count += 1
+
+data = {
+    "prompts": prompts,
+    "responses": responses
+}
+```
+
+### Configure LoRA tuning
+
+Activate LoRA tuning using the Keras `model.backbone.enable_lora()` method, including a LoRA rank value. The *LoRA rank* determines the dimensionality of the trainable matrices that are added to the original weights of the LLM. It controls the expressiveness and precision of the fine-tuning adjustments. A higher rank means more detailed changes are possible, but also means more trainable parameters. A lower rank means less computational overhead, but potentially less precise adaptation.
+
+This example uses a LoRA rank of 4. In practice, begin with a relatively small rank (such as 4, 8, 16). This setting is computationally efficient for experimentation. Train your model with this rank and evaluate the performance improvement on your task. Gradually increase the rank in subsequent trials and see if that further boosts performance.
+
+```python
+# Enable LoRA for the model and set the LoRA rank to 4.
+gemma_lm.backbone.enable_lora(rank=4)
+```
+
+Check the model summary after setting the LoRA rank. Notice that enabling LoRA reduces the number of trainable parameters significantly compared to the total number of parameters in the model:
+
+```python
+gemma_lm.summary()
+```
+
+Configure the rest of the fine-tuning settings, including the preprocessor settings, optimizer, number of tuning epochs, and batch size:
+
+```python
+# Limit the input sequence length to 256 (to control memory usage).
+gemma_lm.preprocessor.sequence_length = 256
+# Use AdamW (a common optimizer for transformer models).
+optimizer = keras.optimizers.AdamW(
+    learning_rate=5e-5,
+    weight_decay=0.01,
+)
+# Exclude layernorm and bias terms from decay.
+optimizer.exclude_from_weight_decay(var_names=["bias", "scale"])
+
+gemma_lm.compile(
+    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+    optimizer=optimizer,
+    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],
+)
+```
+
+### Run the fine-tune process
+
+Run the fine-tuning process using the `fit()` method. This process can take several minutes depending on your compute resources, data size, and number of epochs:
+
+```python
+gemma_lm.fit(data, epochs=1, batch_size=1)
+1000/1000 ━━━━━━━━━━━━━━━━━━━━ 923s 888ms/step - loss: 1.5586 - sparse_categorical_accuracy: 0.5251
+<keras.src.callbacks.history.History at 0x799d04393c40>
+```
+
+#### Mixed precision fine-tuning on NVIDIA GPUs
+
+Full precision is recommended for fine-tuning. When fine-tuning on NVIDIA GPUs, you can use mixed precision (`keras.mixed_precision.set_global_policy('mixed_bfloat16')`) to speed up training with minimal effect on training quality.
+
+```python
+# Uncomment the line below if you want to enable mixed precision training on GPUs
+# keras.mixed_precision.set_global_policy('mixed_bfloat16')
+```
+
+## Inference after fine-tuning
+
+After fine-tuning, you should see changes in the responses when the tuned model is given the same prompt.
+
+### Europe trip prompt
+
+Try the Europe trip prompt from earlier and note the differences in the response.
+
+```python
+prompt = template.format(
+    instruction="What should I do on a trip to Europe?",
+    response="",
+)
+sampler = keras_hub.samplers.TopKSampler(k=5, seed=2)
+gemma_lm.compile(sampler=sampler)
+print(gemma_lm.generate(prompt, max_length=256))
+Instruction:
+What should I do on a trip to Europe?
+
+Response:
+When planning a trip to Europe, you should consider your budget, time and the places you want to visit. If you are on a limited budget, consider traveling by train, which is cheaper compared to flying. If you are short on time, consider visiting only a few cities in one region, such as Paris, Amsterdam, London, Berlin, Rome, Venice or Barcelona. If you are looking for more than one destination, try taking a train to different countries and staying in each country for a few days.
+```
+
+The model now provides a shorter response to a question about visiting Europe.
+
+### Photosynthesis prompt
+
+Try the photosynthesis explanation prompt from earlier and note the differences in the response.
+
+```python
+prompt = template.format(
+    instruction="Explain the process of photosynthesis in a way that a child could understand.",
+    response="",
+)
+print(gemma_lm.generate(prompt, max_length=256))
+Instruction:
+Explain the process of photosynthesis in a way that a child could understand.
+
+Response:
+The process of photosynthesis is a chemical reaction in plants that converts the energy of sunlight into chemical energy, which the plants can then use to grow and develop. During photosynthesis, a plant will absorb carbon dioxide (CO2) from the air and water from the soil and use the energy from the sun to produce oxygen (O2) and sugars (glucose) as a by-product.
+```
+
+The model now explains photosynthesis in simpler terms.
+
+## Improving fine-tune results
+
+For demonstration purposes, this tutorial fine-tunes the model on a small subset of the dataset for just one epoch and with a low LoRA rank value. To get better responses from the fine-tuned model, you can experiment with:
+
+1. Increasing the size of the fine-tuning dataset
+2. Training for more steps (epochs)
+3. Setting a higher LoRA rank
+4. Modifying the hyperparameter values such as `learning_rate` and `weight_decay`.
+
+## Summary and next steps
+
+This tutorial covered LoRA fine-tuning on a Gemma model using Keras. Check out the following docs next:
+
+- Learn how to [generate text with a Gemma model](../get-started/quickstart.md).
+- Learn how to perform [distributed fine-tuning and inference on a Gemma model](distributed.md).
+- Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).
+- Learn how to [fine-tune Gemma using Keras and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb).
diff --git a/docs/deployment/mobile-actions.md b/docs/tuning/mobile-actions.md
similarity index 95%
rename from docs/deployment/mobile-actions.md
rename to docs/tuning/mobile-actions.md
index ddf63f0..ce4922d 100644
--- a/docs/deployment/mobile-actions.md
+++ b/docs/tuning/mobile-actions.md
@@ -87,7 +87,7 @@ You can deploy the model to the Google AI Edge Gallery - Mobile Actions option
 by choosing `Load Model` and selecting it from your Google Drive (or alternative
 method of distribution). The [Google AI Edge Gallery](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery) is
 available on Google Play Store.
-![Mobile Actions Finetune Challenge in Google AI Edge Gallery](../assets/deployment/gallery-mobile-actions-finetune-challenge.png) ![Mobile Actions task in Google AI Edge Gallery](../assets/deployment/gallery-mobile-actions.png)
+![Mobile Actions Finetune Challenge in Google AI Edge Gallery](../assets/tuning/gallery-mobile-actions-finetune-challenge.png) ![Mobile Actions task in Google AI Edge Gallery](../assets/tuning/gallery-mobile-actions.png)
 
 Now, you can try giving a voice command or typing in the app to see how well
 your new fine-tuned model does calling the functions available to it.
diff --git a/docs/tuning/overview.md b/docs/tuning/overview.md
index e12feb1..9e7c752 100644
--- a/docs/tuning/overview.md
+++ b/docs/tuning/overview.md
@@ -1,3 +1,123 @@
-# Fine-Tuning Overview
+# Gemma model fine-tuning
 
-Adapt Gemma models to your specific tasks and domains.
+Fine-tuning a generative artificial intelligence (AI) model such as
+Gemma modifies the behavior of the model. You typically fine-tune Gemma with the
+intent of improving its performance on a specific task or domain, or to better
+fulfill a role, such as customer service. Gemma models are released with open
+weights, which means you can modify those weights, which then changes the
+behavior of the model. The general steps for fine-tuning a Gemma model are as
+follows:
+
+- [Choose a framework](overview.md#choose-a-framework)
+- [Collect data](overview.md#collect-data)
+- [Tune and test the model](overview.md#tune-and-test-the-model)
+- [Deploy the model](overview.md#deploy-the-model)
+
+## Choose a framework
+
+Gemma models are compatible with a variety of AI tuning frameworks. Each
+framework offers various advantages and is typically constrained to a specific
+model format. Here are guides for tuning Gemma models with various frameworks:
+
+- [Keras using LoRA](keras-lora.md)
+- [Gemma library for JAX](https://gemma-llm.readthedocs.io/en/latest/colab_finetuning.html)
+- Hugging Face
+    - [Transformers and PEFT](https://huggingface.co/blog/gemma-peft)
+    - [LLamMA Factory](https://github.com/google-gemma/cookbook/blob/main/.archive/Gemma/%5BGemma_1%5DFinetune_with_LLaMA_Factory.ipynb)
+    - [XTuner](https://github.com/google-gemma/cookbook/blob/main/.archive/Gemma/%5BGemma_1%5DFinetune_with_XTuner.ipynb)
+- [Google Cloud GKE](https://cloud.google.com/kubernetes-engine/docs/tutorials/finetune-gemma-gpu) (multi-GPU with HF Transformers)
+- [Google Cloud Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/models/open-model-tuning)
+- [Unsloth](https://github.com/google-gemma/cookbook/blob/main/.archive/Gemma/%5BGemma_2%5DFinetune_with_Unsloth.ipynb)
+- [Axolotl](https://github.com/google-gemma/cookbook/blob/main/.archive/Gemma/%5BGemma_2%5DFinetune_with_Axolotl.ipynb)
+- [Keras using distributed tuning](distributed.md)
+
+!!! Note
+    The JAX fine-tuning guide has been replaced by the [Gemma library fine-tuning](https://gemma-llm.readthedocs.io/en/latest/colab_sampling.html) guide.
+
+
+Make sure your intended deployment model format, such as Keras format,
+Safetensors, or GGUF, is supported as an output by your chosen framework.
+
+## Collect data
+
+Model tuning requires data. Tuning data typically consists of pairs of input
+data with the expected response. There are many public datasets available online
+for training on various tasks or output. For example, if you wanted to train a
+Gemma model to translate car part descriptions to part numbers, your dataset
+might include the following:
+
+```python
+training_data = [
+  {"input_text": "Part number for A4 brake caliper", "output_text": "4M0615107BS"},
+  {"input_text": "Part number for Beetle fuel pump", "output_text": "6A127026H"},
+  {"input_text": "Part number for Camaro cylinder head", "output_text": "12711770"},
+]
+```
+
+If you want a Gemma model to perform a specific set of tasks or role, you
+typically need to compile a dataset demonstrating multiple variations of that
+task. How much data you need to tune a model depends on your goals, particularly
+how much of a behavioral change you want from the model and how well you want
+the model to perform based on the task to be accomplished and the level of
+variation in the input data.
+
+In general, you should start with a small set of data for your task tuning,
+adjust training parameters, and add data until you achieve the task performance
+that meets your needs. Some of our example applications show that you can
+influence the behavior of a Gemma model with as few as 20 prompt and response
+pairs. For more details, see
+[Build a business email AI assistant with Gemma](../learn/business-email-assistant.md)
+and
+[Tasks in spoken languages with Gemma](spoken-language.md).
+
+## Tune and test the model
+
+Once you have a tuning framework and tuning data in place, you can begin the
+Gemma model tuning process. When performing tuning, you have some options in how
+you tune which affects the resources you need to complete it. You also should
+have a testing plan for your tuned model to evaluate if it is performing the way
+you want it to after tuning.
+
+### Parameter-efficient tuning
+
+When fine-tuning an open weights model such as Gemma, you have the option to
+[tune all the parameters the model](full-finetune.md)
+or use a less resource intensive parameter efficient tuning technique which
+updates a subset of them. A *full* tuning approach means that as you apply your
+tuning data, you calculate new weights for all parameters of the model. This
+approach is compute intensive and memory intensive, since you are performing
+these calculations for billions of parameters. Using less resource intensive
+tuning approaches, called *parameter efficient fine-tuning (PEFT)* , including
+techniques like Low Rank Adapter (LoRA) tuning can produce similar results with
+less compute resources. For details on how to perform tuning with less resources
+using LoRA, see [Fine-tune Gemma models in Keras using
+LoRA](keras-lora.md) and [Fine-Tuning Gemma Models in Hugging
+Face](https://huggingface.co/blog/gemma-peft).
+
+### Testing tuned models
+
+Once you have tuned a model for a specific task you should test its performance
+against the set of tasks you want it to perform. You should test your model with
+tasks or requests that it was not specifically trained on. How you test your
+tuned model depends on the task you want it to perform and how closely you
+manage the inputs and outputs for the model. A common way to manage generative
+model testing is to use success, failure, and borderline cases:
+
+- **Success tests**: Requests that the tuned model should always be able to perform successfully.
+- **Failure tests**: Requests that the tuned model should always not be able to perform, or explicitly refuse to perform, if requested.
+- **Boundary tests**: Requests that the tuned model should be able to perform, if they fall within a defined boundary, or set of boundaries, of acceptable output behavior.
+
+When testing failure or boundary conditions for your generative AI application,
+you should also apply generative AI safety approaches, techniques, and tools as
+described in the [Responsible Generative AI Toolkit](https://ai.google.dev/responsible).
+
+## Deploy the model
+
+After completing your tuning and successful completion of your testing, it's
+time to deploy your model. You can typically refer to the documentation for your
+[chosen framework](overview.md#choose-a-framework) for how to deploy a tuned
+model.
+
+If you are deploying a model with LoRA tuned weights, note that with this
+technique you typically deploy *both* the original model and its weights with
+the LoRA weights as an additional calculation layer for the model.
diff --git a/docs/tuning/qlora.md b/docs/tuning/qlora.md
index 72dc4fc..fb91bdf 100644
--- a/docs/tuning/qlora.md
+++ b/docs/tuning/qlora.md
@@ -1,5 +1,465 @@
-# QLoRA Fine-Tuning
+# Fine-Tune Gemma using Hugging Face Transformers and QloRA
 
-Fine-tune Gemma using QLoRA for memory-efficient training on consumer GPUs.
 
-Content coming soon — this page will be populated from the official Gemma documentation.
+This guide walks you through how to fine-tune Gemma on a custom text-to-sql dataset using Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) and [TRL](https://huggingface.co/docs/trl/index). You will learn:
+
+- What is Quantized Low-Rank Adaptation (QLoRA)
+- Setup development environment
+- Create and prepare the fine-tuning dataset
+- Fine-tune Gemma using TRL and the SFTTrainer
+- Test Model Inference and generate SQL queries
+
+!!! Note
+    This guide was created to run on a Google colaboratory account using a NVIDIA T4 GPU with 16GB and Gemma 1B, but can be adapted to run on bigger GPUs and bigger models.
+
+
+## What is Quantized Low-Rank Adaptation (QLoRA)
+
+This guide demonstrates the use of [Quantized Low-Rank Adaptation (QLoRA)](https://arxiv.org/abs/2305.14314), which emerged as a popular method to efficiently fine-tune LLMs as it reduces computational resource requirements while maintaining high performance. In QloRA, the pretrained model is quantized to 4-bit and the weights are frozen. Then trainable adapter layers (LoRA) are attached and only the adapter layers are trained. Afterwards, the adapter weights can be merged with the base model or kept as a separate adapter.
+
+## Setup development environment
+
+The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model, including different RLHF and alignment techniques.
+
+```python
+# Install Pytorch & other libraries
+%pip install torch tensorboard
+%pip install -U torchao
+
+# Install Transformers
+%pip install "transformers>=5.10.1"
+
+# Install Hugging Face libraries
+%pip install datasets accelerate evaluate bitsandbytes trl "peft>=0.19.0" protobuf sentencepiece
+
+# COMMENT IN: if you are running on a GPU that supports BF16 data type and flash attn, such as NVIDIA L4 or NVIDIA A100
+#%pip install flash-attn
+```
+
+*Note: If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to acelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*
+
+You need a valid Hugging Face Token to publish your model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training.
+
+```python
+# Login into Hugging Face Hub
+from huggingface_hub import login
+login()
+```
+
+## Create and prepare the fine-tuning dataset
+
+When fine-tuning LLMs, it is important to know your use case and the task you want to solve. This helps you create a dataset to fine-tune your model. If you haven't defined your use case yet, you might want to go back to the drawing board.
+
+As an example, this guide focuses on the following use case:
+
+- Fine-tune a natural language to SQL model for seamless integration into a data analysis tool. The objective is to significantly reduce the time and expertise required for SQL query generation, enabling even non-technical users to extract meaningful insights from data.
+
+Text-to-SQL can be a good use case for fine-tuning LLMs, as it is a complex task that requires a lot of (internal) knowledge about the data and the SQL language.
+
+Once you have determined that fine-tuning is the right solution, you need a dataset to fine-tune. The dataset should be a diverse set of demonstrations of the task(s) you want to solve. There are several ways to create such a dataset, including:
+
+- Using existing open-source datasets, such as [Spider](https://huggingface.co/datasets/spider)
+- Using synthetic datasets created by LLMs, such as [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)
+- Using datasets created by humans, such as [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k).
+- Using a combination of the methods, such as [Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca)
+
+Each of the methods has its own advantages and disadvantages and depends on the budget, time, and quality requirements. For example, using an existing dataset is the easiest but might not be tailored to your specific use case, while using domain experts might be the most accurate but can be time-consuming and expensive. It is also possible to combine several methods to create an instruction dataset, as shown in [Orca: Progressive Learning from Complex Explanation Traces of GPT-4.](https://arxiv.org/abs/2306.02707)
+
+This guide uses an already existing dataset ([philschmid/gretel-synthetic-text-to-sql](https://huggingface.co/datasets/philschmid/gretel-synthetic-text-to-sql)), a high quality synthetic Text-to-SQL dataset including natural language instructions, schema definitions, reasoning and the corresponding SQL query.
+
+[Hugging Face TRL](https://huggingface.co/docs/trl/en/index) supports automatic templating of conversation dataset formats. This means you only need to convert your dataset into the right json objects, and `trl` takes care of templating and putting it into the right format.
+
+```text
+{"messages": [{"role": "system", "content": "You are..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
+{"messages": [{"role": "system", "content": "You are..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
+{"messages": [{"role": "system", "content": "You are..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
+```
+
+The [philschmid/gretel-synthetic-text-to-sql](https://huggingface.co/datasets/philschmid/gretel-synthetic-text-to-sql) contains over 100k samples. To keep the guide small, it is downsampled to only use 10,000 samples.
+
+You can now use the Hugging Face Datasets library to load the dataset and create a prompt template to combine the natural language instruction, schema definition and add a system message for your assistant.
+
+```python
+from datasets import load_dataset
+
+# System message for the assistant
+system_message = """You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA."""
+
+# User prompt that combines the user query and the schema
+user_prompt = """Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.
+
+<SCHEMA>
+{context}
+</SCHEMA>
+
+<USER_QUERY>
+{question}
+</USER_QUERY>
+"""
+def create_conversation(sample, idx):
+  return {
+    "messages": [
+      {"role": "system", "content": system_message},
+      {"role": "user", "content": user_prompt.format(question=sample["sql_prompt"], context=sample["sql_context"])},
+      {"role": "assistant", "content": sample["sql"]}
+    ]
+  }
+
+# Load dataset from the hub
+dataset = load_dataset("philschmid/gretel-synthetic-text-to-sql", split="train")
+dataset = dataset.select(range(1250))
+
+# Convert dataset to OAI messages
+dataset = dataset.map(create_conversation, with_indices=True, remove_columns=dataset.features)
+# split dataset into 80% training samples and 20% test samples
+dataset = dataset.train_test_split(test_size=0.2, shuffle=False)
+
+# Print formatted user prompt
+for item in dataset["train"][0]["messages"]:
+  print(item)
+{'role': 'system', 'content': 'You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.'}
+{'role': 'user', 'content': "Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.\n\n<SCHEMA>\nCREATE TABLE salesperson (salesperson_id INT, name TEXT, region TEXT); INSERT INTO salesperson (salesperson_id, name, region) VALUES (1, 'John Doe', 'North'), (2, 'Jane Smith', 'South'); CREATE TABLE timber_sales (sales_id INT, salesperson_id INT, volume REAL, sale_date DATE); INSERT INTO timber_sales (sales_id, salesperson_id, volume, sale_date) VALUES (1, 1, 120, '2021-01-01'), (2, 1, 150, '2021-02-01'), (3, 2, 180, '2021-01-01');\n</SCHEMA>\n\n<USER_QUERY>\nWhat is the total volume of timber sold by each salesperson, sorted by salesperson?\n</USER_QUERY>\n"}
+{'role': 'assistant', 'content': 'SELECT salesperson_id, name, SUM(volume) as total_volume FROM timber_sales JOIN salesperson ON timber_sales.salesperson_id = salesperson.salesperson_id GROUP BY salesperson_id, name ORDER BY total_volume DESC;'}
+```
+
+## Fine-tune Gemma using TRL and the SFTTrainer
+
+You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) makes it straightforward to supervise fine-tune open LLMs. The `SFTTrainer` is a subclass of the `Trainer` from the `transformers` library and supports all the same features, including logging, evaluation, and checkpointing, but adds additional quality of life features, including:
+
+- Dataset formatting, including conversational and instruction formats
+- Training on completions only, ignoring prompts
+- Packing datasets for more efficient training
+- Parameter-efficient fine-tuning (PEFT) support including QloRA
+- Preparing the model and tokenizer for conversational fine-tuning (such as adding special tokens)
+
+The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration.
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig
+from peft import prepare_model_for_kbit_training
+
+# Hugging Face model id
+model_id = "google/gemma-4-E2B" # @param ["google/gemma-4-E2B","google/gemma-4-E4B","google/gemma-4-12B","google/gemma-4-31B","google/gemma-4-26B-A4B"] {"allow-input":true}
+
+# Check if GPU supports bfloat16
+if torch.cuda.is_bf16_supported():
+    torch_dtype = torch.bfloat16
+else:
+    torch_dtype = torch.float16
+
+# Define model init arguments
+model_kwargs = dict(
+    dtype=torch_dtype, # What torch dtype to use
+    device_map="auto", # Let torch decide how to load the model
+)
+
+# BitsAndBytesConfig: Enables 4-bit quantization to reduce model size/memory usage
+model_kwargs["quantization_config"] = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_compute_dtype=torch_dtype,
+    bnb_4bit_quant_storage=torch_dtype,
+)
+
+# Load model and processor
+model = AutoModelForMultimodalLM.from_pretrained(model_id, **model_kwargs)
+processor = AutoProcessor.from_pretrained("google/gemma-4-E2B-it") # Load the Instruction Processor to use the official Gemma template
+
+# NOTE: You should call the prepare_model_for_kbit_training() function to preprocess the quantized model for training.
+# On T4, we are skipping this step purely due to VRAM limitation and for a quick demonstration.
+if (torch.cuda.get_device_properties(0).total_memory/1024**3) > 16:
+    model = prepare_model_for_kbit_training(model)
+WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so
+WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so
+Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
+/usr/local/lib/python3.12/dist-packages/bitsandbytes/backends/cuda/ops.py:213: FutureWarning: _check_is_size will be removed in a future PyTorch release along with guard_size_oblivious.     Use _check(i >= 0) instead.
+  torch._check_is_size(blocksize)
+```
+
+The `SFTTrainer` supports a built-in integration with `peft`, which makes it straightforward to efficiently tune LLMs using QLoRA. You only need to create a `LoraConfig` and provide it to the trainer.
+
+```python
+from peft import LoraConfig
+
+peft_config = LoraConfig(
+    lora_alpha=16,
+    lora_dropout=0.05,
+    r=16,
+    bias="none",
+    # no target_modules --- PEFT's Gemma 4 defaults scope to the LM layers
+    task_type="CAUSAL_LM",
+    modules_to_save=["lm_head", "embed_tokens"], # make sure to save the lm_head and embed_tokens as you train the special tokens
+    ensure_weight_tying=True,
+)
+```
+
+Before you can start your training, you need to define the hyperparameter you want to use in a `SFTConfig` instance.
+
+```python
+import torch
+from trl import SFTConfig
+
+args = SFTConfig(
+    output_dir="gemma-text-to-sql",         # directory to save and repository id
+    max_length=512,                         # max length for model and packing of the dataset
+    num_train_epochs=3,                     # number of training epochs
+    per_device_train_batch_size=1,          # batch size per device during training
+    per_device_eval_batch_size=1,           # batch size per device during evaluation
+    optim="adamw_torch_fused",              # use fused adamw optimizer
+    logging_steps=10,                       # log every 10 steps
+    save_strategy="epoch",                  # save checkpoint every epoch
+    eval_strategy="epoch",                  # evaluate checkpoint every epoch
+    learning_rate=2e-4,                     # learning rate
+    fp16=True if torch_dtype == torch.float16 else False,  # use float16 precision
+    bf16=True if torch_dtype == torch.bfloat16 else False, # use bfloat16 precision
+    lr_scheduler_type="constant",           # use constant learning rate scheduler
+    push_to_hub=True,                       # push model to hub
+    report_to="tensorboard",                # report metrics to tensorboard
+    dataset_kwargs={"skip_prepare_dataset": True}, # important for collator
+    remove_unused_columns = False,                 # important for collator
+)
+
+# Data collator
+def collate_fn(examples):
+    texts = []
+
+    for example in examples:
+        full_text = processor.apply_chat_template(
+            example["messages"], add_generation_prompt=False, tokenize=False
+        )
+        texts.append(full_text.strip())
+
+    # Tokenize the texts and process the audios
+    batch = processor(text=texts, return_tensors="pt", padding=True)
+
+    # The labels are the input_ids, and we mask the padding tokens and audio tokens in the loss computation
+    labels = batch["input_ids"].clone()
+
+    target_tokens = [
+        processor.tokenizer.convert_tokens_to_ids("<|turn>"),
+        processor.tokenizer.convert_tokens_to_ids("model"),
+        processor.tokenizer.convert_tokens_to_ids("\n")
+    ]
+    target_len = len(target_tokens)
+
+    for i in range(labels.size(0)):
+        row_tokens = batch["input_ids"][i].tolist()
+
+        # Find where the assistant block begins
+        assistant_start_idx = None
+        for idx in range(len(row_tokens) - target_len + 1):
+            if row_tokens[idx : idx + target_len] == target_tokens:
+                # We want to keep loss calculation on the assistant transcription tokens,
+                # so we move the index right past the assistant header ('<|turn>\nmodel\n')
+                assistant_start_idx = idx + target_len
+                break
+
+        if assistant_start_idx is not None:
+            # Mask everything from index 0 up to the start of the actual Japanese text response
+            labels[i, :assistant_start_idx] = -100
+        else:
+            # Fallback safety: if template matching fails for an anomalous row, mask padding anyway
+            print("WARNING: maybe the sample is too long, try to increase `token_limit` value.")
+            labels[i, labels[i] == processor.tokenizer.pad_token_id] = -100
+
+        """
+        # --- DEBUG PRINT CODE ---
+        print(f"\n--- Example {i} (Split index: {assistant_start_idx}) ---")
+        debug_string = []
+        for token_id, label_id in zip(row_tokens, labels[i].tolist()):
+            # Decode token by token so we can see exactly what is masked
+            decoded_token = processor.tokenizer.decode([token_id])
+
+            if label_id == -100:
+                # Red text for masked tokens (ANSI Escape Code)
+                debug_string.append(f"\033[91m{decoded_token}\033[0m")
+            else:
+                # Green text for active loss tokens
+                debug_string.append(f"\033[92m{decoded_token}\033[0m")
+
+        print("".join(debug_string))
+        # ---
+        """
+
+
+    # Mask tokens for not being used in the loss computation
+    labels[labels == processor.tokenizer.pad_token_id] = -100
+
+    batch["labels"] = labels
+    return batch
+/tmp/ipykernel_164873/3952412390.py:4: FutureWarning: The default `loss_type` will change from `'nll'` to `'chunked_nll'` in TRL 1.7. For standard models this is transparent (same math, lower memory) and no action is needed — you'll get the new default automatically on upgrade. If you use a custom model, check ahead of time that `loss_type='chunked_nll'` runs and yields the same loss as `'nll'`; if it doesn't, pin `loss_type='nll'` to keep the current behavior and please open an issue at https://github.com/huggingface/trl/issues so we can address the edge case.
+  args = SFTConfig(
+```
+
+You now have every building block you need to create your `SFTTrainer` to start the training of your model.
+
+```python
+from trl import SFTTrainer
+
+# Create Trainer object
+trainer = SFTTrainer(
+    model=model,
+    args=args,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["test"],
+    peft_config=peft_config,
+    processing_class=processor,
+    data_collator=collate_fn,
+)
+```
+
+Start training by calling the `train()` method.
+
+```python
+# Start training, the model will be automatically saved to the Hub and the output directory
+trainer.train()
+
+# Save the final model again to the Hugging Face Hub
+trainer.save_model()
+[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 1, 'bos_token_id': 2, 'pad_token_id': 0}.
+```
+\[3000/3000 1:51:30, Epoch 3/3\]
+
+| Epoch | Training Loss | Validation Loss | Entropy | Mean Token Accuracy | Num Tokens |
+|---|---|---|---|---|---|
+| 1 | 0.315478 | 0.267880 | 0.204330 | 0.927141 | 246435.000000 |
+| 2 | 0.139383 | 0.297303 | 0.131811 | 0.929568 | 492870.000000 |
+| 3 | 0.150697 | 0.315715 | 0.110690 | 0.927032 | 739305.000000 |
+
+
+``` { .no-copy .output }
+Processing Files (0 / 0)      : |          |  0.00B /  0.00B
+New Data Upload               : |          |  0.00B /  0.00B
+...adapter_model.safetensors:   3%|3         | 56.0MB / 1.62GB
+...154.ffa1dd7a1058.125401.0: 100%|##########|  126kB /  126kB
+...268.ffa1dd7a1058.112980.0: 100%|##########| 47.9kB / 47.9kB
+...-to-sql/training_args.bin: 100%|##########| 5.65kB / 5.65kB
+...482.ffa1dd7a1058.164873.0: 100%|##########|  126kB /  126kB
+...ext-to-sql/tokenizer.json: 100%|##########| 32.2MB / 32.2MB
+...039.ffa1dd7a1058.109416.0: 100%|##########| 8.28kB / 8.28kB
+No files have been modified since last commit. Skipping to prevent empty commit.
+WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.
+```
+
+Before you can test your model, make sure to free the memory.
+
+```python
+# free the memory again
+del model
+del trainer
+torch.cuda.empty_cache()
+```
+
+When using QLoRA, you only train adapters and not the full model. This means when saving the model during training you only save the adapter weights and not the full model. If you want to save the full model, which makes it easier to use with serving stacks like vLLM or TGI, you can merge the adapter weights into the model weights using the `merge_and_unload` method and then save the model with the `save_pretrained` method. This saves a default model, which can be used for inference.
+
+!!! Note
+    It requires more than 30GB of CPU Memory when you want to merge the adapter into the model. You can skip this and continue with Test Model Inference.
+
+
+```python
+from transformers import AutoModelForMultimodalLM, AutoProcessor
+from peft import PeftModel
+
+# Load Model base model
+model = AutoModelForMultimodalLM.from_pretrained(model_id, low_cpu_mem_usage=True)
+
+# Merge LoRA and base model and save
+peft_model = PeftModel.from_pretrained(model, args.output_dir)
+merged_model = peft_model.merge_and_unload()
+merged_model.save_pretrained("merged_model", safe_serialization=True, max_shard_size="2GB")
+
+processor = AutoProcessor.from_pretrained("google/gemma-4-E2B-it")
+processor.save_pretrained("merged_model")
+Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
+Writing model shards:   0%|          | 0/4 [00:00<?, ?it/s]
+['merged_model/processor_config.json']
+```
+
+## Test Model Inference and generate SQL queries
+
+After the training is done, you'll want to evaluate and test your model. You can load different samples from the test dataset and evaluate the model on those samples.
+
+!!! Note
+    Evaluating generative AI models is not a trivial task since one input can have multiple correct outputs. This guide only focuses on manual evaluation and vibe checks.
+
+
+```python
+from transformers import AutoModelForMultimodalLM, AutoProcessor
+
+model_id = "merged_model"
+
+# Load Model with PEFT adapter
+model = AutoModelForMultimodalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+  dtype="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
+```
+
+Let's load a random sample from the test dataset and generate a SQL command.
+
+```python
+from random import randint
+import re
+from transformers import pipeline, GenerationConfig, pipeline
+
+config = GenerationConfig.from_pretrained(model_id)
+config.max_new_tokens = 256
+config.eos_token_id = [processor.tokenizer.convert_tokens_to_ids("<turn|>")]
+
+# Load the model and tokenizer into the pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=processor.tokenizer)
+
+# Load a random sample from the test dataset
+rand_idx = randint(0, len(dataset["test"]))
+test_sample = dataset["test"][rand_idx]
+
+# Convert as test example into a prompt with the Gemma template
+prompt = processor.tokenizer.apply_chat_template(test_sample["messages"][:2], tokenize=False, add_generation_prompt=True)
+print(prompt)
+
+# Generate our SQL query.
+outputs = pipe(text_inputs=prompt, generation_config=config)
+
+# Extract the user query and original answer
+print(f"Context:\n", re.search(r'<SCHEMA>\n(.*?)\n</SCHEMA>', test_sample['messages'][1]['content'], re.DOTALL).group(1).strip())
+print(f"Query:\n", re.search(r'<USER_QUERY>\n(.*?)\n</USER_QUERY>', test_sample['messages'][1]['content'], re.DOTALL).group(1).strip())
+print(f"Original Answer:\n{test_sample['messages'][2]['content']}")
+print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip().removesuffix("<turn|>")}")
+<bos><|turn>system
+You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.<turn|>
+<|turn>user
+Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.
+
+<SCHEMA>
+CREATE TABLE Auto_Shows (id INT, show_name VARCHAR(255), show_year INT, location VARCHAR(255)); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (1, 'New York International Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (2, 'Chicago Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (3, 'North American International Auto Show', 2018, 'United States');
+</SCHEMA>
+
+<USER_QUERY>
+How many auto shows were held in the United States in the year 2019?
+</USER_QUERY><turn|>
+<|turn>model
+
+Context:
+ CREATE TABLE Auto_Shows (id INT, show_name VARCHAR(255), show_year INT, location VARCHAR(255)); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (1, 'New York International Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (2, 'Chicago Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (3, 'North American International Auto Show', 2018, 'United States');
+Query:
+ How many auto shows were held in the United States in the year 2019?
+Original Answer:
+SELECT COUNT(*) FROM Auto_Shows WHERE show_year = 2019 AND location = 'United States';
+Generated Answer:
+SELECT COUNT(*) FROM Auto_Shows WHERE location = 'United States' AND show_year = 2019;
+```
+
+## Summary and next steps
+
+This tutorial covered how to fine-tune a Gemma model using TRL and QLoRA. Check out the following docs next:
+
+- Learn how to [generate text with a Gemma model](../get-started/quickstart.md).
+- Learn how to [fine-tune Gemma for vision tasks using Hugging Face Transformers](vision-qlora.md).
+- Learn how to perform [distributed fine-tuning and inference on a Gemma model](distributed.md).
+- Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).
+- Learn how to [fine-tune Gemma using KerasNLP and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb).
diff --git a/docs/tuning/vision-qlora.md b/docs/tuning/vision-qlora.md
index de37bfd..ebec298 100644
--- a/docs/tuning/vision-qlora.md
+++ b/docs/tuning/vision-qlora.md
@@ -1,3 +1,466 @@
-# Vision QLoRA
+# Fine-Tune Gemma for Vision Tasks using Hugging Face Transformers and QLoRA
 
-Fine-tune Gemma for vision tasks using QLoRA.
+
+This guide walks you through how to fine-tune Gemma on a custom image and text dataset for a vision task (generating product descriptions) using Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) and [TRL](https://huggingface.co/docs/trl/index). You will learn:
+
+- What is Quantized Low-Rank Adaptation (QLoRA)
+- Setup development environment
+- Create and prepare the fine-tuning dataset
+- Fine-tune Gemma using TRL and the SFTTrainer
+- Test Model Inference and generate product descriptions from images and text.
+
+!!! Note
+    This guide requires a GPU which support bfloat16 data type such as NVIDIA L4 or NVIDIA A100 and more than 16GB of memory.
+
+
+## What is Quantized Low-Rank Adaptation (QLoRA)
+
+This guide demonstrates the use of [Quantized Low-Rank Adaptation (QLoRA)](https://arxiv.org/abs/2305.14314), which emerged as a popular method to efficiently fine-tune LLMs as it reduces computational resource requirements while maintaining high performance. In QloRA, the pretrained model is quantized to 4-bit and the weights are frozen. Then trainable adapter layers (LoRA) are attached and only the adapter layers are trained. Afterwards, the adapter weights can be merged with the base model or kept as a separate adapter.
+
+## Setup development environment
+
+The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model.
+
+```python
+# Install Pytorch & other libraries
+%pip install torch tensorboard torchvision
+%pip install -U torchao
+
+# Install Transformers
+%pip install "transformers>=5.10.1"
+
+# Install Hugging Face libraries
+%pip install datasets accelerate evaluate bitsandbytes trl "peft>=0.19.0" protobuf pillow sentencepiece
+
+# COMMENT IN: if you are running on a GPU that supports BF16 data type and flash attn, such as NVIDIA L4 or NVIDIA A100
+#%pip install flash-attn
+```
+
+*Note: If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to acelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*
+
+You need a valid Hugging Face Token to publish your model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training.
+
+```python
+# Login into Hugging Face Hub
+from huggingface_hub import login
+login()
+```
+
+## Create and prepare the fine-tuning dataset
+
+When fine-tuning LLMs, it is important to know your use case and the task you want to solve. This helps you create a dataset to fine-tune your model. If you haven't defined your use case yet, you might want to go back to the drawing board.
+
+As an example, this guide focuses on the following use case:
+
+- Fine-tuning a Gemma model to generate concise, SEO-optimized product descriptions for an ecommerce platform, specifically tailored for mobile search.
+
+This guide uses the [philschmid/amazon-product-descriptions-vlm](https://huggingface.co/datasets/philschmid/amazon-product-descriptions-vlm) dataset, a dataset of Amazon product descriptions, including product images and categories.
+
+Hugging Face TRL supports multimodal conversations. The important piece is the "image" role, which tells the processing class that it should load the image. The structure should follow:
+
+```text
+{"messages": [{"role": "system", "content": [{"type": "text", "text":"You are..."}]}, {"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image"}]}, {"role": "assistant", "content": [{"type": "text", "text": "..."}]}]}
+{"messages": [{"role": "system", "content": [{"type": "text", "text":"You are..."}]}, {"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image"}]}, {"role": "assistant", "content": [{"type": "text", "text": "..."}]}]}
+{"messages": [{"role": "system", "content": [{"type": "text", "text":"You are..."}]}, {"role": "user", "content": [{"type": "text", "text": "..."}, {"type": "image"}]}, {"role": "assistant", "content": [{"type": "text", "text": "..."}]}]}
+```
+
+You can now use the Hugging Face Datasets library to load the dataset and create a prompt template to combine the image, product name, and category, and add a system message. The dataset includes images as`Pil.Image` objects.
+
+```python
+from datasets import load_dataset
+from PIL import Image
+
+# System message for the assistant
+system_message = "You are an expert product description writer for Amazon."
+
+# User prompt that combines the user query and the schema
+user_prompt = """Create a Short Product description based on the provided <PRODUCT> and <CATEGORY> and image.
+Only return description. The description should be SEO optimized and for a better mobile search experience.
+
+<PRODUCT>
+{product}
+</PRODUCT>
+
+<CATEGORY>
+{category}
+</CATEGORY>
+"""
+
+# Convert dataset to OAI messages
+def format_data(sample):
+    return {
+        "messages": [
+            {
+                "role": "system",
+                #"content": [{"type": "text", "text": system_message}],
+                "content": system_message,
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": user_prompt.format(
+                            product=sample["Product Name"],
+                            category=sample["Category"],
+                        ),
+                    },
+                    {
+                        "type": "image",
+                        "image": sample["image"],
+                    },
+                ],
+            },
+            {
+                "role": "assistant",
+                "content": [{"type": "text", "text": sample["description"]}],
+            },
+        ],
+    }
+
+def process_vision_info(messages: list[dict]) -> list[Image.Image]:
+    image_inputs = []
+    # Iterate through each conversation
+    for msg in messages:
+        # Get content (ensure it's a list)
+        content = msg.get("content", [])
+        if not isinstance(content, list):
+            content = [content]
+
+        # Check each content element for images
+        for element in content:
+            if isinstance(element, dict) and (
+                "image" in element or element.get("type") == "image"
+            ):
+                # Get the image and convert to RGB
+                if "image" in element:
+                    image = element["image"]
+                else:
+                    image = element
+                image_inputs.append(image.convert("RGB"))
+    return image_inputs
+
+# Load dataset from the hub
+dataset = load_dataset("philschmid/amazon-product-descriptions-vlm", split="train")
+dataset = dataset.train_test_split(test_size=0.1)
+
+# Convert dataset to OAI messages
+# need to use list comprehension to keep Pil.Image type, .mape convert image to bytes
+dataset_train = [format_data(sample) for sample in dataset["train"]]
+dataset_test = [format_data(sample) for sample in dataset["test"]]
+
+print(dataset_train[345]["messages"])
+[{'role': 'system', 'content': 'You are an expert product description writer for Amazon.'}, {'role': 'user', 'content': [{'type': 'text', 'text': 'Create a Short Product description based on the provided <PRODUCT> and <CATEGORY> and image.\nOnly return description. The description should be SEO optimized and for a better mobile search experience.\n\n<PRODUCT>\nBachmann Trains inches 6 inches Pine Trees with Snow 6 Per Box\n</PRODUCT>\n\n<CATEGORY>\nToys & Games | Hobbies | Trains & Accessories | Accessories | Scenery\n</CATEGORY>\n'}, {'type': 'image', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=173x500 at 0x7ED5E4422C00>}]}, {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Enhance your model train landscape with these realistic Bachmann Trains 6-inch pine trees, flocked with snow.  Six highly detailed trees per box, perfect for adding winter charm to your HO scale layout.  Create a captivating snowy scene!'}]}]
+```
+
+## Fine-tune Gemma using TRL and the SFTTrainer
+
+You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) makes it straightforward to supervise fine-tune open LLMs. The `SFTTrainer` is a subclass of the `Trainer` from the `transformers` library and supports all the same features, including logging, evaluation, and checkpointing, but adds additional quality of life features, including:
+
+- Dataset formatting, including conversational and instruction formats
+- Training on completions only, ignoring prompts
+- Packing datasets for more efficient training
+- Parameter-efficient fine-tuning (PEFT) support including QloRA
+- Preparing the model and tokenizer for conversational fine-tuning (such as adding special tokens)
+
+The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration.
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig
+from peft import prepare_model_for_kbit_training
+
+# Hugging Face model id
+model_id = "google/gemma-4-E2B" # @param ["google/gemma-4-E2B","google/gemma-4-E4B","google/gemma-4-12B","google/gemma-4-31B","google/gemma-4-26B-A4B"] {"allow-input":true}
+
+# Check if GPU supports bfloat16
+if not torch.cuda.is_bf16_supported():
+    raise ValueError("GPU does not support bfloat16, please use a GPU that supports bfloat16.")
+
+torch_dtype = torch.bfloat16
+
+# Define model init arguments
+model_kwargs = dict(
+    dtype=torch_dtype, # What torch dtype to use
+    device_map="auto", # Let torch decide how to load the model
+)
+
+# BitsAndBytesConfig int-4 config
+model_kwargs["quantization_config"] = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch_dtype,
+    bnb_4bit_quant_storage=torch_dtype,
+)
+
+# Load model and processor
+model = AutoModelForMultimodalLM.from_pretrained(model_id, **model_kwargs)
+processor = AutoProcessor.from_pretrained("google/gemma-4-E2B-it") # Load the Instruction Tokenizer to use the official Gemma template
+
+# Prepare model for QLoRA training
+model = prepare_model_for_kbit_training(model)
+Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
+/usr/local/lib/python3.12/dist-packages/bitsandbytes/backends/cuda/ops.py:213: FutureWarning: _check_is_size will be removed in a future PyTorch release along with guard_size_oblivious.     Use _check(i >= 0) instead.
+  torch._check_is_size(blocksize)
+```
+
+The `SFTTrainer` supports a built-in integration with `peft`, which makes it straightforward to efficiently tune LLMs using QLoRA. You only need to create a `LoraConfig` and provide it to the trainer.
+
+```python
+from peft import LoraConfig
+
+peft_config = LoraConfig(
+    lora_alpha=16,
+    lora_dropout=0.05,
+    r=16,
+    bias="none",
+    # no target_modules --- PEFT's Gemma 4 defaults scope to the LM layers
+    task_type="CAUSAL_LM",
+    modules_to_save=["lm_head", "embed_tokens"], # make sure to save the lm_head and embed_tokens as you train the special tokens
+    ensure_weight_tying=True,
+)
+```
+
+Before you can start your training, you need to define the hyperparameter you want to use in a `SFTConfig` and a custom `collate_fn` to handle the vision processing. The `collate_fn` converts the messages with text and images into a format that the model can understand.
+
+```python
+from trl import SFTConfig
+
+args = SFTConfig(
+    output_dir="gemma-product-description",     # directory to save and repository id
+    num_train_epochs=3,                         # number of training epochs
+    per_device_train_batch_size=1,              # batch size per device during training
+    per_device_eval_batch_size=1,               # batch size per device during evaluation
+    optim="adamw_torch_fused",                  # use fused adamw optimizer
+    logging_steps=5,                            # log every 5 steps
+    save_strategy="epoch",                      # save checkpoint every epoch
+    eval_strategy="epoch",                      # evaluate checkpoint every epoch
+    learning_rate=2e-4,                         # learning rate, based on QLoRA paper
+    bf16=True,                                  # use bfloat16 precision
+    max_grad_norm=0.3,                          # max gradient norm based on QLoRA paper
+    lr_scheduler_type="constant",               # use constant learning rate scheduler
+    push_to_hub=True,                           # push model to hub
+    report_to="tensorboard",                    # report metrics to tensorboard
+    dataset_text_field="",                      # need a dummy field for collator
+    dataset_kwargs={"skip_prepare_dataset": True}, # important for collator
+    remove_unused_columns = False               # important for collator
+)
+
+# Create a data collator to encode text and image pairs
+def collate_fn(examples):
+    texts = []
+    images = []
+    for example in examples:
+        image_inputs = process_vision_info(example["messages"])
+        text = processor.apply_chat_template(
+            example["messages"], add_generation_prompt=False, tokenize=False
+        )
+        texts.append(text.strip())
+        images.append(image_inputs)
+
+    # Tokenize the texts and process the images
+    batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
+
+    # The labels are the input_ids, and we mask the padding tokens and image tokens in the loss computation
+    labels = batch["input_ids"].clone()
+
+    # Mask tokens for not being used in the loss computation
+    labels[labels == processor.tokenizer.pad_token_id] = -100
+    labels[labels == processor.tokenizer.boi_token_id] = -100
+    labels[labels == processor.tokenizer.image_token_id] = -100
+    labels[labels == processor.tokenizer.eoi_token_id] = -100
+
+    batch["labels"] = labels
+    return batch
+/tmp/ipykernel_26645/1232990497.py:3: FutureWarning: The default `loss_type` will change from `'nll'` to `'chunked_nll'` in TRL 1.7. For standard models this is transparent (same math, lower memory) and no action is needed — you'll get the new default automatically on upgrade. If you use a custom model, check ahead of time that `loss_type='chunked_nll'` runs and yields the same loss as `'nll'`; if it doesn't, pin `loss_type='nll'` to keep the current behavior and please open an issue at https://github.com/huggingface/trl/issues so we can address the edge case.
+  args = SFTConfig(
+```
+
+You now have every building block you need to create your `SFTTrainer` to start the training of your model.
+
+```python
+from trl import SFTTrainer
+
+# Create Trainer object
+trainer = SFTTrainer(
+    model=model,
+    args=args,
+    train_dataset=dataset_train,
+    eval_dataset=dataset_test,
+    peft_config=peft_config,
+    processing_class=processor,
+    data_collator=collate_fn,
+)
+```
+
+Start training by calling the `train()` method.
+
+```python
+# Start training, the model will be automatically saved to the Hub and the output directory
+trainer.train()
+
+# Save the final model again to the Hugging Face Hub
+trainer.save_model()
+[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 1, 'bos_token_id': 2, 'pad_token_id': 0}.
+```
+\[3630/3630 57:38, Epoch 3/3\]
+
+| Epoch | Training Loss | Validation Loss | Entropy | Mean Token Accuracy | Num Tokens |
+|---|---|---|---|---|---|
+| 1 | 0.835934 | 0.844098 | 0.735220 | 0.818571 | 521741.000000 |
+| 2 | 0.411868 | 0.886929 | 0.548703 | 0.822062 | 1043482.000000 |
+| 3 | 0.488929 | 1.007192 | 0.417825 | 0.822075 | 1565223.000000 |
+
+
+``` { .no-copy .output }
+Processing Files (0 / 0)      : |          |  0.00B /  0.00B
+New Data Upload               : |          |  0.00B /  0.00B
+...34898.c7e1d0064144.7818.0: 100%|##########| 43.2kB / 43.2kB
+...escription/tokenizer.json: 100%|##########| 32.2MB / 32.2MB
+...adapter_model.safetensors:   5%|4         | 80.0MB / 1.62GB
+...5378.c7e1d0064144.10487.0: 100%|##########|  289kB /  289kB
+...ription/training_args.bin: 100%|##########| 5.71kB / 5.71kB
+No files have been modified since last commit. Skipping to prevent empty commit.
+WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.
+```
+
+Before you can test your model, make sure to free the memory.
+
+```python
+# free the memory again
+del model
+del trainer
+torch.cuda.empty_cache()
+```
+
+When using QLoRA, you only train adapters and not the full model. This means when saving the model during training you only save the adapter weights and not the full model. If you want to save the full model, which makes it easier to use with serving stacks like vLLM or TGI, you can merge the adapter weights into the model weights using the `merge_and_unload` method and then save the model with the `save_pretrained` method. This saves a default model, which can be used for inference.
+
+!!! Note
+    It requires more than 30GB of CPU Memory when you want to merge the adapter into the model. You can skip this and continue with Test Model Inference.
+
+
+```python
+from transformers import AutoModelForMultimodalLM, AutoProcessor
+from peft import PeftModel
+
+# Load Model base model
+model = AutoModelForMultimodalLM.from_pretrained(model_id, low_cpu_mem_usage=True)
+
+# Merge LoRA and base model and save
+peft_model = PeftModel.from_pretrained(model, args.output_dir)
+merged_model = peft_model.merge_and_unload()
+merged_model.save_pretrained("merged_model", safe_serialization=True, max_shard_size="2GB")
+
+processor = AutoProcessor.from_pretrained(args.output_dir)
+processor.save_pretrained("merged_model")
+Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
+Writing model shards:   0%|          | 0/4 [00:00<?, ?it/s]
+['merged_model/processor_config.json']
+```
+
+## Test Model Inference and generate product descriptions
+
+After the training is done, you'll want to evaluate and test your model. You can load different samples from the test dataset and evaluate the model on those samples.
+
+!!! Note
+    Evaluating Generative AI models is not a trivial task since one input can have multiple correct outputs. This guide only focuses on manual evaluation and vibe checks.
+
+
+```python
+from transformers import AutoModelForMultimodalLM, AutoProcessor
+
+model_id = "merged_model"
+
+# Load Model with PEFT adapter
+model = AutoModelForMultimodalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+  dtype="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
+```
+
+You can test inference by providing a product name, category and image. The `sample` includes a marvel action figure.
+
+```python
+import requests
+from PIL import Image
+
+# Test sample with Product Name, Category and Image
+sample = {
+  "product_name": "Hasbro Marvel Avengers-Serie Marvel Assemble Titan-Held, Iron Man, 30,5 cm Actionfigur",
+  "category": "Toys & Games | Toy Figures & Playsets | Action Figures",
+  "image": Image.open(requests.get("https://m.media-amazon.com/images/I/81+7Up7IWyL._AC_SY300_SX300_.jpg", stream=True).raw).convert("RGB")
+}
+
+def generate_description(sample, model, processor):
+    # Convert sample into messages and then apply the chat template
+    messages = [
+        {"role": "system", "content": system_message},
+        {"role": "user", "content": [
+            {"type": "image","image": sample["image"]},
+            {"type": "text", "text": user_prompt.format(product=sample["product_name"], category=sample["category"])},
+        ]},
+    ]
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    print(text)
+    # Process the image and text
+    image_inputs = process_vision_info(messages)
+    # Tokenize the text and process the images
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+    # Move the inputs to the device
+    inputs = inputs.to(model.device)
+
+    # Generate the output
+    stop_token_ids = [processor.tokenizer.eos_token_id, processor.tokenizer.convert_tokens_to_ids("<turn|>")]
+    generated_ids = model.generate(**inputs, max_new_tokens=256, top_p=1.0, do_sample=True, temperature=0.8, eos_token_id=stop_token_ids, disable_compile=True)
+    # Trim the generation and decode the output to text
+    generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    return output_text[0]
+
+# generate the description
+description = generate_description(sample, model, processor)
+print("MODEL OUTPUT>> \n")
+print(description)
+<bos><|turn>system
+You are an expert product description writer for Amazon.<turn|>
+<|turn>user
+<|image|>Create a Short Product description based on the provided <PRODUCT> and <CATEGORY> and image.
+Only return description. The description should be SEO optimized and for a better mobile search experience.
+
+<PRODUCT>
+Hasbro Marvel Avengers-Serie Marvel Assemble Titan-Held, Iron Man, 30,5 cm Actionfigur
+</PRODUCT>
+
+<CATEGORY>
+Toys & Games | Toy Figures & Playsets | Action Figures
+</CATEGORY><turn|>
+<|turn>model
+
+MODEL OUTPUT>> 
+
+Bring home the action with the Hasbro Marvel Avengers Assemble Titan-held Iron Man Action Figure! This highly detailed 30.5-centimeter figure is perfect for recreating epic battles.  Collect the entire Marvel Avengers Assemble Titan-held series for an authentic experience.  Shop now!
+```
+
+## Summary and next steps
+
+This tutorial covered how to fine-tune a Gemma model for vision tasks using TRL and QLoRA, specifically for generating product descriptions. Check out the following docs next:
+
+- Learn how to [generate text with a Gemma model](../get-started/quickstart.md).
+- Learn how to [fine-tune Gemma for text tasks using Hugging Face Transformers](qlora.md).
+- Learn how to [full model fine-tune using Hugging Face Transformers](full-finetune.md).
+- Learn how to perform [distributed fine-tuning and inference on a Gemma model](distributed.md).
+- Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).
+- Learn how to [fine-tune Gemma using KerasNLP and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb).
diff --git a/scripts/convert_notebook.py b/scripts/convert_notebook.py
index 6203d57..733b29b 100644
--- a/scripts/convert_notebook.py
+++ b/scripts/convert_notebook.py
@@ -260,6 +260,27 @@ def replace(match: re.Match) -> str:
     return CARD_ITEM.sub(replace, text)
 
 
+def merge_output_blocks(text: str) -> str:
+    """Join consecutive output blocks separated only by blank lines.
+
+    Upstream splits a single stream of terminal output across several fences -- Keras
+    `.summary()` emits a title line and a table, and the source fences them separately. Rendered
+    as two boxes it reads as two results. Only `{ .no-copy .output }` blocks are merged: two
+    adjacent *code* blocks may legitimately be separate cells, but output is one stream.
+    """
+    pattern = re.compile(
+        r"^(?P<pad>[ \t]*)```[ \t]*\n"          # closing fence of the first block
+        r"(?:[ \t]*\n)+"                        # blank lines between
+        r"(?P=pad)``` \{ \.no-copy \.output \}[ \t]*\n",  # reopening
+        re.MULTILINE,
+    )
+    previous = None
+    while previous != text:                     # runs of 3+ blocks collapse in passes
+        previous = text
+        text = pattern.sub("", text)
+    return text
+
+
 def tighten_nested_lists(text: str) -> str:
     """Drop the blank line between a list item and its own nested list.
 
@@ -642,6 +663,7 @@ def convert(
     # Whole-document cleanups. Order matters: collapse the card grids into bullets before
     # tightening lists, so the generated bullets are tightened too.
     result = collapse_card_grids(result)
+    result = merge_output_blocks(result)
     result = tighten_nested_lists(result)
     result = embed_videos(result)
     result = center_card_header(result)
diff --git a/scripts/link_map.toml b/scripts/link_map.toml
index 92860e2..664ab3b 100644
--- a/scripts/link_map.toml
+++ b/scripts/link_map.toml
@@ -11,6 +11,7 @@
 # [ported] Target page exists today.
 # ------------------------------------------------------------------------------------------
 [ported]
+"" = "index.md"                                          # bare link to the docs root
 "/core" = "models/gemma-4/index.md"
 "/core/model_card_4" = "models/gemma-4/model-card.md"
 "/core/model_card_3" = "models/gemma-3/index.md"
@@ -82,7 +83,10 @@
 "/integrations/langchain" = "deployment/langchain.md"
 "/integrations/google-cloud" = "deployment/google-cloud.md"
 "/core/gke" = "deployment/google-cloud.md"               # GKE folded into the Google Cloud page
-"/mobile-actions" = "deployment/mobile-actions.md"
+"/integrations/vertex" = "deployment/google-cloud.md"    # Vertex AI likewise
+"/mobile-actions" = "tuning/mobile-actions.md"
+"/conversions/hf-to-mediapipe-task" = "deployment/litert.md"
+"/integrations/litert" = "deployment/litert.md"
 
 # Fine-tuning. Targets are existing stubs; Step 2.3 fills them in.
 "/tune" = "tuning/overview.md"
@@ -91,6 +95,8 @@
 "/core/huggingface_text_full_finetune" = "tuning/full-finetune.md"
 "/core/distributed_tuning" = "tuning/distributed.md"
 "/core/lora_tuning" = "tuning/keras-lora.md"
+"/lora_tuning" = "tuning/keras-lora.md"                   # short form used on some pages
+"/distributed_tuning" = "tuning/distributed.md"
 "/spoken-language/task-specific-tuning" = "tuning/spoken-language.md"
 
 # Learn
@@ -158,8 +164,20 @@
 "learn/personal-code-assistant.md#extension" = "vs-code-extension-project"
 "learn/personal-code-assistant.md#web-service" = "gemma-web-service-project"
 "tuning/spoken-language.md#compute_requirements" = "hardware-requirements"
+# tuning/overview.md carries its own contents list using devsite's short anchors.
+"tuning/overview.md#choose-framework" = "choose-a-framework"
+"tuning/overview.md#tune-and-test" = "tune-and-test-the-model"
+"tuning/overview.md#deploy-model" = "deploy-the-model"
+"models/gemma-4/index.md#sizes" = "parameter-sizes-and-quantization"
+"deployment/google-cloud.md#run" = "cloud-run"
 # The formatting guidance lives under this page's H1, not a section of its own.
 "get-started/prompt-structure.md#formatting-instruction" = "gemma-formatting-and-system-instructions"
+# google-cloud.md opens with its own contents list, using devsite's short anchors.
+"deployment/google-cloud.md#model-garden" = "gemini-enterprise-agent-platform"
+"deployment/google-cloud.md#gke" = "google-kubernetes-engine-gke"
+"deployment/google-cloud.md#adk" = "agent-development-kit-adk"
+"deployment/google-cloud.md#training-clusters" = "gemini-enterprise-agent-platform-training-clusters"
+"deployment/google-cloud.md#vllm-tpu" = "vllm-with-tpus"
 "models/gemma-3n/index.md#matformer" = "matformer-architecture"
 "models/gemma-3n/index.md#conditional-parameter" = "conditional-parameter-loading"
 "models/gemma-3n/index.md#parameters" = "model-parameters-and-effective-parameters"
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index b6f426f..7367d55 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -376,12 +376,17 @@ def check_fences(ctx: Context) -> Iterable[Finding]:
         if page.body.count("```") % 2:
             yield Finding("fences", page.repo_rel, 0, "unbalanced code fences")
 
-        # Two same-language blocks separated by nothing but blank lines is the signature of a
-        # block that got split -- usually by blank lines inside a notebook cell.
+        # Two adjacent *output* blocks separated by nothing but blank lines are one stream of
+        # terminal output that got split -- always safe to merge, and convert_notebook does.
+        #
+        # Deliberately not applied to code blocks: two adjacent `python` blocks are usually
+        # consecutive notebook cells, where the first happens to end with captured output.
+        # Merging those would be wrong. The blank-lines-inside-one-cell defect that motivated
+        # this check is fixed in the converter and pinned by a golden test.
         blocks = list(iter_code_blocks(page))
         body = page.body.splitlines()
         for (start_a, info_a, code_a), (start_b, info_b, _) in zip(blocks, blocks[1:]):
-            if info_a != info_b or not info_a:
+            if info_a != info_b or ".output" not in info_a:
                 continue
             end_a = start_a + len(code_a.splitlines()) + 1  # closing fence
             between = body[end_a - page.body_offset : start_b - page.body_offset - 1]
diff --git a/scripts/rewrite_links.py b/scripts/rewrite_links.py
index 6e6b765..7ee234d 100644
--- a/scripts/rewrite_links.py
+++ b/scripts/rewrite_links.py
@@ -49,9 +49,20 @@
 
 
 def slugify(heading: str) -> str:
-    """Mirror python-markdown's default toc slugify, which is what Zensical uses."""
-    value = re.sub(r"<[^>]+>", "", heading)                 # strip inline html
-    value = re.sub(r"[*`_\[\]]|\(.*?\)", "", value)         # strip inline markdown
+    """Mirror python-markdown's default toc slugify, which is what Zensical uses.
+
+    Order matters. Markdown syntax is reduced to its visible text first, then python-markdown's
+    own rule applies: drop everything that is not a word character, whitespace or hyphen, then
+    collapse runs to single hyphens.
+
+    Note what is *not* stripped: parentheses. "Google Kubernetes Engine (GKE)" becomes
+    `google-kubernetes-engine-gke`, keeping the GKE. An earlier version removed parenthesised
+    text wholesale and produced anchors that silently never matched.
+    """
+    value = re.sub(r"<[^>]+>", "", heading)              # inline html
+    value = re.sub(r"\[([^\]]*)\]\([^)]*\)", r"\1", value)  # [text](url) -> text
+    value = re.sub(r"[*`_]", "", value)                  # emphasis / code markers
+    value = re.sub(r"\{:?[^}]*\}", "", value)            # attr_list, e.g. {: #id }
     value = re.sub(r"[^\w\s-]", "", value).strip().lower()
     return re.sub(r"[-\s]+", "-", value)
 
diff --git a/scripts/sources.toml b/scripts/sources.toml
index 50dac37..0499d0f 100644
--- a/scripts/sources.toml
+++ b/scripts/sources.toml
@@ -32,6 +32,11 @@ path = "get-started/prompt-structure.md"
 source = "https://ai.google.dev/gemma/docs/core/prompt-structure.md.txt"
 frozen = true
 
+[[page]]
+path = "get-started/capabilities/vision.md"
+source = "https://ai.google.dev/gemma/docs/capabilities/vision.md.txt"
+frozen = true
+
 [[page]]
 path = "get-started/capabilities/mtp.md"
 source = "https://ai.google.dev/gemma/docs/mtp/overview.md.txt"
@@ -49,11 +54,65 @@ source = "https://ai.google.dev/gemma/docs/core/huggingface_inference.md.txt"
 assets = "huggingface"
 
 [[page]]
-path = "deployment/mobile-actions.md"
+path = "deployment/keras.md"
+source = "https://ai.google.dev/gemma/docs/core/keras_inference.md.txt"
+
+[[page]]
+path = "deployment/mobile.md"
+source = "https://ai.google.dev/gemma/docs/integrations/mobile.md.txt"
+
+[[page]]
+path = "deployment/web.md"
+source = "https://ai.google.dev/gemma/docs/integrations/web.md.txt"
+
+[[page]]
+path = "deployment/google-cloud.md"
+source = "https://ai.google.dev/gemma/docs/integrations/google-cloud.md.txt"
+
+[[page]]
+path = "deployment/langchain.md"
+source = "https://ai.google.dev/gemma/docs/integrations/langchain.md.txt"
+assets = "deployment"
+
+[[page]]
+path = "deployment/litert.md"
+source = "https://ai.google.dev/gemma/docs/conversions/hf-to-mediapipe-task.md.txt"
+
+[[page]]
+path = "tuning/mobile-actions.md"
 source = "https://ai.google.dev/gemma/docs/mobile-actions.md.txt"
 
 # --- Tuning / Learn ---------------------------------------------------------------------------
 
+[[page]]
+path = "tuning/overview.md"
+source = "https://ai.google.dev/gemma/docs/tune.md.txt"
+
+[[page]]
+path = "tuning/qlora.md"
+source = "https://ai.google.dev/gemma/docs/core/huggingface_text_finetune_qlora.md.txt"
+assets = "tuning"
+
+[[page]]
+path = "tuning/full-finetune.md"
+source = "https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetune.md.txt"
+assets = "tuning"
+
+[[page]]
+path = "tuning/vision-qlora.md"
+source = "https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora.md.txt"
+assets = "tuning"
+
+[[page]]
+path = "tuning/keras-lora.md"
+source = "https://ai.google.dev/gemma/docs/core/lora_tuning.md.txt"
+assets = "tuning"
+
+[[page]]
+path = "tuning/distributed.md"
+source = "https://ai.google.dev/gemma/docs/core/distributed_tuning.md.txt"
+assets = "tuning"
+
 [[page]]
 path = "tuning/spoken-language.md"
 source = "https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning.md.txt"
diff --git a/zensical.toml b/zensical.toml
index e0e2ec2..72a6e05 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -34,7 +34,11 @@ plugins = { macros = { enabled = true, include_yaml = { models = "data/models.ym
 nav = [
   { "Home" = "index.md" },
 
-  { "Get Started" = [
+  # ---------------------------------------------------------------------------
+  # Capabilities live here, not in their own tab: they are fundamental Gemma 4
+  # capabilities and belong where the full picture is visible at once.
+  # ---------------------------------------------------------------------------
+  { "Get started" = [
     "get-started/quickstart.md",
     { "Text" = [
       "get-started/capabilities/text.md",
@@ -42,134 +46,163 @@ nav = [
       "get-started/capabilities/function-calling.md",
     ]},
     { "Multimodal" = [
+      "get-started/capabilities/vision.md",
       "get-started/capabilities/image.md",
       "get-started/capabilities/video.md",
       "get-started/capabilities/audio.md",
     ]},
-    "get-started/prompt-formatting.md",
+    # MTP and the prompt-format references sit here so the main list stays the
+    # three things a newcomer needs: quickstart, text, multimodal.
+    { "Advanced" = [
+      { "Multi-Token Prediction" = "get-started/capabilities/mtp.md" },
+      "get-started/prompt-formatting.md",
+      { "Prompt structure (Gemma 1-3)" = "get-started/prompt-structure.md" },
+      { "Legacy setup" = "get-started/setup.md" },
+    ]},
   ]},
 
+  # ---------------------------------------------------------------------------
+  # Model-card centric on purpose. Grouping is presentation only -- the
+  # directories stay flat so a model's URL never changes when it is
+  # reclassified (see PLAN.md, "Open decision for Step 2.1").
+  # ---------------------------------------------------------------------------
   { "Models" = [
-    { "Core" = [
-      { "Gemma 4" = [
-        { "Overview" = "models/gemma-4/index.md" },
-        { "Model Card" = "models/gemma-4/model-card.md" },
-      ]},
-      { "Gemma 3" = "models/gemma-3/index.md" },
-      { "Gemma 2" = "models/gemma-2/index.md" },
-      { "Gemma" = "models/gemma-1/index.md" },
+    { "Overview" = "models/index.md" },
+    { "Gemma 4" = [
+      { "Overview" = "models/gemma-4/index.md" },
+      { "Model card" = "models/gemma-4/model-card.md" },
     ]},
     { "Variants" = [
-      { "Gemma3n" = [
+      { "Gemma 3n" = [
         { "Overview" = "models/gemma-3n/index.md" },
-        { "Model Card" = "models/gemma-3n/model-card.md" },
+        { "Model card" = "models/gemma-3n/model-card.md" },
       ]},
       { "DiffusionGemma" = [
         { "Overview" = "models/diffusiongemma/index.md" },
-        { "Model Card" = "models/diffusiongemma/model-card.md" },
-        { "Diffusion Explained" = "models/diffusiongemma/explained.md" },
-        { "Generate Output" = "models/diffusiongemma/inference.md" },
+        { "Model card" = "models/diffusiongemma/model-card.md" },
+        { "Diffusion explained" = "models/diffusiongemma/explained.md" },
+        { "Generate output" = "models/diffusiongemma/inference.md" },
       ]},
       { "FunctionGemma" = [
         { "Overview" = "models/functiongemma/index.md" },
-        { "Model Card" = "models/functiongemma/model-card.md" },
-        { "Formatting and Best Practices" = "models/functiongemma/formatting.md" },
-        { "Function Calling with HF" = "models/functiongemma/inference.md" },
-        { "Full Sequence" = "models/functiongemma/full-sequence.md" },
+        { "Model card" = "models/functiongemma/model-card.md" },
+        { "Formatting and best practices" = "models/functiongemma/formatting.md" },
+        { "Function calling with HF" = "models/functiongemma/inference.md" },
+        { "Full sequence" = "models/functiongemma/full-sequence.md" },
         { "Fine-tuning" = "models/functiongemma/fine-tuning.md" },
       ]},
       { "EmbeddingGemma" = [
         { "Overview" = "models/embeddinggemma/index.md" },
-        { "Model Card" = "models/embeddinggemma/model-card.md" },
-        { "Generate Embeddings" = "models/embeddinggemma/inference.md" },
+        { "Model card" = "models/embeddinggemma/model-card.md" },
+        { "Generate embeddings" = "models/embeddinggemma/inference.md" },
         { "Fine-tuning" = "models/embeddinggemma/fine-tuning.md" },
       ]},
       { "PaliGemma" = [
         { "Overview" = "models/paligemma/index.md" },
-        { "PaliGemma 1 Model Card" = "models/paligemma/model-card-v1.md" },
-        { "PaliGemma 2 Model Card" = "models/paligemma/model-card-v2.md" },
-        { "Prompting & System Instructions" = "models/paligemma/prompting.md" },
-        { "Generate Output with Keras" = "models/paligemma/inference.md" },
+        { "PaliGemma 1 model card" = "models/paligemma/model-card-v1.md" },
+        { "PaliGemma 2 model card" = "models/paligemma/model-card-v2.md" },
+        { "Prompting & system instructions" = "models/paligemma/prompting.md" },
+        { "Generate output with Keras" = "models/paligemma/inference.md" },
         { "Fine-tuning with JAX" = "models/paligemma/fine-tuning.md" },
       ]},
       { "ShieldGemma" = [
         { "Overview" = "models/shieldgemma/index.md" },
-        { "ShieldGemma Model Card" = "models/shieldgemma/model-card-v1.md" },
-        { "ShieldGemma 2 Model Card" = "models/shieldgemma/model-card-v2.md" },
+        { "ShieldGemma model card" = "models/shieldgemma/model-card-v1.md" },
+        { "ShieldGemma 2 model card" = "models/shieldgemma/model-card-v2.md" },
       ]},
     ]},
+    { "Previous generations" = [
+      { "Gemma 3" = "models/gemma-3/index.md" },
+      { "Gemma 2" = "models/gemma-2/index.md" },
+      { "Gemma" = "models/gemma-1/index.md" },
+    ]},
     { "Research" = [
       { "RecurrentGemma" = [
         { "Overview" = "models/recurrentgemma/index.md" },
-        { "Model Card" = "models/recurrentgemma/model-card.md" },
-        { "Generate Output with JAX" = "models/recurrentgemma/inference.md" },
+        { "Model card" = "models/recurrentgemma/model-card.md" },
+        { "Generate output with JAX" = "models/recurrentgemma/inference.md" },
         { "Fine-tuning with JAX" = "models/recurrentgemma/fine-tuning.md" },
       ]},
       { "DataGemma" = "models/datagemma/index.md" },
       { "Gemma Scope" = "models/gemma-scope/index.md" },
+      { "Gemma APS" = "models/gemma-aps/index.md" },
     ]},
     "models/releases.md",
   ]},
 
-  { "Tutorials" = [
-    { "A Visual Guide to:" = [
-      "tutorials/visual-guides/gemma-4.md",
-      "tutorials/visual-guides/gemma-4-12b.md",
-      "tutorials/visual-guides/mtp.md",
-      "tutorials/visual-guides/diffusiongemma.md",
-    ]},
-  ]},
-
-  { "Deployment" = [
-    { "Run Locally" = [
-      "deployment/ollama.md",
-      "deployment/llamacpp.md",
-      "deployment/lmstudio.md",
-      "deployment/mlx.md",
-      "deployment/litert.md",
-      "deployment/mobile.md",
-      "deployment/web.md",
+  # ---------------------------------------------------------------------------
+  # Split by intent -- where you run it -- rather than by vendor.
+  # ---------------------------------------------------------------------------
+  # Nav labels are short on purpose -- page H1s stay as written ("Run Gemma with
+  # Hugging Face Transformers"), but the sidebar shows just the engine name.
+  { "Run" = [
+    { "Overview" = "deployment/index.md" },
+    { "Local" = [
+      { "Ollama" = "deployment/ollama.md" },
+      { "llama.cpp" = "deployment/llamacpp.md" },
+      { "LM Studio" = "deployment/lmstudio.md" },
+      { "MLX" = "deployment/mlx.md" },
+      { "Hugging Face Transformers" = "deployment/huggingface.md" },
+      { "Keras" = "deployment/keras.md" },
+      { "LiteRT" = "deployment/litert.md" },
+      { "Web browsers" = "deployment/web.md" },
+      { "Mobile" = "deployment/mobile.md" },
     ]},
-    { "Cloud & Production" = [
-      "deployment/vllm.md",
-      "deployment/sglang.md",
-      "deployment/huggingface.md",
-      "deployment/google-cloud.md",
+    { "Serve" = [
+      { "vLLM" = "deployment/vllm.md" },
+      { "SGLang" = "deployment/sglang.md" },
+      { "Google Cloud" = "deployment/google-cloud.md" },
     ]},
     { "Frameworks" = [
-      "deployment/langchain.md",
+      { "LangChain" = "deployment/langchain.md" },
     ]},
-    "deployment/qat.md",
+    { "Quantization & QAT" = "deployment/qat.md" },
   ]},
 
-  { "Tuning" = [
-    "tuning/overview.md",
-    "tuning/qlora.md",
-    "tuning/full-finetune.md",
-    "tuning/vision-qlora.md",
-    "tuning/keras-lora.md",
-    "tuning/distributed.md",
+  { "Fine-tune" = [
+    { "Overview" = "tuning/overview.md" },
+    { "QLoRA" = "tuning/qlora.md" },
+    { "Full fine-tune" = "tuning/full-finetune.md" },
+    { "Vision QLoRA" = "tuning/vision-qlora.md" },
+    { "LoRA with Keras" = "tuning/keras-lora.md" },
+    { "Distributed" = "tuning/distributed.md" },
+    { "Spoken language" = "tuning/spoken-language.md" },
+    { "Mobile actions" = "tuning/mobile-actions.md" },
   ]},
 
-  { "Demos" = [
-    "demos/concurrent.md",
+  { "Learn" = [
+    { "Visual guides" = [
+      "tutorials/visual-guides/gemma-4.md",
+      "tutorials/visual-guides/gemma-4-12b.md",
+      "tutorials/visual-guides/mtp.md",
+      "tutorials/visual-guides/diffusiongemma.md",
+    ]},
+    { "Tutorials" = [
+      "learn/business-email-assistant.md",
+      "learn/personal-code-assistant.md",
+    ]},
+    { "Notebooks & resources" = "cookbook.md" },
   ]},
 
-  { "Cookbook" = "cookbook.md" },
+  # ---------------------------------------------------------------------------
+  # Absorbs the old Tools tab: things we built, plus things we embed.
+  # ---------------------------------------------------------------------------
+  { "Demos" = [
+    { "Interactive tools" = [
+      { "Overview" = "tools/index.md" },
+      "tools/vram-calculator.md",
+      "tools/model-selector.md",
+    ]},
+    { "Apps & Spaces" = [
+      "demos/concurrent.md",
+    ]},
+  ]},
 
-  { "Community" = [
+  { "Ecosystem" = [
     "community/gemmaverse.md",
     "community/discord.md",
     "community/gemma-skills.md",
-  ]},
-
-  { "Tools" = [
-    "tools/vram-calculator.md",
-    "tools/model-selector.md",
-  ]},
-
-  { "Contributing" = [
-    "contributing/components.md",
+    { "Contribute" = "contributing/components.md" },
   ]},
 ]
 

From 7e4dee005a630c0b12a212b98b70f37959f9dafb Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Tue, 4 Aug 2026 11:28:01 +0200
Subject: [PATCH 11/20] Add visual guides

---
 .lintbaseline.json                            |  12 +-
 docs/get-started/capabilities/mtp.md          |   4 +
 docs/tuning/mobile-actions.md                 |   3 -
 .../tutorials/visual-guides/diffusiongemma.md | 364 +++++++++++++++++-
 docs/tutorials/visual-guides/gemma-4-12b.md   | 149 ++++++-
 docs/tutorials/visual-guides/gemma-4.md       | 329 +++++++++++++++-
 docs/tutorials/visual-guides/mtp.md           | 107 ++++-
 scripts/import_newsletter.py                  | 259 +++++++++++++
 scripts/lint_docs.py                          |   7 +
 zensical.toml                                 |  12 +-
 10 files changed, 1220 insertions(+), 26 deletions(-)
 create mode 100644 scripts/import_newsletter.py

diff --git a/.lintbaseline.json b/.lintbaseline.json
index e827330..9fddc66 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -13,7 +13,7 @@
     "modeldata",
     "css"
   ],
-  "count": 95,
+  "count": 87,
   "known": [
     "frontmatter|docs/community/discord.md|no frontmatter block",
     "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
@@ -94,10 +94,6 @@
     "frontmatter|docs/tuning/qlora.md|no frontmatter block",
     "frontmatter|docs/tuning/spoken-language.md|no frontmatter block",
     "frontmatter|docs/tuning/vision-qlora.md|no frontmatter block",
-    "frontmatter|docs/tutorials/visual-guides/diffusiongemma.md|no frontmatter block",
-    "frontmatter|docs/tutorials/visual-guides/gemma-4-12b.md|no frontmatter block",
-    "frontmatter|docs/tutorials/visual-guides/gemma-4.md|no frontmatter block",
-    "frontmatter|docs/tutorials/visual-guides/mtp.md|no frontmatter block",
     "stubs|docs/community/discord.md|stub: 46 chars of content (minimum 400)",
     "stubs|docs/community/gemma-skills.md|stub: 62 chars of content (minimum 400)",
     "stubs|docs/community/gemmaverse.md|placeholder link: example.com",
@@ -105,10 +101,6 @@
     "stubs|docs/demos/concurrent.md|stub: 52 chars of content (minimum 400)",
     "stubs|docs/index.md|stub: 0 chars of content (minimum 400)",
     "stubs|docs/tools/index.md|stub: 144 chars of content (minimum 400)",
-    "stubs|docs/tools/model-selector.md|stub: 58 chars of content (minimum 400)",
-    "stubs|docs/tutorials/visual-guides/diffusiongemma.md|stub: 50 chars of content (minimum 400)",
-    "stubs|docs/tutorials/visual-guides/gemma-4-12b.md|stub: 54 chars of content (minimum 400)",
-    "stubs|docs/tutorials/visual-guides/gemma-4.md|stub: 49 chars of content (minimum 400)",
-    "stubs|docs/tutorials/visual-guides/mtp.md|stub: 66 chars of content (minimum 400)"
+    "stubs|docs/tools/model-selector.md|stub: 58 chars of content (minimum 400)"
   ]
 }
diff --git a/docs/get-started/capabilities/mtp.md b/docs/get-started/capabilities/mtp.md
index 63faa47..ba79937 100644
--- a/docs/get-started/capabilities/mtp.md
+++ b/docs/get-started/capabilities/mtp.md
@@ -32,6 +32,10 @@ improving reuse of loaded weights. At batch size 1 this overlap is
 limited, which is why the 26B A4B drafter may not yield speedups on
 hardware platforms without good parallelism.
 
+!!! tip
+    For an illustrated walkthrough, see
+    [A Visual Guide to Multi-Token Prediction](../../tutorials/visual-guides/mtp.md).
+
 ## MTP Enhancements
 
 Gemma 4 introduces several enhancements to the standard speculative
diff --git a/docs/tuning/mobile-actions.md b/docs/tuning/mobile-actions.md
index ce4922d..b2136f7 100644
--- a/docs/tuning/mobile-actions.md
+++ b/docs/tuning/mobile-actions.md
@@ -1,8 +1,5 @@
 # Fine-tune FunctionGemma 270M for Mobile Actions
 
-
-## Overview
-
 Mobile Actions is a demo app where users can trigger actions on their device
 from voice or text input. It reimagines assistant interaction as a fully offline
 capability. Whether it's "Create a calendar event for lunch tomorrow", "Add John
diff --git a/docs/tutorials/visual-guides/diffusiongemma.md b/docs/tutorials/visual-guides/diffusiongemma.md
index 1191cfd..079ddef 100644
--- a/docs/tutorials/visual-guides/diffusiongemma.md
+++ b/docs/tutorials/visual-guides/diffusiongemma.md
@@ -1,3 +1,363 @@
-# DiffusionGemma
+---
+title: A Visual Guide to DiffusionGemma
+description: A Visual Guide to DiffusionGemma — a visual walkthrough.
+source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-diffusiongemma
+---
 
-A visual guide to diffusion-based text generation.
+# A Visual Guide to DiffusionGemma
+
+!!! note
+    Originally published on
+    [Exploring Language Models](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-diffusiongemma).
+
+Following the release of [Gemma 4 12B](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4-12b) there is yet another innovative model that I can introduce to you all, [DiffusionGemma](https://huggingface.co/google/diffusiongemma-26B-A4B-it)!
+
+This is one I’m especially excited about because it’s a different way of approaching text generation, yet uses many of the great things we have with existing “regular” LLMs. We are going to explore the old, the new, and how they live side-by-side.
+
+![](https://substackcdn.com/image/fetch/$s_!Pj17!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F362fd6ee-d939-4310-b2b9-f4a8e4ff293a_2536x2964.png)
+
+In this guide, I’m going through a lot of things because there is so much that makes DiffusionGemma special! You will learn more about diffusion in general, how it works for discrete text, the architecture of DiffusionGemma, and all of the interesting techniques used.
+
+Get ready, this is going to be a fun one to explore ;)
+
+## The Problem with Autoregressive Models
+Autoregressive Large Language Models generate one token at a time and are great for serving many users at once. Why? Because generating a single token for a single user is surprisingly cheap in terms of computation. In each step, a significant portion of the time to generate a token is spent loading the weights from memory and not the actual computing.
+
+![](https://substackcdn.com/image/fetch/$s_!ebuj!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F107afcba-fc4e-4cf7-aca9-d75999dde956_1960x1356.png)
+
+During decoding, these regular Transformer models are memory bound rather than compute bound. This means that, for instance, the time it takes to generate one token is the same for one user as it is for 256 users.
+
+The reason for this is that the weights only need to be loaded from memory once per step, regardless of how many users are being served. This means that whether we multiply those weights against 1 user’s vector or those of 256 users, the cost of moving the weights into memory stays the same. By batching all users together, we get the same amount of work for nearly the same cost.
+
+This is not a free lunch though, after a certain point (which depends on the chip itself), the LLM might be serving too many users and lacks compute instead. You can visualize this tradeoff with a roofline plot. It shows the impact of increasing the chip’s performance (how long it needs to spend to generate a token) versus the chip’s memory (how fast it can load data from memory).
+
+When only serving one user (batch size of 1), the LLM is memory-bound and loads a large number of weights without actually doing much computation. The more we increase the batch size (same memory load but more computation), you approach a “ride point” where the hardware is properly utilized.
+
+Thus, while batching is great for serving many users at once, there is nothing to be gained for the individual user! It still receives their tokens with the same speed. No difference in latency.
+
+**DiffusionGemma** flips the script.
+
+What if we used the compute’s idle time for a single user instead? Instead of generating 1 token each for 256 users, what if we generated 256 tokens at once for 1 user?
+
+This is the core idea behind **DiffusionGemma**! The model starts with a sequence of 256 randomly initialized tokens (called a canvas) and tries to choose better tokens for the entire canvas all at the same time. By predicting 256 tokens at the same time, the compute budget of 256 tokens is now focused on a single user instead of spreading it across many users.
+
+![](https://substackcdn.com/image/fetch/$s_!S_4q!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5ea25278-3d55-4675-8606-87117a627af9_2808x1108.png)
+
+Of course, predicting so many tokens at once is very difficult and the model will have a hard time predicting token 254 without knowing which tokens came before it. Note how the first two tokens are great but it slowly starts to output nonsense. A first pass of **DiffusionGemma** on this canvas tends to produce reasonably accurate tokens at the start of the canvas but poor ones towards the end.
+
+![](https://substackcdn.com/image/fetch/$s_!7peL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0d56e1a1-017a-46fd-a040-e73ee9a00136_1980x736.png)
+
+So instead of doing a single pass, what happens if we simply perform multiple passes?
+
+This is where iterative refinement comes in. The model takes another pass over the canvas using its previous predictions. Correct tokens (those that still have high probabilities) help the model make better guesses further into the canvas. With each pass, the model improves tokens that previously had low probabilities or were surrounded by inaccurate tokens. After a number of iterations, the canvas then converges to a text of similar quality to that of regular Transformer models, just much much faster for a single user. The main reason for this is that the model does much fewer forward passes than the number of tokens it generates.
+
+![](https://substackcdn.com/image/fetch/$s_!X3BM!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0ba96272-c727-4fed-97a5-b5af713f9ed3_2252x1572.png)
+
+See how we just went from next-token prediction on regular Transformer models to 256 tokens all at once!
+
+Although both use a number of steps to generate the output, a regular Transformer uses one step per token whilst DiffusionGemma uses each step to improve upon a canvas. What makes this possible is a technique called **diffusion** which became popular in the field of image generation. It is closely related to the concept of iterative refinement.
+
+Instead of the model being **memory-bound** (like the Autoregressive LLM), the Diffusion LLM is **compute-bound** instead and scales more quickly as you add more compute.
+
+![](https://substackcdn.com/image/fetch/$s_!a49i!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5babf96a-1fb7-4e9a-9d24-2c7596fdde07_1960x1036.png)
+
+What you get is the main difference between Autoregressive and Diffusion LLMs, namely the way they process the same tokens. Autoregressive LLMs generate one token each forward pass and are memory-bound when serving a single user whereas Diffusion LLMs generate a sequence of tokens each forward pass and are compute-bound.
+
+![](https://substackcdn.com/image/fetch/$s_!Rx67!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fde16287d-e8ec-4b21-b6fc-ddd1ba4d7230_2048x995.png)
+
+So let’s explore diffusion in more detail and how **DiffusionGemma** took that principle for image generation and applied it to text generation.
+
+## What is Diffusion?
+Diffusion is a process in image generation which centers around “noise”.
+
+### Diffusion for Images
+Diffusion is a process in image generation which centers around removing “noise” from an image. Starting with a randomly initialized image (100% “noise”), diffusion attempts to reduce the amount of noise with each subsequent step. In image generation, this process is guided by a prompt. After enough steps, it will eventually generate or “uncover” an image.
+
+![](https://substackcdn.com/image/fetch/$s_!-JSR!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd7e67dfa-d338-4104-9fa4-c1d426bf6252_2048x495.png)
+
+The process of reducing the amount of noise in an image is called **denoising**. As you can see from the figure, this is an iterative and sequential process. With each step, you can see the image a little bit better and by the end, the full image is shown. This principle is not only central to image diffusion but as you will see, also to DiffusionGemma.
+
+This process of denoising is guided by two main principles, **forward diffusion** and **reverse diffusion**.
+
+#### Forward Diffusion
+To have the model learn how to denoise an image, you will have to start with some training data. This data contains image/text pairs. For each pair, you add a certain amount of random (Gaussian) noise to the image.
+
+![](https://substackcdn.com/image/fetch/$s_!F4kj!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1a5473c5-ab79-4c00-b91e-c87b5b909926_2048x1017.png)
+
+This process is called **forward diffusion**. It creates new data from existing data and adds a signal to the training process.
+
+#### Reverse Diffusion
+**Reverse Diffusion**, on the other hand, attempts to train a model to predict the noise we added to the initial data. This training data then has a clear signal to predict: the noise. You are essentially telling the model, here is a noisy image and from that I want you to only predict the noise.
+
+![](https://substackcdn.com/image/fetch/$s_!FMg7!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd68b8bb2-c8f0-42f7-9326-82ede7ecf4ef_2048x540.png)
+
+So why would we want to introduce noise into the images and then attempt to predict it?
+
+Well, we can subtract the predicted noise from the noisy image to get it closer to the images that the model was trained on. By doing this iteratively, you can start with complete noise and slowly denoise it until there is no more noise left in the image.
+
+![](https://substackcdn.com/image/fetch/$s_!UDzT!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0946ce1b-5b45-473e-99fd-48c21c5fe235_2048x1306.png)
+
+Together, **forward diffusion** (*generating noisy training data*) and **reverse diffusion** (*denoising noisy input data*) create this process of breaking and fixing an image.
+
+![](https://substackcdn.com/image/fetch/$s_!Rt9o!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F469fd11d-6664-49e2-8e74-c542ecd3ff72_2048x521.png)
+
+Note that in image diffusion this process is guided by a prompt which serves as an extra signal to decide what is and what isn’t noise. Without something to guide it, the model has no idea what it should create in the first place.
+
+What guides this process is called the encoder and serves to process the input query and understand the semantic meaning behind it. The output of the encoder is then passed to the diffusion model so it is guided to create the image in your query. Without the encoder, the diffusion model would have no idea what to create from the 100% noise it receives at the first step.
+
+Together, they create the full pipeline which consists of two main architectures:
+
+- **Denoiser** – The model used to iteratively remove noise from the noisy input (e.g., a diffusion model).
+- **Encoder** – The model used to process the input query (e.g., an encoder language model).
+
+![](https://substackcdn.com/image/fetch/$s_!9csP!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F62e96b13-a4e5-4a84-9207-dd4b89bd509b_2048x1788.png)
+
+As shown before, the diffusion model is also called the **denoiser** since it focuses primarily on removing noise from a noisy input. We are going to be using this term throughout to showcase the purpose of the model.
+
+### Diffusion for Text
+Now that we have seen diffusion for images, how can that be translated to text? It is easier to add individual noise to pixels as those are continuous values. You can make a red pixel a little bit less red and a little bit more blue. However, how do you make the token “The” a little bit less “The”? What constitutes “noise” for a single token?
+
+#### Masked Diffusion
+Instead of looking at them as individual tokens, let’s consider the whole. In masked language modeling, like is done when training encoder models like BERT, random tokens in the input are replaced with the **[MASK]** token. We can consider this **[MASK]** token to be “noise” (also called corrupted tokens).
+
+The procedure then becomes similar to what we have seen in Diffusion for Images, you sample a given text, pick an amount of noise, and randomly mask tokens based on the amount of noise.
+
+![](https://substackcdn.com/image/fetch/$s_!r3Ct!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F09c757cc-6f3e-4b5d-a3c8-dc4d94a031fd_2048x978.png)
+
+When we do this multiple times, we can create a training dataset. You sample the same text (or different ones) and add different amounts of noise. This allows the model to learn how to denoise different amounts of noise, just like with Diffusion for images.
+
+![](https://substackcdn.com/image/fetch/$s_!T7pI!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9b586fd7-907e-4e69-8705-1ab0f59b252b_2048x1106.png)
+
+This **forward diffusion** process is then followed by **reverse diffusion**, which would have the model predict what the correct token should be at the [MASK] position. At each step, the model denoises only the tokens for which it is confident it has a good replacement.
+
+![](https://substackcdn.com/image/fetch/$s_!kUTj!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc16cda6a-c481-42ad-bebc-6a40635346c9_2004x1368.png)
+
+During a single denoising step, the model predicts the most probable token at each canvas position. Then, only the tokens that exceed a certain threshold will get chosen. The ones that do not meet the threshold will remain a masked token.
+
+![](https://substackcdn.com/image/fetch/$s_!ukgK!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3006380d-d9f5-4488-8a2e-f25cf099522f_2048x1666.png)
+
+This process continues until all masked tokens are replaced or until a certain number of steps are reached. Generally, the more steps you use the more accurate the resulting sequence will be. Instead of coming to all tokens at once, it can use several steps to decide which tokens should go where.
+
+Like with Diffusion for images, we can visualize the forward and reverse masked diffusion process in one figure:
+
+![](https://substackcdn.com/image/fetch/$s_!e6sb!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fae9d9bb5-d62e-4809-80f8-3c6ab47ffc8c_2048x1389.png)
+
+Much like diffusion for image, diffusion for text generally consists of two models (or two techniques) used in conjunction:
+
+- **Denoiser** – The model used to iteratively remove noise from the noisy input (more on this in the “*Architecture of DiffusionGemma”* section)
+- **Encoder** – The model used to process the input query (e.g., an encoder language model).
+
+![](https://substackcdn.com/image/fetch/$s_!j_ra!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F92f0dfcc-611d-41b4-849a-1550930af99e_2048x1428.png)
+
+There is one problem though with masked diffusion and that is self-correction! Whenever the model decides to replace a **[MASK]** token, it is fixed. Once chosen it cannot be replaced. This hinders its potential to correct some of the tokens it might have chosen too early. It is actually similar behavior to autoregressive models that likewise choose a token and are “stuck” with the selected token.
+
+![](https://substackcdn.com/image/fetch/$s_!Q-gl!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa57e669f-8f5c-4e0c-94da-2a95e52da98c_2048x476.png)
+
+Instead, what if we were to explore a different type of “noise” instead?
+
+#### Uniform State Diffusion
+Using a **[MASK]** token is a rather “strict” form of defining noise. It either is there or it isn’t, with nothing in between. When compared with image diffusion this is a rather “boolean” way of looking at text. Moreover, when a token is selected to replace a given **[MASK]** token, it cannot be replaced again.
+
+Instead of corrupting a sequence of tokens by replacing them with the same thing (a **[MASK]** token) we can replace each token with something different (a **random** token). In the forward diffusion process, random tokens are used as noise to create a dataset the same way you would do with masked diffusion.
+
+![](https://substackcdn.com/image/fetch/$s_!t6W1!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc94b255-a3c8-43c5-98fe-8fb14eebd163_2048x929.png)
+
+This process creates a dataset with noise but without making it explicit where the noise is (aside from having the ground truth). What makes this so different from masking is that the model has to figure out where the noise is coming from and adjust accordingly. Whether there is 10% of replaced words or 90% requires a thorough understanding of the input.
+
+In the denoising process (reverse diffusion), the model has to detect which tokens are noise and which should be updated. At each denoising step, the model predicts a token for each location in the canvas. If a token shouldn’t change, it will keep the high probability. However, if it decides to replace that token, another token with a high probability will be generated instead. As such, the model continues to predict the best token for each position. This also means that a token that might have gotten a high probability in steps 1 through 10 might suddenly get a low probability in step 11 as the canvas gets updated.
+
+Therefore, at each position in the canvas, the model suggests a token with a certain probability. If it reaches a certain threshold, it will be put in that position. However, if it does not meet the threshold, the tokens that were there previously will be re-noised and replaced with a random token.
+
+![](https://substackcdn.com/image/fetch/$s_!CBjv!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8ba6e40b-b9ef-47be-8671-349ff9db1437_2048x1832.png)
+
+So why would you re-noise low probability tokens?
+
+If you kept the old token, then that wouldn’t be a uniformly random token. The model has learned during training that the canvas would consist of partially noised tokens that were drawn following a uniform distribution. By keeping the old one, the model might start planning around the wrong token in the next step. To stay close to the training data, and for practical reasons, you re-noise low probability tokens.
+
+Together, **forward diffusion** (*generating noisy text sequences*) and **reverse diffusion** (*denoising random tokens*) create this process of breaking and fixing a sequence of tokens.
+
+![](https://substackcdn.com/image/fetch/$s_!Op0I!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7e5a2dd0-bf1d-404a-abf1-5478fc844f9c_2048x1389.png)
+
+As a result, the model does not stop when it replaces a random token with another token. It can still change any token in the canvas. Compared to Masked Diffusion, this allows the model to continuously update and change the canvas as it goes through steps.
+
+![](https://substackcdn.com/image/fetch/$s_!-TNX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F87b58fb6-ffae-4c66-9577-71f3fe64400b_1823x2048.png)
+
+Uniform-state diffusion, and text-based diffusion in general, as such is quite different from autoregressive models. Where autoregression is predicting one token at a time sequentially, diffusion iteratively updates a canvas. This difference between autoregression and diffusion is especially pronounced when we put them side-by-side.
+
+![](https://substackcdn.com/image/fetch/$s_!LA73!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbb5c79a3-1e56-4a18-91d4-6bded1411e99_2048x2017.png)
+
+These differences are important because they help you understand when you might choose a text diffusion model over an autoregressive one and vice versa. Although text diffusion models excel at generating many tokens for a single user, they have lower multi-user throughput than their autoregressive counterparts. As such, there is no free lunch with text diffusion. Here’s an overview of some of the fundamental differences between them:
+
+![](https://substackcdn.com/image/fetch/$s_!Pkw8!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F34dd0f11-8d62-4ab1-b3cf-29c6655c4688_1942x1062.png)
+
+One downside to note of text diffusion, and uniform state diffusion in particular, is that they are difficult to train. The reason for that is that the training objective does not only need to denoise canvases at various noise levels, it also needs to first identify which tokens are actually noise in the first place. Otherwise, it would just consider all tokens in the canvas to be noise, which is not ideal.
+
+So how can we have the benefits of Uniform State Diffusion without the downsides of training? Let’s explore the solution in the Encoder-Decoder patch of DiffusionGemma!
+
+## Architecture of DiffusionGemma
+**DiffusionGemma** applies Uniform State Diffusion that we explore thus far. However, the downsides of training this entirely from scratch still remains. The solution was simply not to train from scratch but to use an existing checkpoint as a start instead, namely the Gemma 4 26B A4B model.
+
+Gemma 4 26B A4B, as covered in [A Visual Guide to Gemma 4](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4), is a Mixture of Experts (MoE) model that already went through extensive training and has great performance. There is one problem though…
+
+Gemma 4 26B A4B is a *decoder-only* model and meant to generate text one by one whilst diffusion generally needs an *encoder* and a *denoiser*. This is where an elegant solution comes in, the **Encoder-Denoiser** patch!
+
+This patch is a way to convert a single decoder-only model into a:
+
+- **Encoder** – Used for “understanding” the query
+- **Denoiser** – Used for denoising the canvas
+
+It does so by having a single model (Gemma 4 26B A4B) dynamically switch between a *denoiser mode* and an *encoder mode*.
+
+### Denoiser Mode - Act like an Encoder
+To convert a decoder-only model (Gemma 4 26B A4B) into a denoiser, we can make use of something it is not directly using when generating tokens, namely the logits of all tokens!
+
+Specifically, an autoregressive LLM first converts the text into token embeddings (a set of 1-dimensional numerical values). As those token embeddings flow through the LLM, they get processed and updated continuously. This is often referred to as the hidden states of the model. The final hidden states are projected to logits which represent a confidence score for each word in its vocabulary. This means that for each token in the input, a set of confidence scores are generated. However, only logits of the last hidden state are used to choose the predicted token. All other logits are essentially thrown away!
+
+![](https://substackcdn.com/image/fetch/$s_!3H0u!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0d01d955-e5a5-4150-b76b-22cc81a01d4e_1915x2048.png)
+
+This “throwing away of logits” is not necessarily a bad thing considering the logits of the final token contains all information about the previous tokens. The reason why we only use the final is because each token in a sequence can only attend to (“see”) to the tokens before it. So the hidden state of the final token is an aggregation of the entire sequence, which you need to predict the next word.
+
+![](https://substackcdn.com/image/fetch/$s_!gKcp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcc17e2a9-6261-4e30-928e-b3986c22aabd_2048x1444.png)
+
+This leads to an interesting idea. What if we were to replace two things:
+
+- The input sequence of tokens with a canvas
+- The causal attention with bidirectional attention
+
+Since the model should work as a denoiser, we expect the input tokens to be a noisy canvas instead. With causal attention, however, that would still mean that the logits at each position only attend to information that came before it. Since we want to generate a sequence of tokens all at once, we do not want a given token to be unaware of the tokens that come after it, noisy or otherwise. We instead have to replace causal attention with bidirectional attention. This makes it so that a token can attend to all other tokens in the sequence, regardless of their relative position.
+
+![](https://substackcdn.com/image/fetch/$s_!F_I2!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc26262af-d9f1-4fe8-8775-f6dc04f55b59_1927x2048.png)
+
+With bidirectional attention, we can now use the logits of all tokens in the input noisy canvas as all tokens attend to one another. For each position in the noisy canvas, the best matching token is selected. If that token has a high probability, it is accepted but if it still has a low probability then it is re-noised and replaced with a different random token.
+
+When you replace an attention mechanism, it doesn’t just work out of the box. Gemma 4 26B A4B has been trained with causal attention, so it will get confused if it can suddenly use bidirectional attention. That is something you will have to train, but more on that later.
+
+With a noisy canvas as input and bidirectional attention, we can use Gemma 4 26B A4B as the denoiser iteratively updating a canvas. However, without an input query, there is no way for the model to know how to fill in the canvas. That’s where the encoder comes in.
+
+### Encoder Mode - Acts like a Decoder
+The encoder mode of Gemma 4 26B A4B, like the encoder in image diffusion, is meant for processing the input query and providing context and guidance to the denoiser. Normally, you would use an encoder (LLM with bidirectional attention) to process the input. However, Gemma 4 26B A4B was trained with causal attention and if we were to replace it again, that would be rather costly. Instead, we can use Gemma 4 26B A4B as is to perform the query processing. This Gemma 4 26B A4B model isn’t used natively but fine-tuned to support the different tasks of denoising and encoding.
+
+In this process, we do not use the LM Head since we are not generating tokens, but token representations instead. These representations generally come in two ways, either the hidden states or the KV cache.
+
+However, using the hidden states would require additional cross-attention which is yet another patch. Since the encoder mode and decoder mode share the exact same model, with different attention mechanisms, DiffusionGemma shares the KV cache instead between the encoder mode and the decoder mode.
+
+![](https://substackcdn.com/image/fetch/$s_!F1ci!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8c9499d5-dfda-4b1b-ab1d-195dcc7dca65_2048x1463.png)
+
+Another reason is that even though the Encoder is a Decoder model with causal attention, it can still generate great representations of the input query. As such, these KV-cache representations are shared with the Denoiser (Gemma 4 26B A4B with bidirectional attention) so that it understands what the user actually wants it to generate. The KV-cache is not updated throughout the denoising steps of the Denoiser.
+
+## Inference of DiffusionGemma
+A big part of diffusion models is how they handle inference. Specifically, the canvas is not generated in a single step but iteratively updated. This results in several questions we have yet to answer:
+
+- How does the model know about predictions it made in the previous step?
+- How do you generate a sequence that is larger than the canvas?
+- How many denoising steps does the denoiser use?
+
+### Self-Conditioning
+The first question that we answer is “*How does the model know about predictions it made in the previous step?*”.
+
+The answer, fortunately, is quite straightforward. After the denoiser has run a single step it can use the probabilities that it generated and pass it along to the second step. This gives the model information about what it intended to do the previous turn and where it came from.
+
+In practice, it does so by multiplying the probabilities after the softmax with the embeddings of all tokens, also called the embedding matrix or embedding table. This multiplication results in one embedding per position in the canvas that represents this probability distribution. As such, it puts more weight on the embeddings of more probable tokens. They are then passed to a small feedforward neural network (FFNN) and added to the token embeddings in the next step.
+
+This essentially gives it a memory of what it attempted to do in the previous step and how confident it was when predicting certain positions.
+
+![](https://substackcdn.com/image/fetch/$s_!dI2O!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7abf126b-482c-41c8-9319-b0ab17f6409b_1890x2048.png)
+
+When you put everything together, the second step is exactly the same as the first step and re-uses the KV-cache of the encoder. This self-conditioning on the previous step can be seen as a skip-step where information is propagated to the next step so it can continue the process with more context.
+
+We now have the full architectural overview of DiffusionGemma:
+
+![](https://substackcdn.com/image/fetch/$s_!igIE!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9bb3f537-9f60-40f6-b958-f8ecea60ac42_1961x2048.png)
+
+### Multi-Canvas Sampling (Block Diffusion)
+The second question that we need to answer is “*How do you generate a sequence that is larger than the canvas?*”.
+
+The canvas in DiffusionGemma has a size of 256 tokens, which isn’t all that big! Fortunately, we can mix and match diffusion with autoregression together to expand the number of tokens that are generated.
+
+Specifically, we first generate the 256 tokens using DiffusionGemma as we explored thus far. Those 256 tokens only need to be passed through the encoder once to generate the KV cache after which the denoiser takes a number of steps to fill up this canvas. When it is finished, the prompt is updated with the new 256 tokens and added to the input sequence of the encoder to extend the KV cache. The denoiser then continues to create another sequence of 256. This process continues until the denoiser generates a stop token.
+
+As such, the canvases are generated using diffusion but are stitched together sequentially. This means that we are alternating diffusion with autoregression!
+
+![](https://substackcdn.com/image/fetch/$s_!TJPo!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F115ddfd7-1771-4b08-933a-e564f9e29018_2048x699.png)
+
+Looking back at what at the figures we used to compare diffusion with autoregression, they are now combined into a process where each diffusion output is appended together in an autoregressive way:
+
+![](https://substackcdn.com/image/fetch/$s_!wC4f!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc3b15f06-0c7d-49f5-a189-72dd077229fd_2048x1176.png)
+
+A big part of what makes this possible is that the KV cache for the encoder is calculated using causal attention. Since each token only attends to what came before it, only the KV cache needs to be calculated for the canvases that are added in each “autoregressive step”. The result is a relatively cheap operation since we can continuously *update* the KV cache rather than having to *recalculate* it.
+
+A big part of what makes this possible is that the KV cache for the encoder only needs to be calculated at the start of every “autoregressive step”. During a “diffusion step”, the KV cache is reused and does not need to be recalculated since no new tokens are added until the end of diffusion.
+
+### The Scheduler
+The scheduler in image Diffusion controls how much noise to remove at each denoising step and how many steps to take. In DiffusionGemma, it is a collection of techniques that together decide how the step-based process of denoising works. It “schedules” the denoising process and doesn’t decide directly which tokens to reject or accept. In contrast to image Diffusion, the scheduler in DiffusionGemma consists of three separate components working together.
+
+First, the **step count**. This decides how many maximum denoising steps there are. More steps generally means higher quality output but slower generation.
+
+![](https://substackcdn.com/image/fetch/$s_!nAdT!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F56533f63-e39a-4b42-a3ee-48c18c7dfdb0_1528x1296.png)
+
+Second, the **logits scheduler**. After the model generates its raw logits, they are converted to probabilities via softmax. From those probabilities, it is now always the most probable token that is selected. It selects the token based on a multinomial distribution. This means that tokens with high probability have a higher chance of being selected and vice versa.
+
+![](https://substackcdn.com/image/fetch/$s_!3RVq!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffc7d5d0e-8f03-4a8c-b7e6-bddb838f8efa_2048x1615.png)
+
+There is a problem though with the generated logits, namely that they tend to be more uncertain. The reason for this is that at each step in the denoising process, there are noisy tokens in the canvas which might hurt the predictability (and therefore the logits) of each position.
+
+The logits scheduler makes sure that the model’s predictions (the logits) are more decisive. It does so by dividing the logits by a temperature value. This value is the same as you see in Autoregressive LLMs.
+
+![](https://substackcdn.com/image/fetch/$s_!HPmh!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4b74e872-6e5c-42d2-b3f4-f78fd0c98738_2048x402.png)
+
+Note that the steps count down instead to make sure that at each denoising step has a lower temperature:
+
+![](https://substackcdn.com/image/fetch/$s_!aSaG!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa8d400f5-475c-4c31-90a3-866ff7e0ee62_2048x519.png)
+
+Dividing the logits by the temperature at each step helps make the logits distribution more distinct or “peaky“. Since the early canvas is mostly noise, sharpening predictions will have little meaning anyway. Only at later steps does the model have a cleaner canvas and we can sharpen the predictions.
+
+![](https://substackcdn.com/image/fetch/$s_!YCxq!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc419bfba-a98d-45bb-b3b0-5d5e86879e0a_2048x1044.png)
+
+This means that in early steps, the model will explore a bit more and not just focus on the most probable tokens. Whereas in later steps, the model will explore less and focus more on the most probable tokens. It enforces a balance between exploration (early steps) and exploitation (later steps).
+
+![](https://substackcdn.com/image/fetch/$s_!Ipw7!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F10dde2aa-ef11-4d1e-bb0f-d42c65d4e0d8_2048x1782.png)
+
+As such, we can see the earlier steps as a bit more “noisy” with respect to token distributions compared to later steps.
+
+Third, **adaptive stopping**. Although the step count already defines the maximum number of steps the denoising process can take, the model might converge early on. For that, there is the adaptive stopping mechanism. At every step, it checks:
+
+- **Stability** – Have the highest-probability token predictions been identical for the last *N* steps?
+- **Confidence** – How confident is the model across all tokens in the canvas?
+
+In the configuration of DiffusionGemma, the confidence threshold is 0.005 and the stability threshold is 1. Confidence is measured by how spread out the model’s predictions are, which is referred to as entropy. In a given canvas position, if a token’s probability is quite high (e.g., 95% to “LLM”) it is confident and the entropy is low. This means that if the model is confident across most token positions and the predicted tokens are the same as in the previous step, then the model will stop generating regardless of how many steps it has left.
+
+### The Entropy Bounded Sampler
+In image diffusion, the sampler controls how to combine the model’s prediction with the current noisy image to produce the next, slightly-less-noisy image. In DiffusionGemma, it controls the amount of noise during the denoising process. Each sampler consists of three components:
+
+- **Canvas initialization** – How the noisy canvas is created.
+- **Token acceptance** – Which of the predicted tokens to keep and reject
+- **Token re-noising** – How rejected tokens are re-noised
+
+As a default, the sampler used is the Entropy Bounded Sampler.
+
+#### Canvas Initialization
+The canvas is created with random tokens (drawn uniformly) which is essentially the same as starting from pure noise in image diffusion.
+
+#### Token Acceptance
+The Entropy Bounded Sampler decides which tokens to keep based on how confident the model is about each one. To measure that confidence, it uses entropy which defines how spread out a probability distribution is. Remember that for each position in the canvas, the model creates a probability distribution. If that distribution leans heavily towards a single token, then it is quite confidence and the entropy will be low. If, however, the distribution is equally distributed (it doesn’t know which token to choose) the entropy will be high.
+
+![](https://substackcdn.com/image/fetch/$s_!1zBI!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2c83e560-455e-451d-8f38-69877265f111_2048x824.png)
+
+The sampler calculates the entropy value for each position in the canvas. Then, it sorts them from lowest entropy (most confident) to highest entropy (least confident). Therefore, this token list starts from the model’s most confident prediction and the sampler will go through one by one to check whether it can be accepted.
+
+![](https://substackcdn.com/image/fetch/$s_!DAXJ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F21eb6dc3-9c68-4bfa-a6e3-1e5f9bc51b37_2048x979.png)
+
+The acceptance criterion is based on the following formula:
+
+![](https://substackcdn.com/image/fetch/$s_!7Vkt!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc0eca5c7-7ab9-4e33-adc0-2bc0dd42bfa5_2048x571.png)
+
+Starting with the canvas position where the model is most confident, the sampler checks whether the sum of entropies seen thus far (minus the maximum entropy) exceeds the limit you set beforehand. If it does, then the model is not confident enough and it will be rejected. If it does not exceed the limit, the model is confident enough and we continue with the next most confident canvas position.
+
+![](https://substackcdn.com/image/fetch/$s_!ha3a!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2ddd4760-06e8-41e1-9e39-8b71ee9c003b_2048x872.png)
+
+As such, the sampler only accepts a group of tokens when they are each individually confident and as a whole do not exceed the limit. This allows the model to focus more on accepting tokens that it is quite sure are correct, regardless of what the other token positions will be.
+
+#### Token Re-noising
+Re-noising the canvas in the Entropy Bounded Sampler is quite straightforward, all rejected tokens are re-noised! That doesn’t mean though that the accepted tokens will always remain in place. In next denoising steps, the model may decide to give it a lower probability after generating updated context.
+
+![](https://substackcdn.com/image/fetch/$s_!B6mT!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F89e216c8-01b0-4505-82dd-283694fc0a4a_2048x1767.png)
diff --git a/docs/tutorials/visual-guides/gemma-4-12b.md b/docs/tutorials/visual-guides/gemma-4-12b.md
index 15473df..d46b218 100644
--- a/docs/tutorials/visual-guides/gemma-4-12b.md
+++ b/docs/tutorials/visual-guides/gemma-4-12b.md
@@ -1,3 +1,148 @@
-# Gemma 4 12B
+---
+title: A Visual Guide to Gemma 4 12B
+description: A Visual Guide to Gemma 4 12B — a visual walkthrough.
+source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4-12b
+---
 
-A visual deep-dive into the Gemma 4 12B unified model.
+# A Visual Guide to Gemma 4 12B
+
+!!! note
+    Originally published on
+    [Exploring Language Models](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4-12b).
+
+[A new Gemma 4 model](https://huggingface.co/google/gemma-4-12B-it) has been released and it’s an interesting one. There was an empty spot between the E4B and 26B A4B that needed to be filled and a 12B model fits nicely there. However, it wouldn’t be Google DeepMind without trying to do something special with that model and sharing it with the public.
+
+So what is different with Gemma 4 12B?
+
+It is encoder-free!
+
+I’m going to be honest with you. The first time I heard “encoder-free” I was confused. Aren’t generative LLMs these days decoder-only anyway? It took me a minute to realize that the encoders for audio and video understanding were actually removed in this model but still had those multimodal capabilities.
+
+![](https://substackcdn.com/image/fetch/$s_!5wj1!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5d303de1-ff8c-427d-8865-86ec3ec04ace_3688x2736.png)
+
+The removal of the encoders, which are typically in charge of making sense of the multimodal inputs, places the burden of making sense of all outputs on the LLM. Although the model is encoder-free, all modalities are now unified within the LLM. Instead of the model having to wait for the encoders to finish processing the audio and image inputs, the LLM can get started earlier processing the input and generating output!
+
+In this guide, I want to showcase what it took to remove the vision and audio encoders and replace them with something much faster. The result, a 12B model that can handle audio and image inputs but without the need for encoders.
+
+## Encoding and Connecting Modalities
+Before we explore Gemma 4 and how it was made encoder-free, let’s take a step back and discover what it generally means to connect modalities other than text to an LLM.
+
+When a piece of text is given to an LLM, the LLM itself (through its token embedding layer) is in charge of splitting it up into tokens and converting them to embeddings. They are then passed to the decoder layers of the LLM which houses the attention layers used to create meaningful and contextual representations of those token embeddings.
+
+To give an LLM the capability to understand modalities other than text, like audio and vision, much of the processing is generally not handled by the LLM. Instead, a separate encoder driven by attention is used to first process the input into embeddings. The encoder tends to be a small Transformer model and uses attention to process the input, much like the LLM would.
+
+![](https://substackcdn.com/image/fetch/$s_!WT2m!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6e8f2eae-611f-40a0-97f4-5f862da7ca36_3810x1344.png)
+
+However, the resulting embeddings cannot be used directly by the LLM. They typically are of different dimensions and might behave differently in n-dimensional space. A common technique is to use something called a “connector”. It converts the output of an encoder to the same dimensions as the token embeddings. After all, a Large **Language** Model is mostly trained on text.
+
+![](https://substackcdn.com/image/fetch/$s_!P0zW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F791201e6-51e5-4358-b9f0-3c0fb4905341_4128x2610.png)
+
+Generally, these two components are used to make LLMs multimodal. It works quite well and is used by many open-access multimodal LLMs, like Gemma 4 and Qwen 3.5.
+
+It is not a free lunch though. The non-text encoders will need to process their inputs first before the LLM can start working. This adds some latency to the inference of these models. Moreover, it also adds quite a few parameters since the encoders are, in absolute terms, quite large.
+
+So… What if we do not need the encoder? What if the connector is all you need?
+
+To explore how DeepMind unified multimodal inputs, let’s first explore how the encoders in Gemma 4 work and what it took to replace them.
+
+## The Modalities of Gemma 4
+Gemma 4 supports three modalities:
+
+- **Text input** – All models
+- **Image input** – All models
+- **Audio input** – The E2B and E4B models only
+
+All of these modalities require a transformer-like architecture with attention to process those inputs. For the image and audio inputs, two separate transformer encoders are used to create visual and audio tokens that the LLM can use.
+
+### The Vision Encoder
+Gemma 4 E2B, E4B, 26B A4B, and 31B all have a vision encoder that is used to process input images. Those vision encoders have some weight to them. For E2B and E4B, they have 150 million parameters. For 26B A4B and 31B, they have 550 million parameters.
+
+![](https://substackcdn.com/image/fetch/$s_!4k8m!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2965d9a1-8eea-41b6-9242-22376ec34080_4408x3928.png)
+
+The input patches have a size of 16 by 16 pixels. After they are processed by the vision encoder, they are pooled together in a grid of 3 by 3 patches each.
+
+![](https://substackcdn.com/image/fetch/$s_!9Jtg!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F645ed06e-253c-4b23-ade0-522500d47823_2904x1672.png)
+
+Those patch embeddings represent 48 by 48 pixels as a result of pooling the original patches together. After this pooling step, a small linear projection layer (the connector) is used to transform the patch embeddings into embeddings that are suitable to be processed by the LLM. As such, the final patch embeddings have the same shape as the token embeddings and are interleaved with the token embeddings.
+
+### The Audio Encoder
+The smaller Gemma 4 models (E2B and E4B) also have an audio encoder that is used to process input audio. Like the vision encoders, these audio encoders have some weight to them. The audio encoder for E2B and E4B are the same and both have 305 million parameters.
+
+![](https://substackcdn.com/image/fetch/$s_!q0xz!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4ff43215-5a63-4c0a-85de-87167c0b9306_5510x4910.png)
+
+Like the vision encoder, after it processes the audio input, it projects the embeddings onto the same dimensional space as the token embeddings. The resulting audio token embeddings are interleaved with the text token embeddings.
+
+These models, albeit relatively small compared to their corresponding Gemma 4 model, still require processing the input which increases latency and adds complexity to the entire pipeline. Moreover, when you fine-tune a Gemma 4 model, you are typically only fine-tuning the LLM itself and not its encoders. This makes it more difficult to grow the encoders with the models as you would have to fine-tune them in tandem, which adds complexity.
+
+So what if we could do without the encoders entirely?
+
+## Making Gemma 4 12B Encoder-free
+To remove the encoders and at the same time provide the community with a new model, Google DeepMind introduces the Gemma 4 12B model. The main architecture of Gemma 4 12B (looking only at the LLM itself) is rather similar to that of the 31B dense model. It uses the same decoder structure interleaving local attention with global attention in such a way that global attention is always last.
+
+![](https://substackcdn.com/image/fetch/$s_!h2lV!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F12384610-b895-46de-b5f3-4765cf1c87f6_3000x4488.png)
+
+The model sits nicely between the existing E4B and 26B A4B models. That makes it suited for setups that have between 12GB and 16GB of VRAM.
+
+Aside from the size, a big part of its offering is the removal of those somewhat chunky encoders.
+
+### Replacing The Vision Encoder
+Gemma 4 12B replaces the entire vision encoder with a lightweight embedding module. Instead of running all those transformer layers (15 or 27) to extract and process visual features, the embedding module only contains a single layer to create the embeddings.
+
+![](https://substackcdn.com/image/fetch/$s_!2N-X!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffed0644d-ed62-4652-957e-9911c5dfa7d6_3424x3840.png)
+
+The model directly uses those 48x48 patches instead of the 16x16 patches. Although simplifying the architecture is a nice side effect, the vision encoder would have created semantically rich embeddings which isn’t the case with the embedder. So pooling the 16x16 patches into 48x48 wouldn’t have that much of an effect.
+
+During processing of the input patches, positional information needs to be added so the LLM has an idea of where those vision tokens are located in the original image. Since the embedder is attention-free, you can’t use the same 2D-positional RoPE as is done in the vision encoder. Likewise, using the LLM’s positional encoding also wouldn’t work since it treats its inputs as a one-dimensional sequence.
+
+Instead, you can inject spatial information in the vision token embeddings before they enter the LLM. The process is actually quite straightforward and involves two matrices:
+
+![](https://substackcdn.com/image/fetch/$s_!nQ5-!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0347cf29-5a49-4984-b3b8-d85412a324d8_1668x1296.png)
+
+These two matrices represent the x and y coordinates of the patches. Each has a dimension of 1120 (representing the patch position) by 3840 (same as the model’s input dimensions). The patch positions represent the maximum number of patches of the input image. As with the vision encoders, the maximum number of patches can represent different ratio aspects.
+
+The Gemma 4 models with vision encoders allow the user to select budget sizes of 70, 140, 280, 560, or 1120 tokens (also referred to as “patches”). These budget sizes represent the maximum number of patches to split an image up. More tokens means a more fine-grained perspective.
+
+For each patch, positional embeddings are added based on their locations. Let’s go through an example. Let’s say that you have a patch at location x=2 and y=1. For both the x and y matrices, the corresponding embedding is selected. Those embeddings are then added together to create the positional embeddings that are added to each vision token embedding generated in the embedder.
+
+![](https://substackcdn.com/image/fetch/$s_!1n69!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa8ef1fbc-57a4-4868-8b7c-845f75b3d063_2400x2896.png)
+
+After adding the positional embeddings there’s a final LayerNorm for stability before the embeddings are projected to the dimensions that Gemma 4 12B expects.
+
+![](https://substackcdn.com/image/fetch/$s_!GDnt!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe1f8eeca-54f0-47a0-b55c-f41279df7d3f_4656x3216.png)
+
+Compared to the larger models with vision encoders of 550 million parameters, the embedder only has about 35 million parameters! This is no free lunch though since the LLM now has to take over much of the processing and understanding that the vision encoder initially did, which it can learn during training.
+
+You might wonder where the 35 million parameters come from when the model is encoder-free! The reason for this has mostly to do with projecting the image patches to the dimensionality of the LLM. Each patch has 48 x 48 x 3 = 6912 pixels that need to be projected onto the dimensions of Gemma 4 12B (3,840). Just this projection (6,912 x 3,840 + 3,840) takes up about 26 million parameters.
+
+So the encoder didn’t shrink to 35 million parameters of attention and/or feed forward neural network weights, it’s just that there are so many pixels to project!
+
+![](https://substackcdn.com/image/fetch/$s_!ABoi!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F47f6586a-6c03-4913-82b1-19df88f6f835_3280x3872.png)
+
+You might also notice that only one patch is used in the image above. Since the embedder has no attention, it processes each patch in isolation. The attention is handled by the LLM instead!
+
+Looking at the picture below, the difference is quite large. Compared to Gemma 4 31B, the embedder is very minimal and is mostly meant to add the positional embeddings. Other than that, the embedder only has about 35 million parameters whilst the vision encoder has around 550 million!
+
+![](https://substackcdn.com/image/fetch/$s_!RRuZ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc1acf330-a0f3-4e60-9ec1-892a918900c5_4656x4864.png)
+
+A major benefit of encoder-free models is that the vision tokens reach the LLM much faster compared to the larger vision encoders. As a result, the LLM can get started earlier processing the input and generating output!
+
+### Replacing The Audio Encoder
+Replacing the audio encoder is much like was done with the vision encoder, but even simpler!
+
+Instead of creating processed features, as is the main purpose of an encoder, the raw audio input is used directly much like the raw pixels for the embedder. With audio, this comes with a major advantage, namely that it does not need additional positional embeddings! The reason for this is that audio is already a 2-dimensional sequence and as such can be processed much like text sequences.
+
+We first split the audio input up into sequences of 40 milliseconds. The audio sequence is recorded at 16,000 snapshots per second (16 kHz), where each sequence contains 640 values. Each value represents how high or low the sound wave is. Together these 640 values are the raw amplitude samples and represent the raw features at that sequence.
+
+![](https://substackcdn.com/image/fetch/$s_!X2Fc!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F53862529-7a89-45f2-b59c-c83b26b21774_4680x2016.png)
+
+These raw features are then given to a linear projection layer so that they have the same dimensionality as Gemma 4 12B expects.
+
+![](https://substackcdn.com/image/fetch/$s_!D6Ij!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa5c58dee-0600-49af-b080-6b08fce52d9b_2340x1376.png)
+
+And that was it! Really, all that was needed is to remove the encoder and directly project the raw features into the same format as the text tokens.
+
+Looking at the picture below, there is a big difference in creating audio tokens between Gemma 4 12B and Gemma 4 E4B. Gemma 4 E4B needs to tokenize the audio input and perform processing through stacked decoders. Gemma 4 12B on the other only splits and projects, that is it.
+
+![](https://substackcdn.com/image/fetch/$s_!5OvL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffba73d61-baca-4a32-9f48-6116b4bdbfa3_4656x4864.png)
+
+Gemma 4 12B is not just a model that fits nicely between E4B and 26B A4B, it does something different and the way it does so is quite interesting!
diff --git a/docs/tutorials/visual-guides/gemma-4.md b/docs/tutorials/visual-guides/gemma-4.md
index 0f1e644..0bd3a26 100644
--- a/docs/tutorials/visual-guides/gemma-4.md
+++ b/docs/tutorials/visual-guides/gemma-4.md
@@ -1,3 +1,328 @@
-# Gemma 4
+---
+title: A Visual Guide to Gemma 4
+description: A Visual Guide to Gemma 4 — a visual walkthrough.
+source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4
+---
 
-A visual walkthrough of the Gemma 4 architecture.
+# A Visual Guide to Gemma 4
+
+!!! note
+    Originally published on
+    [Exploring Language Models](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4).
+
+I’m beyond excited to announce the Gemma 4 family of models! It’s a big part of the reason why I joined Google DeepMind. Developing incredible research and sharing it with the world is something that even to this day, is ingrained in the culture of their research teams.
+
+![](https://substackcdn.com/image/fetch/$s_!Vj_W!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F37fe0e0b-90b8-445b-b2a8-0a402605cc18_6208x4980.png)
+
+There are four models in the Gemma 4 family:
+
+- **[Gemma 4 - E2B](https://huggingface.co/google/gemma-4-e2b-it)** – A dense model with per-layer embeddings, which make them effectively 2 billion parameters
+- **[Gemma 4 - E4B](https://huggingface.co/google/gemma-4-e4b-it)** – A dense model with per-layer embeddings, which make them effectively 4 billion parameters
+- **[Gemma 4 - 31B](https://huggingface.co/google/gemma-4-31b-it)**– A dense model with 31 billion parameters
+- **[Gemma 4 - 26B A4B](https://huggingface.co/google/gemma-4-26B-A4B-it)** – A Mixture of Experts (MoE) model with 26 billion total parameters of which 4 billion are activated during inference.
+
+By having a broad range of model sizes, you can choose whichever model best suits your use case and whether it actually fits on your hardware:
+
+![](https://substackcdn.com/image/fetch/$s_!KYBn!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F107a0d7b-d59b-4815-9f50-65dee5123fc9_1282x304.png)
+
+There are two main architectures that are used across these models, namely dense and Mixture-of-Experts.
+
+![](https://substackcdn.com/image/fetch/$s_!S_JH!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4c6e4126-0b68-4276-bf59-6f3a3341b597_3932x3352.png)
+
+Aside from a wide range of sizes, all models are multimodal and can reason about input images. They were trained to handle many images of varying sizes (more on that later!).
+
+I’m especially interested in trying out the small models a bit more since they not only support input images and text, but also audio.
+
+![](https://substackcdn.com/image/fetch/$s_!vCrp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F46450e20-c6c9-4e8b-b9c4-2aa941210628_4272x2088.png)
+
+There is A LOT to cover, but before I go into the specifics of the different sizes and architectures, let’s first explore what all models have in common!
+
+*👈 click on the stack of lines on the left to see a **Table of Contents** (ToC)*
+
+## The Gemma 4 Architecture
+The Gemma 4 architecture is in many ways like the Gemma 3 series of models, with some changes here and there. There are a number of things that were changed compared to Gemma 3 that relate to all model sizes:
+
+- **Interleaving Layers** – Global attention is always the last layer
+- **K=V** – The Keys are set to be equivalent to the Values only for the global attention
+- **p-RoPE** – Low-frequency-pruned RoPE applied to the embeddings
+
+Some of the models are a bit different than others (e.g., only small models use Per-Layer Embeddings). Before we go into the specifics of each model, let’s explore in more detail what they have in common.
+
+### Interleaving Layers
+Like Gemma 3, Gemma 4 interleaves layers of local attention (also called “sliding window attention”) with global attention (which is regular or “full” attention).
+
+Remember that in global attention, every token attends to all tokens that came before it. Sliding window attention, however, only attends to tokens within a certain limit. This significantly reduces the compute needed to calculate the full attention.
+
+![](https://substackcdn.com/image/fetch/$s_!KjIB!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffea15481-672a-4fe2-b803-5f4d493998d5_4770x2460.png)
+
+In practice, that means that when text is processed using a sliding window, it may only see a part of the entire sequence rather than the entire thing. The “sliding” then refers to the idea of continuously moving the sequence in view as the number of tokens are being generated.
+
+![](https://substackcdn.com/image/fetch/$s_!MKWU!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F39531efc-71c3-44b6-97aa-546b108fb4d6_4770x1242.png)
+
+You typically fix the number of tokens it can see previously. In the case of Gemma 4 models, the smaller models (E2B and E4B) have a sliding window of 512 tokens and the larger models (26B A4B and 31B) have a sliding window of 1024 tokens.
+
+Let’s go through an example with a sliding window of 4 tokens to see what is happening at each token generation:
+
+![](https://substackcdn.com/image/fetch/$s_!YTt2!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3015e48f-a122-4af5-b443-a95681039f9c_4770x2082.png)
+
+In our example, there is only attention given to the last four tokens which at some point in the generation starts to “ignore” the ones that came before that. However, it is actually not forgetting the representations that it calculated in the previous steps. The hidden states allow for the attention to be passed along the attention mechanism from previous layers and steps all the way to the current token.
+
+![](https://substackcdn.com/image/fetch/$s_!AsCY!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff26153b1-ec96-4c04-ad84-717bc134b06b_4020x2046.png)
+
+Although information can be propagated by stacking sliding windows, it is not a perfect recall or attention mechanism. Think of it like a game of telephone, information gets diluted each time it passes through another layer!
+
+Therefore, much like in Gemma 3, local attention and global attention layers are interleaved such that the model does attend to the full sequence at times to better capture the global structure.
+
+In Gemma 3, this interleaving was generally in a 4:1 pattern with 4 layers of local attention followed by a single layer of global attention. However, Gemma 3 - 4B for instance had 34 layers, which means its last layer used local attention rather than global attention. This was changed in the Gemma 4 models to make sure that the last layer is always global attention.
+
+![](https://substackcdn.com/image/fetch/$s_!tQBR!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feb2f917c-31b6-4461-a881-d1ae4a6d0071_4146x2982.png)
+
+The 4:1 pattern, however, is only for the E2B as all other variants have a 5:1 pattern where they start with 5 layers of local attention followed by a single layer of global attention. We can visualize this pattern side-by-side to also demonstrate the depth of these models.
+
+![](https://substackcdn.com/image/fetch/$s_!CRux!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3253caa8-0499-42c7-beaf-ff1e828d890e_3012x3510.png)
+
+Note that the context window of the local attention layers were reduced from 1024 to 512 tokens to allow for further efficiency gains.
+
+### Making Global Attention more Efficient
+Interleaving local attention with global attention is an interesting way of making a Large Language Model more efficient. However, it is not a free lunch. The global attention layers still attend to the entire context, which is a costly and slow process.
+
+In this section, we explore various tricks that Gemma 4 uses to make those global attention layers more efficient!
+
+#### Grouped Query Attention
+Much like Gemma 3, these models use Grouped Query Attention (GQA) which allows the Query heads to share KV-values which reduces the amount of caching that needs to be done. The local attention layers use GQA and have 2 Query heads sharing one KV head.
+
+With Gemma 4, the global attention layers are made more efficient by having 8 Query heads to share one KV head. This drastically reduces the caching needed of the KV values since global attention by itself already has a lot it needs to store (the entire context) compared to the small context of the local attention layers.
+
+![](https://substackcdn.com/image/fetch/$s_!NqV3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbe2618d8-6c06-4e87-bc30-e865ccab1f15_5790x2760.png)
+
+Note that reducing the number of keys and values per head may hurt performance and so to compensate for that, the size of the Keys was doubled!
+
+Doubling the dimensions of the Keys does fill up the KV-cache quite a bit, so let’s explore another interesting trick for reducing the KV-cache.
+
+#### K=V
+Despite the improvements to the grouping in Grouped Query Attention, the global attention layers still take up quite a bit of memory since they attend to the entire sequence. Grouping into 8 queries helps a bit but there is more that can be done for efficiency!
+
+A neat trick, that does not hurt performance that much, is by using the Keys and Values only in the global attention layers. Effectively, this means that all Keys are equivalent to the Values which further reduces the memory requirements for the KV-Cache (or perhaps more accurately now the K-cache for the global attention layer).
+
+![](https://substackcdn.com/image/fetch/$s_!HBUg!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffc081644-bfea-4956-a9cd-235eb5c93b0f_4100x2060.png)
+
+#### p-RoPE
+An important component of any Large Language Model is how it keeps track of the order of words in a sequence. One of the most common techniques is called Rotary Positional Encodings (RoPE). RoPE takes the Query and Key vectors and slices them up into pairs of two values. Each pair can now be seen as a vector in 2-dimensional space pointing towards a direction. RoPE rotates this direction slightly for each pair of values at decreasing speeds. The first pair has a large rotation compared to the last pair. This rotation allows the model to track the relative distances between words.
+
+![](https://substackcdn.com/image/fetch/$s_!m7Qp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd981478a-907b-403e-aaf4-fb93aa37955d_4456x2544.png)
+
+Let’s zoom in a bit on what happens if we were to rotate a query embedding. It first gets cut up into pairs of 2 and each subsequent pair is rotated. The rotation itself becomes smaller with each pair. This is referred to as the frequency, where a high frequency means that the rotation is quite large whereas a small frequency results in a smaller rotation.
+
+![](https://substackcdn.com/image/fetch/$s_!HT5D!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd2ef73d6-fd46-407f-94fb-1f294ca6528a_4080x2040.png)
+
+High frequency pairs are very sensitive to small changes in position due to the amount of information they were given at the first place (large rotation). They are great for tracking a word’s position. The low frequency pairs, however, are given a very slight rotation and barely move at all from word to word. Since these low frequency pairs only contain minimal positional information, and are closest to what the original KQ values were, they are more suitable for tracking semantic content. As such, they stay the most true to what they originally were (without positional information).
+
+![](https://substackcdn.com/image/fetch/$s_!uS48!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fce6aa947-cd32-47cc-8d45-acc07984a706_5130x1665.png)
+
+However, as it turns out, the high frequency pairs already contain sufficient positional information. The low frequency pairs contain just a little bit of positional information which is too little to be meaningful. It can even be harmful as models tend to use the low frequency pairs for semantic information, so the added positional information is just noise. Moreover, with long contexts the small rotations stack up and can eventually cause tokens that are far apart to become misaligned. This hinders the model’s ability to connect words across long sequences.
+
+An elegant solution to this problem is by simply applying RoPE to only some of the dimensions of all vectors, which is called p-RoPE. If p is 0.25, then only the first 25% of pairs get positional information and all other pairs are set to 0. This allows the low frequency pairs to preserve meaning.
+
+![](https://substackcdn.com/image/fetch/$s_!IFpH!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc204db00-74ba-46c6-9747-b1203bf2e666_4080x2400.png)
+
+Gemma 4 uses p-RoPE on global attention layers since the context window is much higher there compared to the local attention layers. Moreover, p-RoPE is especially useful in global attention layers since large context windows may result in distances between tokens that the model hadn’t seen before. The space of all possible RoPE rotations is quite large with global attention versus local attention and by limiting the number of rotations, the model can do a much better job of handling a large context.
+
+When we put everything together, you get the following improvements applied to the **global attention layer**:
+
+- The final layer is always a global attention layer
+- Has groups of 8 Queries per Key
+- The dimensionality of the Keys are doubled
+- Keys = Values
+- p-RoPE where p=0.25
+
+![](https://substackcdn.com/image/fetch/$s_!udLN!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F79da2845-0098-444d-83f5-61a80e464538_4848x2920.png)
+
+### The Vision Encoder
+A big component of the Gemma 4 releases are their multimodal capabilities. All variants support image inputs and allow for handling variable aspect ratios and resolutions!
+
+What makes this possible is a Vision Encoder based on the Vision Transformer (ViT) to process the images. A ViT works similarly to a regular Large Language Model and instead of tokenizing text into sequences of words, it splits up the input image into sequences of patches. Each patch (a subset of the input image) is then passed a Transformer model which spits out an embedding per patch.
+
+![](https://substackcdn.com/image/fetch/$s_!N5tr!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbe6b733c-9d08-4c26-a4ae-a98cb8ac70bc_5352x2760.png)
+
+Each patch is a snippet of the original image and tends to be 16 by 16 pixels. The original paper was aptly named “An image is worth 16x16 words: Transformers for image recognition at scale”.
+
+#### Variable Aspect Ratio
+ViT assumes that the original image is a square that can be divided into patches of 16 by 16 pixels. However, there is a downside to this approach. When you process sequences of patches but the original image can be all different kinds of sizes, the position of a given patch (for instance patch 4) can mean different things depending on the proportion of the original image. For instance, if we assume that the original image will always be a grid of 3 by 3 patches, then patch 4 will always be in the middle. However, if the grid is dynamic and can be 2 by 4 patches for instance, then patch 4 will not be in the middle.
+
+![](https://substackcdn.com/image/fetch/$s_!gXv-!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F987e3dda-3d3d-4f3d-80e6-1b3f2fa8ba20_5560x2820.png)
+
+As such, applying RoPE is not straightforward anymore because the meaning of position (“Patch 4”) is now fully dependent on the aspect ratio of the original image.
+
+An interesting technique applied in Gemma 4 is to replace RoPE by 2D RoPE, which attempts to instill the 2D position of a patch into the positional embeddings rather than seeing them as a 1D sequence of patches. To do so, the embedding of a patch is first split up into two equal-sized parts. RoPE is applied to both parts of the embedding independently, but instead of using m as the position, they now use width (w) and height (h) to add positional information. That way, half of the embedding now contains positional information about its relative width position and the other half contains positional information about its relative height position.
+
+![](https://substackcdn.com/image/fetch/$s_!YBFT!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1991ddef-1083-401f-b419-33caa3aaed60_6440x2320.png)
+
+Using 2D RoPE is not all that is needed to support variable aspect ratios. The original ViT, for instance, resizes the input to make them into squares so that the processed input nicely fits patches of 16 by 16 pixels. Imagine you have a wide image and you want to force it into a square… that would look either quite weird or crop away much of its content!
+
+Gemma 4 introduces support for different aspect ratios by adaptively resizing the input image so that it still supports patches of 16 by 16 pixels. To maintain the original aspect ratio it will pad the image whenever there couldn’t be a perfect fit of a 16 by 16 pixel box.
+
+![](https://substackcdn.com/image/fetch/$s_!1Us2!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F08bd6389-c503-4c44-8e3a-2e349195d989_4352x2784.png)
+
+The original ViT typically assumed a fixed size for the square and therefore would always get back a fixed number of patches (9 in our example). The adaptive resizing method returns a variable number of patches, which can grow quickly in size. Therefore, the tokens generated by the ViT are pooled based on their spatial location. Patch embeddings close to each other get merged until a fixed number of patch embeddings are left.
+
+#### Variable Resolution
+Although variable aspect ratios solve a bunch of problems, how do we decide the number of patches to use? Two images with the same aspect ratio can have different resolutions. Imagine you have two images with both an aspect ratio of 3:2 but one has a lower resolution of 272 by 176 and the other a higher resolution of 544 by 352.
+
+One method would be to always decide on a certain fixed number of patches and resize the image accordingly. However, for certain tasks we might want to support higher resolutions, such as object detection and segmentation whilst for video you might be alright with smaller resolutions to speed up analyzing subsequent frames.
+
+![](https://substackcdn.com/image/fetch/$s_!Ydmv!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F63325403-8aec-4269-86ed-cee314e78d76_5600x2180.png)
+
+Gemma 4 supports different resolutions by introducing a soft token budget. This budget represents the maximum number of patch embeddings (also called soft tokens or visual tokens) are passed to the LLM to process. The user can decide between budget sizes of 70, 140, 280, 560, or 1120 tokens.
+
+Depending on the budget, the input is resized. If you have a higher budget (like 1120 tokens), then your image can maintain a higher resolution and as a result will have many more patches to process. If you have a lower budget (like 70 tokens), then your image needs to be downscaled and you will have fewer patches that need to be processed. With a higher budget (and therefore more tokens), you can capture much more information than with a lower budget.
+
+This budget determines how much the image is resized. Imagine you have a budget of 280 tokens, then the maximum number of patches will be 9 x 280 = 2,520. Why times 9? That’s because in the next step, every 3x3 block of neighboring patches are merged into a single embedding by averaging them.
+
+The budget sizes roughly represent the following resolutions:
+
+![](https://substackcdn.com/image/fetch/$s_!KwmX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F63b9b1b6-b137-4d52-9eb3-681cea2eba69_1323x496.png)
+
+For simplicity, this assumes a square but with variable aspect ratio it can take different ratios and as a result, the patches will also have different dimensions. Also note how the soft tokens are smaller than the actual number of pooled embeddings, that is because the resolution should be multiples of 48 considering 3 patches of 16 pixels are pooled to a single embedding.
+
+Below is an example of how an initially large number of patches gets averaged (also called pooling) into a lower number of patch embeddings (also called soft tokens).
+
+![](https://substackcdn.com/image/fetch/$s_!Zl1j!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F46207f33-328a-40aa-ac72-61faab484749_3216x3008.png)
+
+The pooled patches are typically smaller than the token budget because not every image will perfectly capture the maximum number of patches.
+
+#### Linear Projection
+The patch embeddings generated as a result of the image encoder cannot be given directly to Gemma 4. Like all language models, it expects the input embeddings to have a certain dimensionality to be able to process it. Ideally, you would also want its embeddings to occupy dimensional space in a similar way as the token embeddings such that they can be compared easily.
+
+There is a nice example in the image below where you can see how a given image encoder creates an embedding that is much different from the token embedding created by the text encoder.
+
+![](https://substackcdn.com/image/fetch/$s_!z6fD!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F61ce0dd5-88fb-421b-9718-9cd5f1de1dc5_5640x4000.png)
+
+To make sure that the patch embeddings are aligned correctly with the token embedding the patch embeddings are typically projected using a small neural network so that they have the same dimensions and value distributions as what the language model expects. In Gemma 4, this projection is then followed by RMSNorm to match scale expectations of subsequent Transformer blocks.
+
+![](https://substackcdn.com/image/fetch/$s_!RKWU!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F20e856f9-a1a4-4c79-8f6c-9ee0ece5a361_4656x3928.png)
+
+And that’s how images are processed by Gemma 4! Note that to perform this linear projection, it is trained alongside Gemma 4 to make sure that the projected patch embeddings closely match what Gemma 4 expects.
+
+Moreover, the smaller models (E2B and E4B) all use a vision encoder with 150 million parameters and all other models use a vision encoder with 550 million parameters.
+
+### Putting Everything Together
+When we put everything together, the Gemma 4 architecture that all variants share, can be summarized into three major aspects:
+
+- Interleaving Layers of Local and Global Attention
+- Either Dense or Mixture of Experts
+- Vision encoder
+
+![](https://substackcdn.com/image/fetch/$s_!0-CF!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fecba0193-890d-4f66-ae5a-e6bfdb683841_2424x2672.png)
+
+Note that the feedforward neural networks (FFNNs) are not interleaved with Mixture of Experts (MoE). Any Gemma 4 variants will have either one in all layers or the other.
+
+Now that we have covered the main principles of each variant, let’s go into each set of models to explore them a bit more in-depth:
+
+- Gemma 4 - **[31B](https://huggingface.co/google/gemma-4-31B-it)**
+- Gemma 4 - **[26B A4B](https://huggingface.co/google/gemma-4-25B-A4B-it)**
+- Gemma 4 - **[E2B](https://huggingface.co/google/gemma-4-E2B-it)** and **[E4B](https://huggingface.co/google/gemma-4-E4B-it)**
+
+## Gemma 4 - 31B - Dense
+Despite the popularity of Mixture of Experts (which we cover later), the Gemma 4 family has a dense variant that is quite large with 31B parameters. This model is a nice representation of a more “vanilla” architecture amongst the Gemma 4 variants.
+
+This model is architecturally quite similar to its Gemma 3 counterpart, namely the 27B variant. They both interleave local and global attention and have the same pre- and post- RMSNorm. What it does differently are the aspects we discussed before, like K=V and using P-RoPE. In particular, Gemma 4 - 31B has fewer layers (60 vs 62) but is a wider model.
+
+![](https://substackcdn.com/image/fetch/$s_!cc62!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fbe1a0781-472e-45d8-96f4-8f6cc9911c19_3000x4488.png)
+
+Unlike the Mixture of Experts model and the per-layer embeddings of the tiny models, this variant does not have a “magic” ingredient for us to explore. I think it is a nice representation of the “vanilla” Gemma 4 architecture and allows us to build on this to explore the other models.
+
+## Gemma 4 - 26B A4B - Mixture of Experts
+Gemma 4 has a variant that contains “A” in its name, namely the 26 A4B model. The “A” stands for “active parameters” in contrast to the total number of parameters they contain. Specifically, the 26B A4B model contains in total 26 billion parameters (which are all loaded in memory) but only 4 billion parameters are used during inference. These are referred to as the “active parameters”. By only activating a subset of parameters, these models run much faster than their total number of parameters might suggest.
+
+![](https://substackcdn.com/image/fetch/$s_!6B9e!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8869694f-06de-4318-bd3a-c9359a3be1ae_4000x6128.png)
+
+The architecture that makes this possible is called Mixture of Experts or MoE. Instead of using one big Feedforward Neural Network (FFNN), it uses several smaller ones and dynamically chooses which ones to process a given input.
+
+As such, there are two main components to a MoE:
+
+- **Experts** — A set of FFNNs that can process tokens but only a subset is activated at a time. They tend to be smaller than the FFNN in a dense (“regular”) model.
+- **Router** — Determines which tokens are processed by which experts.
+
+A given token embedding is processed in the MoE layer by first passing to the router which selects one or more experts to activate. To do so, expert probabilities are generated which allows for routing the token embedding. The selected experts update the token embedding as a Feedforward Neural Network normally would.
+
+The probabilities allow for some experts to have a larger or smaller influence on the processed embeddings compared to other experts. As such, the processed embedding is multiplied by the expert probability.
+
+![](https://substackcdn.com/image/fetch/$s_!CU2S!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F32e57db5-ea63-4a3e-a193-c430708f2da8_4704x4744.png)
+
+Although the visual shows a single expert, most MoE activate at least several and combine the result. In Gemma 4, the MoE variant has 128 experts that the router can choose from and 8 will be activated at a time.
+
+Moreover, there is a **shared expert**. This expert will always be activated and used to process the input embedding. The underlying idea of having a shared expert is that it contains a lot of general knowledge that should always be activated, whereas the selected experts contain more fine-grained knowledge. In Gemma 4, this shared expert is three times the size to make sure it encompasses the necessary general knowledge.
+
+![](https://substackcdn.com/image/fetch/$s_!Rzhe!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb7b3eb2f-f178-43aa-902c-cd66ace3a131_4704x5272.png)
+
+Now that you have a big picture of Mixture of Experts in Gemma 4, let’s explore a bit more in-depth what it means to have an efficient architecture with sparse and active parameters. The **sparse parameters** are every single parameter of the model which needs to be loaded into memory when you want to load the model. Gemma 4’s 26B billion parameter model for instance is quite large and you might think that it will run slow because of its big size. However, not all of its sparse parameters are actually used by your device (e.g., GPU) when you are running the model. With Mixture of Experts in Gemma 4, only 8 experts and 1 shared expert is actually used for intermediate calculations. All other 119 experts can take a backseat. These are the active parameters and represent the “**A**” in “26B **A**4B”. This means that although this model is large, it runs almost as fast as a 4B parameter model!
+
+![](https://substackcdn.com/image/fetch/$s_!txYk!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Faf9da40e-432c-481c-8d1d-3699896517f1_3660x3870.png)
+
+I’ve had a blast trying this model and I can tell you that it is a quite capable to run. However, you might want to use a smaller model if you want to run it on device like on your phone.
+
+## Gemma 4 - E2B & E4B - Dense
+Gemma 4 has a variant that contains “E” in its name, namely the E2B and E4B model. The “E” stands for “effective parameters” in contrast to the total number of parameters they contain.
+
+![](https://substackcdn.com/image/fetch/$s_!X3cx!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffaff88e2-4cd6-4669-8211-7d5b6039559c_5082x5160.png)
+
+These models are very efficient and great for on-device use cases. What makes these models especially exciting is that they contain an additional audio encoder for processing audio alongside text and images.
+
+Before we go into the specifics of the audio encoder, let’s first explore this “E”!
+
+### Per-Layer Embeddings
+An interesting component of the smaller Gemma 4 models is the “E” in “E2B” and “E4B”. It stands for “effective” and is in a way similar to sparse versus active parameters that we explored in Mixture of Experts. “Effective” essentially means that these parameters are used for computation and loaded on to your (V)RAM whereas the other parameters (a lookup table with embeddings) is used only once to quickly find related embeddings.
+
+To explore what this means in practice, let’s first cover what happens at the token embedding layer. Whenever it receives a given token, it looks up its embedding using a lookup table. This table can be quite large since it has to store an embedding for each word in its entire vocabulary. Gemma 4 E2B, for instance, has 262,144 tokens each with an embedding size of 1536 dimensions.
+
+![](https://substackcdn.com/image/fetch/$s_!i3rO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea5dd72c-ec58-4da9-94b6-fcc50f46210d_7790x2870.png)
+
+Then, the embeddings for each token gets put through stacks of decoder blocks and iteratively processed. To improve the capabilities of a model, you would normally just add a couple of layers or more parameters. Bigger models tend to outperform smaller ones.
+
+Instead of adding parameters to the model itself, a clever trick is to create an additional set of embeddings at every single layer. It is a bit simplified but imagine that embedding for the token “cat” might emphasize at:
+
+- Layer 2 - “I am a noun, usually followed by a verb.”
+- Layer 18 - “I am a small animal and related to terms like ‘pet’ or ‘feline’.”
+
+These embeddings are quite a bit smaller (256 versus 1536 dimensions in E2B and 2056 in E4B) than the original lookup table to save storage. As a result, we now have a lookup table of 262,144 tokens x 256 dimensions x N layers. The benefit of having a larger lookup table is that we can store important information on flash storage rather than needing valuable (V)RAM which is meant for computations in the model.
+
+These are called Per-Layer Embeddings (PLE) and as the name suggests, are embeddings that used for specific layers.
+
+![](https://substackcdn.com/image/fetch/$s_!0AhS!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe6c18411-5f64-4843-8809-9aa581b4ee7f_4130x3810.png)
+
+At the start of inference, the model grabs the set of embeddings for each input token. Each input token will have an embedding per layer to be used at that specific layer. Note that this lookup is done only once during inference, making this action quite compute efficient since there is no need to lookup the embeddings every time a layer is activated.
+
+![](https://substackcdn.com/image/fetch/$s_!nf1_!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa820d8bb-a7c8-4de1-b350-f74160c5634b_4130x2590.png)
+
+These embeddings are processed between each set of decoder blocks. Here, are gating function is used to decide how to weigh each value in a chosen embedding. This allows the model to additionally focus on certain parts of the embedding retrieved from the lookup table. The resulting embedding, still of dimensionality 256, is then projected up to match the original embedding size (1,536 for E2B and 2,560 for E4B).
+
+After a normalization layer, this weighted representation is then combined with the original output of the previous decoder block. This allows for the processed signal to be “reminded” of what the token embedding represents, rather than it being mixed through a bunch of layers and getting a lot of context added to it.
+
+![](https://substackcdn.com/image/fetch/$s_!otr7!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2e78b43b-e615-4ba0-a3de-7361138b28e2_5560x8280.png)
+
+The model can now focus its internal dimensions on making sense of the tokens rather than needing to carry around information from the bottom layer. This makes the existing layers much more efficient and expressive than they would be on their own.
+
+But more importantly, these per-layer embeddings, albeit being quite large (262,144 x 35 x 256) are stored in flash memory. Storing it in (V)RAM would be a waste considering we only need a couple of embeddings during inference. Therefore, the “**E**” in “E2B” and “E4B” relate to all parameters except for the per-layer embeddings.
+
+This is an elegant technique that allows these models to run on smaller devices, such as cellphones. There, the RAM is extremely valuable and limited, so anything you can do to minimize its usage is a welcome addition.
+
+### Audio Encoder
+The smaller models (E2B and E4B) can also handle audio inputs and are nicely suited for tasks like automatic speech recognition and translation. Much like the vision encoder, the audio encoder transforms the raw audio input into embeddings that can be processed by Gemma 4.
+
+There are several steps to this process but let’s start at the beginning. A common process in converting raw audio into tokens and their embeddings is through a three step process:
+
+1. **Extract features** – Features are extracted from the raw audio through a mel-spectogram which is a 2D visual representation of the raw audio. It represents time (horizontal axis) and frequency bands (vertical axis).
+1. **Group into chunks** – The mel features are grouped into chunks as a starting point for the sequence of tokens.
+1. **Downsample chunks** - The chunks are overlapped and processed by two 2-dimensional convolutional layers to shorten the sequence length. As such, it converts the 2D chunks into a sequence of embeddings (also called “soft” tokens to represent the continuous nature of the token).
+
+![](https://substackcdn.com/image/fetch/$s_!fjgL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F93de9d19-38b6-4487-964c-1faa79971366_1856x1660.png)Fun fact: I recorded audio myself and created the mel spectogram for this visual!
+
+This process is much like the linear projection of the image patches to create embeddings. They still need to be processed subsequently by an encoder so that the embeddings are filled with contextual information. The audio encoder used in Gemma 4 is called a conformer and is much like a regular Transformer Encoder but also uses a convolutional module to process the soft tokens. A nice comparison is between the dense Gemma 4 31B and how the Conformer is implemented. Note how it produces embeddings rather than tokens.
+
+![](https://substackcdn.com/image/fetch/$s_!ZU5p!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7f598397-69e1-4185-bba6-ebe9881c9e7d_2000x2992.png)
+
+Much like the vision encoder, the embeddings produced by the Conformer are projected onto the dimensional space of the embeddings that Gemma 4 would expect. Otherwise, you get a mismatch in embedding size which cannot be processed by Gemma 4.
+
+![](https://substackcdn.com/image/fetch/$s_!KsrJ!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0f6fd442-0913-4538-b49a-262025b7aa4e_5820x3990.png)
+
+This gives us a pipeline that feels quite similar to what we explored with the vision encoder!
diff --git a/docs/tutorials/visual-guides/mtp.md b/docs/tutorials/visual-guides/mtp.md
index 46cee80..5c7e0fc 100644
--- a/docs/tutorials/visual-guides/mtp.md
+++ b/docs/tutorials/visual-guides/mtp.md
@@ -1,3 +1,106 @@
-# MTP for Gemma 4
+---
+title: A Visual Guide to MTP
+description: How multi-token prediction and speculative decoding make Gemma 4 faster — drafter models, shared KV cache, and the clustered LM head.
+source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4
+---
 
-A visual guide to Multi-Token Prediction and speculative decoding.
+# A Visual Guide to Multi-Token Prediction
+
+!!! note
+    Originally published as part of
+    [A Visual Guide to Gemma 4](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4) on Exploring Language Models.
+    For the rest of the architecture, see [A Visual Guide to Gemma 4](gemma-4.md).
+
+To improve the inference speed of the Gemma 4 models, a new series of autoregressive “**drafter**” models has been released alongside the main lineup ([E2B](https://huggingface.co/google/gemma-4-E2B-it-assistant), [E4B](https://huggingface.co/google/gemma-4-E4B-it-assistant), [31B](https://huggingface.co/google/gemma-4-31B-it-assistant), and [26B A4B](https://huggingface.co/google/gemma-4-26B-A4B-it-assistant)). Instead of solely relying on the primary Gemma 4 models (referred to as the “**target**” models), the draft model predicts several tokens in the time it takes the target model to process just one. This technique is also known as **speculative decoding**.
+
+After the drafter has predicted multiple draft tokens, the target model now only has to verify those suggested draft tokens. The verification is done in parallel thereby drastically speeding up inference. It reduces the number of forward passes the target model has to do for each token. Because our drafter generates a sequence of tokens for verification, we refer to it as the **Multi-Token Prediction** (MTP) head.
+
+The draft models released for the Gemma 4 family are small and introduce several enhancements to improve the quality of drafted tokens and to further speed up inference, like using the target model activations and KV-cache to get better predictions.
+
+These enhancements result in significant decoding speedups while guaranteeing similar quality, making these checkpoints perfect for low-latency and on-device applications.
+
+![](https://substackcdn.com/image/fetch/$s_!4JnF!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8c3bcafd-1b5e-4367-bbf3-6c221054d06d_1223x2048.png)
+
+There is a lot to unpack here, so let’s go through speculative decoding, MTP, and the drafters!
+
+## What is Speculative Decoding?
+Gemma 4 models generate text autoregressively and produce one token at a time. Roughly the same amount of compute is needed for each token, regardless of how difficult it is to predict a given token. As such, this can be an unnecessarily slow process when the tokens are quite easy to predict.
+
+Imagine that the larger model is generating text and has already created “*Actions speak*”. For those that recognize the start of this sentence, it is a common proverb in the English language and extends to “*Actions speak *louder *than words.*”. Since it is quite common, smaller models are likely to generate the exact same completion (“*louder than words*”) as a larger model would. As such, it would be a waste of time and compute for the larger model to predict “*louder than words*” one token at a time.
+
+With speculative decoding, we can use a smaller draft model to predict several tokens ahead of time. The draft model will receive the same input “*Actions speak*” and still autoregressively predict a number of tokens, let’s say four tokens. The drafted tokens are generated much faster than the larger model would as the draft model is only a fraction of its size.
+
+![](https://substackcdn.com/image/fetch/$s_!aYx_!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F370343ee-ecbd-425f-9e7e-4323da9113fc_1208x990.png)
+
+## What is Multi-Token Prediction?
+However, the drafted tokens are not necessarily correct, otherwise we could just have used the smaller model. Instead, these tokens are given to the target model to verify in parallel. Since the target model can do this in one forward pass, it doesn’t have to do a forward pass for each token. The drafter is what we call the Multi-Token Prediction (MTP) head. Each forward pass of the target model performs regular next-token prediction (NTP) and produces intermediate hidden states. The drafter (MTP Head) uses those hidden states and runs several autoregressive forward passes to general multiple tokens. As such, a single forward pass of the target model results in multiple tokens instead of one. There is one from next-token prediction of the target model and multiple ones from the drafter (MTP head).
+
+If the target model agrees with the suggestions of the draft model, then all tokens are accepted. Instead of having to generate four tokens, the smaller model did that in a fraction of the time. The target model only had to verify them in the time it would have taken the target model to generate one token. Moreover, if all draft tokens were accepted, the target model would still generate one additional token by itself.
+
+If the target model disagrees with only part of the draft tokens, it will accept them until it disagrees. The target model will then replace the rejected token with a token of its own.
+
+![](https://substackcdn.com/image/fetch/$s_!Wpc-!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb9b7b2a2-d758-48ea-a8c8-eb74cee9fadc_1226x1530.png)
+
+This process is actually quite fast considering the model can verify the quality of the drafted tokens all at once instead of having to verify each of the drafted tokens one at a time. Since the draft model is so small, it takes much less time to predict a single token compared to the target model. This means that the target model can verify multiple tokens at almost the same time it takes to generate a single token! Note that the draft model, like most language models, generates those tokens sequentially but can do this much faster due to its small size.
+
+All tokens that the target model deem to be good enough are selected. The first token it rejects and all others that follow are not included are discarded. However, since the target model has done a forward pass it can still perform a next token prediction. So although a token, like “pens”, is rejected the target model would still come with an alternative of the rejected token.
+
+![](https://substackcdn.com/image/fetch/$s_!WPTL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6e33b067-7d4c-47b6-8868-b88d400f5735_1196x1222.png)
+
+As a result, any number of drafted tokens might be selected by the target model. The full process is quite interesting to visualize considering the draft model performs its processing autoregressively and generates tokens sequence by sequence, the target model can verify all drafted tokens in parallel. The target model is still autoregressive but instead of having to generate those draft tokens one by one, it can now verify them all at once.
+
+![](https://substackcdn.com/image/fetch/$s_!ukO7!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F882747df-a368-4a95-b6f8-e950db65e6c6_1196x1214.png)
+
+## MTP with Gemma 4
+The draft models released for the Gemma 4 family are most similar to the dense Gemma 4 models, but much much smaller. In fact, the draft model for the Gemma 4 E2B has only about 76M parameters, four layers, and a smaller input embedding size (256 compared to 1536).
+
+![](https://substackcdn.com/image/fetch/$s_!HJl6!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6cacdb5a-a144-4cc6-852d-3d401831ab92_1974x2048.png)
+
+Note how the decoder itself is similar to that of a dense Gemma 4 model. However, there is a lot happening before and after the decoder!
+
+The draft models have various enhancements specifically to make it more efficient and to further speed up inference. Likewise, there are also some interesting techniques used to improve the quality of the drafted tokens and decreases drafter latency. After all, we want the drafted tokens to be as accurate as possible, and to be generated as fast as possible.
+
+These changes can be summarized as follows:
+
+- **Target Activations**: The draft model uses the activations from the last layer of the target model, concatenates them with the token embeddings, and down-projects them to the drafter model’s dimension.
+- **KV Cache Sharing**: The draft model cross-attends to the target model’s KV cache rather than building its own.
+- **Efficient Embedder**: The LM Head performs a sparse decoding technique that identifies the most likely clusters of tokens to predict (E2B and E4B only).
+
+Let’s explore each of these in more detail!
+
+### Target Activations
+To improve the quality of the tokens generated by the draft model, the final activations of the target model (e.g., E2B) are fed to the draft model. These are concatenated with the token embedding of the draft model both having 1,536 values assuming the E2B model. The concatenated embeddings are quite large and for efficiency reasons they are projected down to only 256 values. This is essentially a compression of the processed state of the large draft model and the newly initialized embeddings of the draft model. Why let previous activations go to waste?
+
+![](https://substackcdn.com/image/fetch/$s_!TUb3!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5c74db32-f2fe-4dfb-8d50-22ba5793bdb5_2048x923.png)
+
+These activations are only given to the draft model in the first round of processing. Remember that after the draft starts with an initial round, it may produce several tokens by passing them back to itself autoregressively. As such, in round 2, the activations that the draft model generated in round 1 are used since a new token was generated. Since the intermediate activations of the small draft model are only 256 values, they are projected up to match the dimensions of its input embedding table (namely 1,536 values). Note that to further improve efficiency, the input embedding table is shared between the target model and the draft model.
+
+![](https://substackcdn.com/image/fetch/$s_!HMZT!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa10e62de-252f-4460-9c91-80ee0306dc1e_2048x910.png)
+
+Then, in round 3, the draft model uses the activations that were generated in round 2, and so on!
+
+### KV Cache Sharing
+The KV cache can take up quite a bit of space since it contains the key and value representations for every token in the sequence, across every layer. Although Gemma 4 has already taken many steps to reduce this (like setting K=V in the global attention layer), the draft model takes it a step further.
+
+Instead of the draft model needing to process the full prompt and build its own KV cache, it cross-attends to the already-computed KV cache of the target model. For its local attention layers, the draft model simply reuses the last computed local KV cache of the target model. Since the last layer of any Gemma 4 model is always global, that global KV cache is reused for the draft model’s global attention layer.
+
+![](https://substackcdn.com/image/fetch/$s_!iFHU!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F85f7bad4-4fad-4958-8023-127c833b79be_2048x2037.png)
+
+As before, since the target model already did much of the heavy lifting, it would be a waste to throw that away!
+
+### The Efficient Embedder
+Finally, there is an efficient embedder that reduces the compute needed to generate the drafted tokens from the Language Model Head (LM Head). In traditional models, the LM Head would convert the hidden state generated by the decoders to logits (token probabilities) by multiplying it with a large weights matrix (the same used in the embedding layer, namely the lookup table). For such a small model, this process can be quite expensive!
+
+![](https://substackcdn.com/image/fetch/$s_!usUF!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9da3f327-0015-43a8-b655-d8c634fe07f9_1052x1070.png)
+
+The question you might ask yourself is whether we really need to calculate the logits for all 262,144 tokens when most of them are likely to be irrelevant?
+
+In the drafter for Gemma 4 E2B and E4B models, this process is made more efficient through a classic technique, namely clustering. All token embeddings have been clustered separately into large groups of tokens that have similar meaning. We then find an embedding representation for each cluster.
+
+![](https://substackcdn.com/image/fetch/$s_!Dc_M!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff6716388-6616-4678-ace0-c96333618657_1536x1276.png)
+
+This new lookup table is then used in the LM Head where it is multiplied by the hidden state. This results in cluster logits instead of token logits. This means that the cluster logits tell us the clusters that most likely contain the next token. The clusters that are most likely to contain the correct token are selected. Then, all tokens within those clusters are used to calculate the final token logits.
+
+![](https://substackcdn.com/image/fetch/$s_!v7BV!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F755089fb-51ad-4a4e-bfc1-16b1986f2c9d_812x1228.png)
+
+This computation is much easier and instead tells us that the next token is almost certainly in the chosen clusters. This efficient embedder allows for speeding up the draft model even more!
diff --git a/scripts/import_newsletter.py b/scripts/import_newsletter.py
new file mode 100644
index 0000000..da66d34
--- /dev/null
+++ b/scripts/import_newsletter.py
@@ -0,0 +1,259 @@
+#!/usr/bin/env python3
+"""
+Import a visual guide from newsletter.maartengrootendorst.com as markdown.
+
+    python scripts/import_newsletter.py <post-url> <output.md> --title "A Visual Guide to X"
+
+Extracts the article body verbatim -- headings, prose, lists, quotes and images -- rather than
+summarising it. The guides are Maarten's own writing, so the words have to survive the import
+exactly; a paraphrase would misrepresent them.
+
+Images keep their Substack CDN URLs for now, so the pages can be reviewed before anyone spends
+time vendoring artwork. `scripts/lint_docs.py --check images` allows that host explicitly and
+flags it as pending -- see PLAN.md Step 6.1.
+"""
+
+from __future__ import annotations
+
+import argparse
+import html
+import re
+import sys
+import urllib.request
+from html.parser import HTMLParser
+from pathlib import Path
+
+# `<div dir="auto" class="body markup">` -- class is not necessarily the first attribute.
+BODY_START = re.compile(r'<div[^>]*\bclass="[^"]*\bbody\b[^"]*\bmarkup\b[^"]*"[^>]*>')
+
+# Substack chrome that sits inside the article flow but is not part of the writing.
+SKIP_CLASSES = (
+    "subscription-widget",
+    "subscribe-widget",
+    "button-wrapper",
+    "captioned-image-container-caption",
+    "poll",
+    "footnote-anchor",
+    "pencraft",
+)
+
+
+class ArticleToMarkdown(HTMLParser):
+    """Convert Substack's article HTML to markdown, keeping structure and image URLs."""
+
+    def __init__(self) -> None:
+        super().__init__(convert_charrefs=True)
+        self.out: list[str] = []
+        self.buffer: list[str] = []
+        self.depth = 0                 # nesting depth of the article div
+        self.skip_depth = 0            # >0 while inside skipped chrome
+        self.heading: str | None = None
+        self.list_stack: list[str] = []
+        self.in_quote = False
+        self.in_code = False
+        self.seen_images: set[str] = set()
+
+    # --- helpers ---------------------------------------------------------------------------
+    def emit(self, text: str) -> None:
+        self.out.append(text)
+
+    def flush(self) -> None:
+        text = "".join(self.buffer).strip()
+        self.buffer.clear()
+        if not text:
+            return
+        text = re.sub(r"[ \t]+", " ", text)
+        if self.heading:
+            self.emit(f"{'#' * int(self.heading[1])} {text}\n")
+        elif self.list_stack:
+            marker = "-" if self.list_stack[-1] == "ul" else "1."
+            indent = "    " * (len(self.list_stack) - 1)
+            self.emit(f"{indent}{marker} {text}")
+        elif self.in_quote:
+            self.emit("\n".join(f"> {line}" for line in text.splitlines()) + "\n")
+        else:
+            self.emit(text + "\n")
+
+    # --- parser callbacks ------------------------------------------------------------------
+    def handle_starttag(self, tag: str, attrs: list) -> None:
+        attributes = dict(attrs)
+        classes = attributes.get("class") or ""
+
+        if self.skip_depth:
+            self.skip_depth += 1
+            return
+        if any(name in classes for name in SKIP_CLASSES):
+            self.flush()
+            self.skip_depth = 1
+            return
+
+        if tag == "img":
+            src = attributes.get("src") or ""
+            # Substack serves a low-res placeholder plus the real asset in srcset.
+            if src and "substackcdn.com" in src and src not in self.seen_images:
+                self.seen_images.add(src)
+                self.flush()
+                alt = (attributes.get("alt") or "").strip()
+                self.emit(f"![{alt}]({src})\n")
+        elif tag in ("h1", "h2", "h3", "h4", "h5", "h6"):
+            self.flush()
+            self.heading = tag
+        elif tag in ("ul", "ol"):
+            self.flush()
+            self.list_stack.append(tag)
+        elif tag == "li":
+            self.flush()
+        elif tag == "blockquote":
+            self.flush()
+            self.in_quote = True
+        elif tag in ("strong", "b"):
+            self.buffer.append("**")
+        elif tag in ("em", "i"):
+            self.buffer.append("*")
+        elif tag == "code":
+            self.buffer.append("`")
+            self.in_code = True
+        elif tag == "a":
+            self.buffer.append("[")
+            self._href = attributes.get("href") or ""
+        elif tag == "br":
+            self.buffer.append(" ")
+        elif tag == "hr":
+            self.flush()
+            self.emit("---\n")
+
+    def handle_endtag(self, tag: str) -> None:
+        if self.skip_depth:
+            self.skip_depth -= 1
+            return
+
+        if tag in ("h1", "h2", "h3", "h4", "h5", "h6"):
+            self.flush()
+            self.heading = None
+        elif tag in ("ul", "ol"):
+            self.flush()
+            if self.list_stack:
+                self.list_stack.pop()
+            self.emit("")
+        elif tag in ("li", "p", "div"):
+            self.flush()
+        elif tag == "blockquote":
+            self.flush()
+            self.in_quote = False
+        elif tag in ("strong", "b"):
+            self.buffer.append("**")
+        elif tag in ("em", "i"):
+            self.buffer.append("*")
+        elif tag == "code":
+            self.buffer.append("`")
+            self.in_code = False
+        elif tag == "a":
+            href = getattr(self, "_href", "")
+            self.buffer.append(f"]({href})" if href else "]")
+
+    def handle_data(self, data: str) -> None:
+        if self.skip_depth:
+            return
+        self.buffer.append(data)
+
+
+def extract_body(page: str) -> str:
+    """Slice out the article div, balancing tags so trailing page chrome is excluded."""
+    match = BODY_START.search(page)
+    if not match:
+        raise SystemExit("could not find the article body; Substack markup may have changed")
+
+    start = match.end()
+    depth = 1
+    for token in re.finditer(r"<(/?)div\b", page[start:]):
+        depth += -1 if token.group(1) else 1
+        if depth == 0:
+            return page[start : start + token.start()]
+    return page[start:]
+
+
+def normalise_headings(text: str) -> str:
+    """Re-level the article's headings to sit under the page title, and de-style them.
+
+    Substack posts start their sections at `#`, which would give the page a second H1 and break
+    the table of contents. Everything is shifted so the article's own top level becomes `##`,
+    preserving the relative hierarchy. Emphasis inside heading text (`### **Foo**`) is stripped:
+    the heading level already carries the emphasis.
+    """
+    levels = [len(m.group(1)) for m in re.finditer(r"^(#+) \S", text, re.MULTILINE)]
+    if not levels:
+        return text
+    shift = 2 - min(levels)
+
+    def relevel(match: re.Match) -> str:
+        level = min(len(match.group(1)) + shift, 6)
+        title = re.sub(r"[*_]{1,3}", "", match.group(2)).strip()
+        title = re.sub(r"\s+", " ", title)
+        return f"{'#' * level} {title}"
+
+    return re.sub(r"^(#+) +(.+?)\s*$", relevel, text, flags=re.MULTILINE)
+
+
+def tidy(markdown: str) -> str:
+    text = html.unescape(markdown)
+
+    # Substack wraps every image in a link to its full-size version. Keep the image, drop the
+    # wrapper -- a link whose only content is an image is chrome, not a reference.
+    text = re.sub(r"\[\s*(!\[[^\]]*\]\([^)\s]+\))\s*\]\([^)\s]+\)", r"\1", text)
+
+    # Emphasis must hug its text: `**bold **` renders literally. Move any whitespace that sits
+    # just inside the markers to the outside, in one pass so the rules can't fight each other.
+    def hug(match: re.Match) -> str:
+        lead, inner, trail = match.groups()
+        return f"{' ' if lead else ''}**{inner}**{' ' if trail else ''}"
+
+    text = re.sub(r"\*\*(\s*)([^*\n]+?)(\s*)\*\*", hug, text)
+
+    text = re.sub(r"\*\*\s*\*\*", "", text)      # emptied emphasis
+    text = re.sub(r"\[\]\(\)", "", text)
+    text = re.sub(r"\n{3,}", "\n\n", text)
+    text = re.sub(r"[ \t]+\n", "\n", text)
+    text = re.sub(r"[ \t]{2,}", " ", text)
+    text = normalise_headings(text)
+    return text.strip() + "\n"
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("url")
+    parser.add_argument("output")
+    parser.add_argument("--title", required=True)
+    parser.add_argument("--description", default="")
+    args = parser.parse_args()
+
+    request = urllib.request.Request(args.url, headers={"User-Agent": "Mozilla/5.0"})
+    with urllib.request.urlopen(request) as response:
+        page = response.read().decode("utf-8", errors="replace")
+
+    converter = ArticleToMarkdown()
+    converter.feed(extract_body(page))
+    converter.flush()
+    body = tidy("\n".join(converter.out))
+
+    description = args.description or f"{args.title} — a visual walkthrough."
+    front = (
+        "---\n"
+        f"title: {args.title}\n"
+        f"description: {description}\n"
+        f"source_url: {args.url}\n"
+        "---\n\n"
+        f"# {args.title}\n\n"
+        f"!!! note\n"
+        f"    Originally published on\n"
+        f"    [Exploring Language Models]({args.url}).\n\n"
+    )
+
+    out = Path(args.output)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(front + body, encoding="utf-8")
+    print(f"  {out}: {len(body):,} chars, {len(converter.seen_images)} images")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index 7367d55..35655e0 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -59,6 +59,11 @@
 # The site being replaced. Any link here is a leak.
 UPSTREAM_DOCS = "ai.google.dev/gemma/docs"
 
+# Image hosts allowed to stay remote FOR NOW. The visual guides are imported with their
+# Substack CDN URLs so the pages can be reviewed before anyone spends time on artwork.
+# Vendoring them is PLAN.md Step 6.1 -- empty this tuple when that lands.
+PENDING_IMAGE_HOSTS = ("substackcdn.com", "substack-post-media")
+
 # Devsite callouts that should have become admonitions during conversion.
 DEVSITE_CALLOUTS = ("Note:", "Key Point:", "Caution:", "Warning:", "Important:", "Tip:")
 
@@ -294,6 +299,8 @@ def check_images(ctx: Context) -> Iterable[Finding]:
                             "inline base64 image -- extract to docs/assets/",
                         )
                     elif src.startswith(("http://", "https://")):
+                        if any(host in src for host in PENDING_IMAGE_HOSTS):
+                            continue  # see PENDING_IMAGE_HOSTS
                         yield Finding(
                             "images", page.repo_rel, lineno,
                             f"hotlinked image: {src}",
diff --git a/zensical.toml b/zensical.toml
index 72a6e05..dbf61c7 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -161,21 +161,23 @@ nav = [
 
   { "Fine-tune" = [
     { "Overview" = "tuning/overview.md" },
+    { "Guides" = [
     { "QLoRA" = "tuning/qlora.md" },
     { "Full fine-tune" = "tuning/full-finetune.md" },
     { "Vision QLoRA" = "tuning/vision-qlora.md" },
     { "LoRA with Keras" = "tuning/keras-lora.md" },
     { "Distributed" = "tuning/distributed.md" },
     { "Spoken language" = "tuning/spoken-language.md" },
-    { "Mobile actions" = "tuning/mobile-actions.md" },
+    { "Mobile actions" = "tuning/mobile-actions.md" },]
+    }
   ]},
 
   { "Learn" = [
     { "Visual guides" = [
-      "tutorials/visual-guides/gemma-4.md",
-      "tutorials/visual-guides/gemma-4-12b.md",
-      "tutorials/visual-guides/mtp.md",
-      "tutorials/visual-guides/diffusiongemma.md",
+      { "Gemma 4" = "tutorials/visual-guides/gemma-4.md" },
+      { "Gemma 4 12B" = "tutorials/visual-guides/gemma-4-12b.md" },
+      { "Multi-Token Prediction" = "tutorials/visual-guides/mtp.md" },
+      { "DiffusionGemma" = "tutorials/visual-guides/diffusiongemma.md" },
     ]},
     { "Tutorials" = [
       "learn/business-email-assistant.md",

From 831f817bcf7a0436970a2ff8eb7d8c4347fdd0cc Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Tue, 4 Aug 2026 11:44:08 +0200
Subject: [PATCH 12/20] Small changes

---
 .lintbaseline.json   |  3 +-
 PLAN.md              | 78 ++++++++++++++++++++++++++++++++------------
 scripts/lint_docs.py |  3 ++
 scripts/sources.toml | 46 ++++++++++++++++++++++++++
 zensical.toml        |  4 +--
 5 files changed, 109 insertions(+), 25 deletions(-)

diff --git a/.lintbaseline.json b/.lintbaseline.json
index 9fddc66..7984c65 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -13,7 +13,7 @@
     "modeldata",
     "css"
   ],
-  "count": 87,
+  "count": 86,
   "known": [
     "frontmatter|docs/community/discord.md|no frontmatter block",
     "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
@@ -99,7 +99,6 @@
     "stubs|docs/community/gemmaverse.md|placeholder link: example.com",
     "stubs|docs/cookbook.md|stub: 395 chars of content (minimum 400)",
     "stubs|docs/demos/concurrent.md|stub: 52 chars of content (minimum 400)",
-    "stubs|docs/index.md|stub: 0 chars of content (minimum 400)",
     "stubs|docs/tools/index.md|stub: 144 chars of content (minimum 400)",
     "stubs|docs/tools/model-selector.md|stub: 58 chars of content (minimum 400)"
   ]
diff --git a/PLAN.md b/PLAN.md
index 4b698df..227dbf6 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -44,33 +44,35 @@ that validation is only transferable if the words survive the port intact.
 Rule of thumb: if a diff against the source `.md.txt` shows a prose change, it needs a reason
 written down.
 
-### Hand-maintained sections — do not re-convert
+### Import once, then hand-maintain — non-negotiable
 
-**As of 2026-08-03, `docs/models/**` and `docs/get-started/**` are hand-maintained by Maarten.**
-The converter is no longer the source of truth for them; the files on disk are. Running the
-converter over these pages would silently discard his edits.
+**Every page is hand-maintained the moment it is imported.** Maarten edits pages continuously
+to get them looking right; re-running an importer over one would silently discard that work.
 
-This is enforced, not just documented — those 41 pages carry `frozen = true` in
-`scripts/sources.toml`:
+All **64** manifest pages carry `frozen = true`. There are no unfrozen pages, and that is the
+steady state — not a backlog to work through.
 
-- `port_docs.py` skips them and reports `frozen (hand-maintained, skipped)`.
-- `check_fidelity.py` skips them: divergence from upstream is now *intended*, so comparing
-  against the source would produce noise, not signal.
-- Their `source` line stays in the manifest for provenance — `source_url` frontmatter in
-  Step 3.1 still needs it, and it records where the page originally came from.
+- `port_docs.py` skips frozen pages: `frozen (hand-maintained, skipped)`.
+- `check_fidelity.py` skips them too — divergence from upstream is now *intended*, so comparing
+  against the source would produce noise rather than signal.
+- The `source` line stays for provenance: `source_url` frontmatter (Step 3.1) needs it, and it
+  records where the page came from.
 
-**The converter is for new imports only.** When importing a new page: add it to the manifest
-*without* `frozen`, run the pipeline, review the result — and set `frozen = true` as soon as
-anyone starts editing it by hand.
+**Importing a new page** is the only time a converter runs:
 
-Currently convertible (6): `deployment/index.md`, `deployment/huggingface.md`,
-`deployment/mobile-actions.md`, `tuning/spoken-language.md`,
-`learn/business-email-assistant.md`, `learn/personal-code-assistant.md`.
+1. Add it to `scripts/sources.toml` *without* `frozen`.
+2. Run the pipeline (`port_docs.py` → `vendor_images.py` → `rewrite_links.py`).
+3. Review the result.
+4. **Set `frozen = true` immediately.** Before any hand-editing, not after.
 
-!!! warning "Converter fixes no longer reach frozen pages"
-    A future converter improvement will not propagate to Models or Get started. Those pages
-    need the equivalent fix applied by hand, or a one-off unfreeze/re-port/re-freeze with the
-    edits re-applied. Weigh that before adding converter transforms aimed at those sections.
+!!! warning "Converter improvements do not reach existing pages"
+    A fix to `convert_notebook.py` only affects future imports. Existing pages need the
+    equivalent change applied by hand. Weigh that before adding converter transforms aimed at
+    pages that are already in.
+
+**Visual guides** use `scripts/import_newsletter.py` instead, marked `importer =
+"import_newsletter"`. `gemma-4.md` and `mtp.md` were additionally split by hand after import —
+re-importing either would merge MTP back into the parent guide and drop the pointer.
 
 Local preview:
 
@@ -882,6 +884,40 @@ model they did not choose.
 **Sequencing:** Step 3.1 (frontmatter schema, `models:` key) → static header with applicability
 → 3.4 tools into the same bar → 4.2 selector into the same bar.
 
+## Phase 2 status — not finished
+
+| Step | State |
+|---|---|
+| 2.1 Restructure nav | ✅ 8 tabs, zero orphans |
+| 2.2 Local engine pages | ✅ Run section has zero stubs |
+| 2.3 Fill remaining stubs | **7 left** — see below |
+| 2.4 Port remaining upstream pages | **6 left** — see below |
+
+**Remaining stubs (7):**
+
+| Page | Blocked on |
+|---|---|
+| `community/gemmaverse.md` | real project data (still ships `example.com` cards) |
+| `community/discord.md` | port `/discord` |
+| `community/gemma-skills.md` | content — no upstream source |
+| `cookbook.md` | content decision |
+| `demos/concurrent.md` | content — the app lives in the cookbook repo |
+| `tools/index.md`, `tools/model-selector.md` | **Phase 4** — these are the interactive tools |
+
+**Upstream pages not ported (6):** `/core/gemma_library`, `/core/pytorch_gemma`,
+`/core/gemma_on_gemini_api`, `/core/deploy_to_cloud_run_from_ai_studio`, `/gemma_chat`,
+`/meme-generator`. Each needs a keep-or-drop decision rather than a straight port —
+`/meme-generator` in particular is a demo app, not reference material.
+
+Deliberately not ported and **not** gaps: `/integrations/{ollama,llamacpp,lmstudio,mlx}`
+(written as quick references instead), `/core/gke` (folded into Google Cloud),
+`/codegemma/*` (dropped by decision).
+
+**Provenance gap.** Pages ported before `sources.toml` existed — `get-started/capabilities/*`,
+`prompt-formatting.md`, `quickstart.md` — have no `source` line. They are protected by absence
+(nothing can re-convert them), but Step 3.1's `source_url` frontmatter will need those URLs
+adding to the manifest with `frozen = true`.
+
 ## Phase 3 — Agent-first infrastructure
 
 *The headline requirement from the original plan, currently ~11% built.*
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index 35655e0..d826e7c 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -335,6 +335,9 @@ def check_internal(ctx: Context) -> Iterable[Finding]:
 def check_stubs(ctx: Context) -> Iterable[Finding]:
     """Every page in the nav must have real content."""
     for page in ctx.pages:
+        # A page rendered by a template holds its content there, not in the body.
+        if page.frontmatter.get("template"):
+            continue
         if "Content coming soon" in page.body:
             yield Finding("stubs", page.repo_rel, 0, "placeholder text: 'Content coming soon'")
             continue
diff --git a/scripts/sources.toml b/scripts/sources.toml
index 0499d0f..d0b5f1a 100644
--- a/scripts/sources.toml
+++ b/scripts/sources.toml
@@ -47,86 +47,104 @@ frozen = true
 [[page]]
 path = "deployment/index.md"
 source = "https://ai.google.dev/gemma/docs/run.md.txt"
+frozen = true
 
 [[page]]
 path = "deployment/huggingface.md"
 source = "https://ai.google.dev/gemma/docs/core/huggingface_inference.md.txt"
 assets = "huggingface"
+frozen = true
 
 [[page]]
 path = "deployment/keras.md"
 source = "https://ai.google.dev/gemma/docs/core/keras_inference.md.txt"
+frozen = true
 
 [[page]]
 path = "deployment/mobile.md"
 source = "https://ai.google.dev/gemma/docs/integrations/mobile.md.txt"
+frozen = true
 
 [[page]]
 path = "deployment/web.md"
 source = "https://ai.google.dev/gemma/docs/integrations/web.md.txt"
+frozen = true
 
 [[page]]
 path = "deployment/google-cloud.md"
 source = "https://ai.google.dev/gemma/docs/integrations/google-cloud.md.txt"
+frozen = true
 
 [[page]]
 path = "deployment/langchain.md"
 source = "https://ai.google.dev/gemma/docs/integrations/langchain.md.txt"
 assets = "deployment"
+frozen = true
 
 [[page]]
 path = "deployment/litert.md"
 source = "https://ai.google.dev/gemma/docs/conversions/hf-to-mediapipe-task.md.txt"
+frozen = true
 
 [[page]]
 path = "tuning/mobile-actions.md"
 source = "https://ai.google.dev/gemma/docs/mobile-actions.md.txt"
+frozen = true
 
 # --- Tuning / Learn ---------------------------------------------------------------------------
 
 [[page]]
 path = "tuning/overview.md"
 source = "https://ai.google.dev/gemma/docs/tune.md.txt"
+frozen = true
 
 [[page]]
 path = "tuning/qlora.md"
 source = "https://ai.google.dev/gemma/docs/core/huggingface_text_finetune_qlora.md.txt"
 assets = "tuning"
+frozen = true
 
 [[page]]
 path = "tuning/full-finetune.md"
 source = "https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetune.md.txt"
 assets = "tuning"
+frozen = true
 
 [[page]]
 path = "tuning/vision-qlora.md"
 source = "https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora.md.txt"
 assets = "tuning"
+frozen = true
 
 [[page]]
 path = "tuning/keras-lora.md"
 source = "https://ai.google.dev/gemma/docs/core/lora_tuning.md.txt"
 assets = "tuning"
+frozen = true
 
 [[page]]
 path = "tuning/distributed.md"
 source = "https://ai.google.dev/gemma/docs/core/distributed_tuning.md.txt"
 assets = "tuning"
+frozen = true
 
 [[page]]
 path = "tuning/spoken-language.md"
 source = "https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning.md.txt"
 assets = "tuning"
+frozen = true
 
 [[page]]
 path = "learn/business-email-assistant.md"
 source = "https://ai.google.dev/gemma/docs/business-email-assistant.md.txt"
 assets = "learn"
+frozen = true
 
 [[page]]
 path = "learn/personal-code-assistant.md"
 source = "https://ai.google.dev/gemma/docs/personal-code-assistant.md.txt"
 assets = "learn"
+frozen = true
 
 # --- Core -------------------------------------------------------------------------------------
 
@@ -338,3 +356,31 @@ frozen = true
 path = "models/gemma-scope/index.md"
 source = "https://ai.google.dev/gemma/docs/gemma_scope.md.txt"
 frozen = true
+
+# --- Visual guides ------------------------------------------------------------------------
+# Imported with scripts/import_newsletter.py, not convert_notebook.py.
+# gemma-4.md and mtp.md were split by hand after import -- re-importing would undo that.
+
+[[page]]
+path = "tutorials/visual-guides/gemma-4.md"
+source = "https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4"
+importer = "import_newsletter"
+frozen = true
+
+[[page]]
+path = "tutorials/visual-guides/gemma-4-12b.md"
+source = "https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4-12b"
+importer = "import_newsletter"
+frozen = true
+
+[[page]]
+path = "tutorials/visual-guides/mtp.md"
+source = "https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4"
+importer = "import_newsletter"
+frozen = true
+
+[[page]]
+path = "tutorials/visual-guides/diffusiongemma.md"
+source = "https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-diffusiongemma"
+importer = "import_newsletter"
+frozen = true
diff --git a/zensical.toml b/zensical.toml
index dbf61c7..4f6c8f9 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -168,8 +168,8 @@ nav = [
     { "LoRA with Keras" = "tuning/keras-lora.md" },
     { "Distributed" = "tuning/distributed.md" },
     { "Spoken language" = "tuning/spoken-language.md" },
-    { "Mobile actions" = "tuning/mobile-actions.md" },]
-    }
+    { "Mobile actions" = "tuning/mobile-actions.md" },
+  ]},
   ]},
 
   { "Learn" = [

From 175eb8d102e6ebe96f9edc94f098f7f8f263eeac Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Tue, 4 Aug 2026 15:58:31 +0200
Subject: [PATCH 13/20] Improved design

---
 .lintbaseline.json                        |   3 +-
 PLAN.md                                   |  66 ++++++
 docs/deployment/huggingface.md            |   2 +-
 docs/deployment/keras.md                  |   2 +-
 docs/deployment/langchain.md              |   2 +-
 docs/get-started/setup.md                 |  58 -----
 docs/learn/business-email-assistant.md    |   2 +-
 docs/learn/personal-code-assistant.md     |   2 +-
 docs/models/recurrentgemma/fine-tuning.md |   2 +-
 docs/models/recurrentgemma/inference.md   |   2 +-
 docs/stylesheets/content.css              | 215 ++++++++++++++++++
 docs/stylesheets/gemma.css                |  69 +++++-
 docs/stylesheets/layout.css               | 254 ++++++++++++++--------
 docs/stylesheets/nav.css                  | 163 ++++++++++++++
 docs/tuning/keras-lora.md                 |   2 +-
 docs/tuning/spoken-language.md            |   2 +-
 scripts/link_map.toml                     |   3 +-
 scripts/sources.toml                      |   5 -
 zensical.toml                             |   3 +-
 19 files changed, 683 insertions(+), 174 deletions(-)
 delete mode 100644 docs/get-started/setup.md
 create mode 100644 docs/stylesheets/content.css
 create mode 100644 docs/stylesheets/nav.css

diff --git a/.lintbaseline.json b/.lintbaseline.json
index 7984c65..7682944 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -13,7 +13,7 @@
     "modeldata",
     "css"
   ],
-  "count": 86,
+  "count": 85,
   "known": [
     "frontmatter|docs/community/discord.md|no frontmatter block",
     "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
@@ -41,7 +41,6 @@
     "frontmatter|docs/get-started/prompt-structure.md|no frontmatter block",
     "frontmatter|docs/get-started/quickstart.md|missing required key: description",
     "frontmatter|docs/get-started/quickstart.md|missing required key: title",
-    "frontmatter|docs/get-started/setup.md|no frontmatter block",
     "frontmatter|docs/learn/business-email-assistant.md|no frontmatter block",
     "frontmatter|docs/learn/personal-code-assistant.md|no frontmatter block",
     "frontmatter|docs/models/datagemma/index.md|no frontmatter block",
diff --git a/PLAN.md b/PLAN.md
index 227dbf6..3d5fde4 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -884,6 +884,72 @@ model they did not choose.
 **Sequencing:** Step 3.1 (frontmatter schema, `models:` key) → static header with applicability
 → 3.4 tools into the same bar → 4.2 selector into the same bar.
 
+### Step 1.4 — Stripe-inspired refinement ✅
+
+Measured against `docs.stripe.com` rather than eyeballed. What actually differed:
+
+| | Stripe | Was | Now |
+|---|---|---|---|
+| Content width | 800px | **1600px** | **880px** |
+| Body size | 14px | 15.5px | **14px** |
+| Line height | 1.43 | 1.7 | 1.65 |
+| Neutral ramp | 13 steps | none | **12 steps**, blue-tinted |
+| Brand use | accent only | **full header** | accent only |
+
+- [x] **Layout reshaped.** Sidebar sits flush left; the *content* is centred in the space that
+      remains, rather than stretching to fill it. That is the single biggest change — at 1600px
+      lines ran to ~180 characters.
+- [x] `.wide` opt-out for benchmark tables and image-dense guides (the 7-column Gemma 4 table
+      fits at 880px: 814px in an 842px container).
+- [x] **Header is a near-white tint** (`--gemma-n50`), not a slab of brand blue. Blue appears
+      once: the active tab, as colour + a 2px rule.
+- [x] New `nav.css` (chrome) and `content.css` (tables, admonitions, collapsibles, inline code,
+      content tabs) — Stripe-inspired, tinted surfaces over heavy borders.
+
+**Four bugs found only because the result was measured in a browser:**
+
+1. **Body text was 18px, not 14px.** Material ships `html { font-size: 125% }`, so every `rem`
+   in the file was 25% larger than it read. Anchored to 100%.
+2. **The active-tab rule never matched.** Zensical marks the active tab on the `<li>`
+   (`md-tabs__item--active`), not the `<a>`.
+3. **Material's palette outranks a plain scheme selector.** It scopes at
+   `[data-md-color-scheme=X][data-md-color-primary=Y]` (0,2,0); ours was (0,1,0) and lost
+   silently regardless of load order. Dark-mode links and inline code stayed light.
+4. **Admonition tints outranked the base rule** at `.md-typeset .admonition.note` (0,3,1).
+
+**Layout shape** (verified at 1366px and 2000px):
+
+```
+sidebar 17rem │  ←auto→  │ content 56rem │ toc 13rem │  ←auto→
+   flush left            └── centred as a pair, TOC follows the text ──┘
+```
+
+The sidebar stays flush left at every width. Content and the table of contents are centred
+*as a pair* via auto margins on the pair's outer edges — capping and centring all three
+columns instead pushes the sidebar inward, which a docs rail should not do.
+
+**`hide: toc` pages get their own rule.** Material still renders the sidebar element, just
+`hidden`, so the pair's `margin-right: auto` lands on a zero-width box and every pixel of
+slack piles up on the left, shunting the column right. Those pages centre on their own and
+widen to 64rem — hiding the table of contents is usually a request for a wider page.
+
+Padding lives on `.md-content`, not `.md-content__inner`: the breadcrumb is a **sibling** of
+`__inner`, so padding there left the two misaligned by 77px. Breadcrumb, `h1` and body text
+now share one left edge.
+
+**Contrast verified in the live page, both schemes — everything passes AA:**
+
+| | Light | Dark |
+|---|---|---|
+| Body text | 21.0 | 15.4 |
+| Links | 4.65 | 7.27 |
+| Active tab | 4.92 | — |
+| Table header / muted | 6.38 | 8.39 |
+
+Two tokens exist purely to hold that line: `--gemma-text-muted` uses `n600` (n500 measured
+4.46 on the tint), and `--gemma-accent-on-tint` (`#2F63E6`) carries small brand-coloured text
+on tinted surfaces, where plain `blue-deep` measures 4.41.
+
 ## Phase 2 status — not finished
 
 | Step | State |
diff --git a/docs/deployment/huggingface.md b/docs/deployment/huggingface.md
index c610f5e..89a650a 100644
--- a/docs/deployment/huggingface.md
+++ b/docs/deployment/huggingface.md
@@ -4,7 +4,7 @@
 Generating text, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models. This tutorial shows you how to get started running Gemma using Hugging Face Transformers using both text and image input to generate text content. The Transformers Python library provides a API for accessing pre-trained generative AI models, including Gemma. For more information, see the [Transformers](https://huggingface.co/docs/transformers/en/index) documentation.
 
 !!! Note
-    Gemma 3 and later models support input of both images and text. Earlier versions of Gemma only support text input, except for some variants, including [PaliGemma](../get-started/setup.md).
+    Gemma 3 and later models support input of both images and text. Earlier versions of Gemma only support text input, except for some variants, including [PaliGemma](../get-started/quickstart.md).
 
 
 ### Install Python packages
diff --git a/docs/deployment/keras.md b/docs/deployment/keras.md
index 70ecafd..606fdcf 100644
--- a/docs/deployment/keras.md
+++ b/docs/deployment/keras.md
@@ -3,7 +3,7 @@
 
 Generating content, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models. This tutorial shows you how to get started running Gemma using Keras, including generating text content with text and image input. [Keras](https://keras.io/) provides implementations for running Gemma and other models using JAX, PyTorch, and TensorFlow. If you're new to Keras, you might want to read [Getting started with Keras](https://keras.io/getting_started/) before you begin.
 
-Gemma 3 and later models support text and image input. Earlier versions of Gemma only support text input, except for some variants, including [PaliGemma](../get-started/setup.md).
+Gemma 3 and later models support text and image input. Earlier versions of Gemma only support text input, except for some variants, including [PaliGemma](../get-started/quickstart.md).
 !!! Note
     This notebook won't run on T4 if you try with Gemma 4. Use L4 or higher GPUs.
     
diff --git a/docs/deployment/langchain.md b/docs/deployment/langchain.md
index 12483c6..61d9633 100644
--- a/docs/deployment/langchain.md
+++ b/docs/deployment/langchain.md
@@ -108,7 +108,7 @@ content='Output:\nSure, here is the answer:\n\n3 + 3 = 6<'
 
 This section shows you how to download Gemma from Kaggle and then run the model.
 
-To complete this section, you'll first need to complete the setup instructions at [Gemma setup](../get-started/setup.md).
+To complete this section, you'll first need to complete the setup instructions at [Gemma setup](../get-started/quickstart.md).
 
 Then move on to the next section, where you'll set environment variables for your Colab environment.
 
diff --git a/docs/get-started/setup.md b/docs/get-started/setup.md
deleted file mode 100644
index 7acd9cc..0000000
--- a/docs/get-started/setup.md
+++ /dev/null
@@ -1,58 +0,0 @@
-# Legacy Gemma setup
-
-!!! Note
-    This is only relevant for Gemma 3 and prior versions.
-
-
-This page provides setup instructions for using Gemma in Colab. Some of the
-instructions are applicable to other development environments as well.
-
-## Get access to Gemma
-
-Before using Gemma for the first time, you must request access to the
-model through Kaggle. As part of the process, you'll have to use a Kaggle
-account to accept the Gemma use policy and license terms.
-
-If you don't already have a Kaggle account, you can register for one at
-[kaggle.com](https://www.kaggle.com). Then complete the following steps:
-
-1. Go to the [Gemma model card](https://www.kaggle.com/models/google/gemma) and select **Request Access**.
-2. Complete the consent form and accept the terms and conditions.
-
-## Select a Colab runtime
-
-To complete a Colab tutorial, you must have a Colab runtime with sufficient
-resources to run the Gemma model. To [get started](quickstart.md), you can
-use a T4 GPU:
-
-1. In the upper-right of the Colab window, select ▾ (**Additional connection options**).
-2. Select **Change runtime type**.
-3. Under **Hardware accelerator** , select **T4 GPU**.
-
-## Configure your API key
-
-To use Gemma, you must provide your Kaggle username and a Kaggle API key. To
-generate and configure these values, follow these steps:
-
-1. To generate a Kaggle API key, go to the **Account** tab of your Kaggle [user
-   profile](https://www.kaggle.com/settings) and select **Create New Token** . This will trigger the download of a `kaggle.json` file containing your API credentials.
-2. Open `kaggle.json` in a text editor. The contents should look something like
-   this:
-
-   ```text
-   {"username":"your_username","key":"012345678abcdef012345678abcdef1a"}
-   ```
-
-3. In Colab, select **Secrets** (🔑) and add your Kaggle username and Kaggle
-   API key. Store your username under the name `KAGGLE_USERNAME` and your API
-   key under the name `KAGGLE_KEY`.
-
-!!! Note
-    Kaggle notebooks have a key storage feature under **Add-ons** \> **Secrets**, along with instructions for accessing stored keys.
-
-
-Now you're ready to complete the remaining setup steps in Colab. If you're
-working through a Colab tutorial, go to Colab and set the environment variables.
-
-!!! Tip
-    As an alternative to setting environment variables, you can use `kagglehub` to [authenticate](https://github.com/Kaggle/kagglehub?tab=readme-ov-file#authenticate).
diff --git a/docs/learn/business-email-assistant.md b/docs/learn/business-email-assistant.md
index c16376b..c1fa431 100644
--- a/docs/learn/business-email-assistant.md
+++ b/docs/learn/business-email-assistant.md
@@ -155,7 +155,7 @@ respectively.
 To set the environment variables:
 
 1. Obtain your Kaggle username and your token key by following the instructions in the [Kaggle documentation](https://www.kaggle.com/docs/api#authentication).
-2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
+2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/quickstart.md) page.
 3. Create environment variable files for the project, by creating a `.env` text file at *each* these locations in your clone of the project:
 
    ``` { .no-copy .output }
diff --git a/docs/learn/personal-code-assistant.md b/docs/learn/personal-code-assistant.md
index 2ec4eec..393fec4 100644
--- a/docs/learn/personal-code-assistant.md
+++ b/docs/learn/personal-code-assistant.md
@@ -184,7 +184,7 @@ file, which is used by the web service program to download the model.
 To set the environment variables:
 
 1. Obtain your Kaggle username and your API token by following the instructions in the [Kaggle documentation](https://www.kaggle.com/docs/api#authentication).
-2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
+2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/quickstart.md) page.
 3. Create an environment variable file for the project, by creating a
    `.env` text file at this location in your clone of the project:
 
diff --git a/docs/models/recurrentgemma/fine-tuning.md b/docs/models/recurrentgemma/fine-tuning.md
index e14d88d..371bcf1 100644
--- a/docs/models/recurrentgemma/fine-tuning.md
+++ b/docs/models/recurrentgemma/fine-tuning.md
@@ -12,7 +12,7 @@ The following sections explain the steps for preparing a notebook to use a Recur
 
 ### Set up Kaggle access for Gemma
 
-To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](../../get-started/setup.md) with a few exceptions:
+To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](../../get-started/quickstart.md) with a few exceptions:
 
 - Get access to RecurrentGemma (instead of Gemma) on [kaggle.com](https://www.kaggle.com/models/google/recurrentgemma).
 - Select a Colab runtime with sufficient resources to run the RecurrentGemma model.
diff --git a/docs/models/recurrentgemma/inference.md b/docs/models/recurrentgemma/inference.md
index 44a6aec..0313669 100644
--- a/docs/models/recurrentgemma/inference.md
+++ b/docs/models/recurrentgemma/inference.md
@@ -11,7 +11,7 @@ The following sections explain the steps for preparing a notebook to use a Recur
 
 ### Set up Kaggle access for Gemma
 
-To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](../../get-started/setup.md) with a few exceptions:
+To complete this tutorial, you first need to follow the setup instructions *similar* to [Gemma setup](../../get-started/quickstart.md) with a few exceptions:
 
 - Get access to RecurrentGemma (instead of Gemma) on [kaggle.com](https://www.kaggle.com/models/google/recurrentgemma).
 - Select a Colab runtime with sufficient resources to run the RecurrentGemma model.
diff --git a/docs/stylesheets/content.css b/docs/stylesheets/content.css
new file mode 100644
index 0000000..09e34d1
--- /dev/null
+++ b/docs/stylesheets/content.css
@@ -0,0 +1,215 @@
+/* ===================================================================
+   CONTENT — the elements pages are actually made of.
+
+   Owns: tables, admonitions, collapsible sections, inline code,
+         content tabs, blockquotes.
+   Does NOT own: colours (gemma.css), page width (layout.css),
+         cards/badges/steps (components.css), chrome (nav.css).
+
+   The through-line: state is carried by *surface tint*, not by heavy
+   borders. One hairline plus a tinted fill reads calmer than an
+   outlined box, and stays legible in both schemes.
+   =================================================================== */
+
+/* -------------------------------------------------------------------
+   Tables
+   Tinted header, hairline rows, no vertical rules. Wide tables scroll
+   inside their own container rather than widening the page.
+   ------------------------------------------------------------------- */
+.md-typeset table:not([class]) {
+  display: table;
+  width: 100%;
+  font-size: 0.82rem;
+  border: 1px solid var(--gemma-border);
+  border-radius: var(--gemma-radius-md);
+  border-collapse: separate;
+  border-spacing: 0;
+  box-shadow: none;
+  overflow: hidden;
+}
+
+.md-typeset table:not([class]) th {
+  padding: var(--gemma-space-2) var(--gemma-space-3);
+  background-color: var(--gemma-surface-sunken);
+  font-size: 0.75rem;
+  font-weight: 600;
+  letter-spacing: 0.02em;
+  text-transform: uppercase;
+  color: var(--gemma-text-muted);
+  border-bottom: 1px solid var(--gemma-border);
+}
+
+.md-typeset table:not([class]) td {
+  padding: var(--gemma-space-2) var(--gemma-space-3);
+  border-top: 1px solid var(--gemma-border);
+  vertical-align: top;
+}
+
+.md-typeset table:not([class]) tbody tr:first-child td {
+  border-top: 0;
+}
+
+.md-typeset table:not([class]) tbody tr:hover {
+  background-color: var(--gemma-surface-sunken);
+}
+
+/* -------------------------------------------------------------------
+   Admonitions
+   A tinted panel with a single accent rail on the left. Quieter than
+   Material's default full-colour title bar.
+   ------------------------------------------------------------------- */
+.md-typeset .admonition,
+.md-typeset details {
+  margin: var(--gemma-space-5) 0;
+  padding: var(--gemma-space-3) var(--gemma-space-4);
+  font-size: 0.83rem;
+  background-color: var(--gemma-surface-sunken);
+  border: 1px solid var(--gemma-border);
+  border-left: 3px solid var(--gemma-n300);
+  border-radius: var(--gemma-radius-md);
+  box-shadow: none;
+}
+
+.md-typeset .admonition-title,
+.md-typeset summary {
+  margin: 0 0 var(--gemma-space-1);
+  padding: 0 0 0 var(--gemma-space-5);
+  font-size: 0.83rem;
+  font-weight: 600;
+  background-color: transparent;
+  border: 0;
+}
+
+.md-typeset .admonition-title::before,
+.md-typeset summary::before {
+  top: 0.1em;
+  left: 0;
+  width: 1rem;
+  height: 1rem;
+}
+
+.md-typeset .admonition > :last-child,
+.md-typeset details > :last-child {
+  margin-bottom: 0;
+}
+
+/* Material tints each type at `.md-typeset .admonition.note` (0,3,1), which outranks the
+   base rule above. Matching that specificity keeps every panel on the same neutral surface;
+   the type is carried by the rail colour instead. */
+.md-typeset .admonition[class],
+.md-typeset details[class] {
+  background-color: var(--gemma-surface-sunken);
+}
+
+/* Accent rail and icon tint per type. */
+.md-typeset .admonition.note,
+.md-typeset details.note {
+  border-left-color: var(--gemma-blue);
+}
+
+.md-typeset .note > .admonition-title::before,
+.md-typeset .note > summary::before {
+  background-color: var(--gemma-blue);
+}
+
+.md-typeset .admonition.tip,
+.md-typeset details.tip {
+  border-left-color: var(--gemma-status-success);
+}
+
+.md-typeset .admonition.warning,
+.md-typeset details.warning {
+  border-left-color: var(--gemma-status-warning);
+}
+
+.md-typeset .admonition.danger,
+.md-typeset details.danger {
+  border-left-color: var(--gemma-status-danger);
+}
+
+/* -------------------------------------------------------------------
+   Collapsible sections
+   ------------------------------------------------------------------- */
+.md-typeset details {
+  padding-bottom: var(--gemma-space-3);
+}
+
+.md-typeset details > summary {
+  cursor: pointer;
+  list-style: none;
+}
+
+.md-typeset details[open] > summary {
+  margin-bottom: var(--gemma-space-3);
+  padding-bottom: var(--gemma-space-2);
+  border-bottom: 1px solid var(--gemma-border);
+}
+
+/* -------------------------------------------------------------------
+   Inline code
+   A tinted chip, not a coloured shout. Used constantly in these docs
+   for model names and flags, so it has to sit quietly inside a
+   sentence while still being unmistakable.
+   ------------------------------------------------------------------- */
+.md-typeset code {
+  padding: 0.1em 0.35em;
+  font-size: 0.85em;
+  background-color: var(--gemma-surface-sunken);
+  border: 1px solid var(--gemma-border);
+  border-radius: var(--gemma-radius-sm);
+  color: var(--md-default-fg-color);
+  word-break: normal;
+}
+
+/* Inside a fence, the block carries the surface -- the inline chip styling above would
+   double up. Keep Material's scheme-aware code background rather than blanking it, which
+   left fenced blocks with no fill at all. */
+.md-typeset pre > code,
+.md-typeset .highlight code {
+  padding: var(--gemma-space-3) var(--gemma-space-4);
+  background-color: var(--md-code-bg-color);
+  border: 1px solid var(--gemma-border);
+  border-radius: var(--gemma-radius-md);
+}
+
+/* Line-numbered and annotated blocks nest differently; don't double the border there. */
+.md-typeset .highlighttable .linenos,
+.md-typeset .highlighttable .code {
+  border: 0;
+}
+
+.md-typeset a code {
+  color: var(--gemma-accent);
+  border-color: color-mix(in srgb, var(--gemma-accent) 30%, transparent);
+}
+
+/* -------------------------------------------------------------------
+   Content tabs
+   The selected tab is the same language as the top nav: brand colour
+   plus a 2px rule.
+   ------------------------------------------------------------------- */
+.md-typeset .tabbed-labels {
+  box-shadow: inset 0 -1px var(--gemma-border);
+}
+
+.md-typeset .tabbed-labels > label {
+  padding: var(--gemma-space-2) var(--gemma-space-3);
+  font-size: 0.8rem;
+  font-weight: 500;
+  color: var(--gemma-text-muted);
+}
+
+.md-typeset .tabbed-set > input:checked + label {
+  color: var(--gemma-accent);
+  font-weight: 600;
+  border-color: var(--gemma-blue);
+}
+
+/* -------------------------------------------------------------------
+   Blockquote
+   ------------------------------------------------------------------- */
+.md-typeset blockquote {
+  padding-left: var(--gemma-space-4);
+  color: var(--gemma-text-muted);
+  border-left: 2px solid var(--gemma-border);
+}
diff --git a/docs/stylesheets/gemma.css b/docs/stylesheets/gemma.css
index b7cca58..847d9df 100644
--- a/docs/stylesheets/gemma.css
+++ b/docs/stylesheets/gemma.css
@@ -42,6 +42,55 @@
   --gemma-accent: var(--gemma-blue-deep);   /* text-safe brand colour   */
   --gemma-accent-fill: var(--gemma-blue);   /* backgrounds, bars, icons */
 
+  /* -----------------------------------------------------------------
+     Neutral ramp
+
+     Tinted very slightly blue so greys belong to the palette rather
+     than reading as a separate neutral system. This is where the calm
+     comes from: surfaces differ by *tint*, not by borders everywhere.
+     ----------------------------------------------------------------- */
+  --gemma-n0: #ffffff;
+  --gemma-n50: #f7f9fc;   /* page tint behind white surfaces */
+  --gemma-n100: #eef1f6;
+  --gemma-n150: #e4e9f0;  /* hairlines */
+  --gemma-n200: #cfd6e0;
+  --gemma-n300: #aab3c2;
+  --gemma-n400: #8b95a6;
+  --gemma-n500: #6b7486;
+  --gemma-n600: #545c6b;
+  --gemma-n700: #3f4653;
+  --gemma-n800: #2b303a;
+  --gemma-n900: #202124;  /* = --gemma-ink */
+
+  /* Semantic surfaces, remapped for dark mode below. */
+  --gemma-surface: var(--gemma-n0);
+  --gemma-surface-sunken: var(--gemma-n50);
+  --gemma-border: var(--gemma-n150);
+  /* n500 measures 4.46:1 on the n50 tint -- just under AA for the 12-13px text this is
+     used for (table headers, nav, tabs). n600 clears it at 6.38:1. */
+  --gemma-text-muted: var(--gemma-n600);
+
+  /* Nav links sit one step stronger than muted so the hierarchy survives.
+     Scheme-aware: a fixed neutral here reads as 2.9:1 in dark mode. */
+  --gemma-text-secondary: var(--gemma-n700);
+
+  /* Brand blue for small text on the tinted header/table surfaces. Plain blue-deep measures
+     4.41:1 there -- fine on white, under AA on the tint. */
+  --gemma-accent-on-tint: #2F63E6;
+
+  /* Status accents for admonition rails. Categorical, so not derived
+     from the brand blue -- they have to stay distinguishable. */
+  --gemma-status-info: var(--gemma-blue);
+  --gemma-status-success: #10b981;
+  --gemma-status-warning: #f59e0b;
+  --gemma-status-danger: #ef4444;
+
+  /* Header: a near-white blue tint, not a slab of brand colour. The blue
+     appears only on the active tab indicator, where 3:1 is sufficient. */
+  --gemma-header-bg: var(--gemma-n50);
+  --gemma-header-fg: var(--gemma-n700);
+  --gemma-header-border: var(--gemma-n150);
+
   /* -----------------------------------------------------------------
      Spacing scale (rem). Use these rather than ad-hoc values.
      ----------------------------------------------------------------- */
@@ -100,10 +149,22 @@
 /* ===================================================================
    Dark scheme
    =================================================================== */
+[data-md-color-scheme="slate"][data-md-color-primary],
 [data-md-color-scheme="slate"] {
   --gemma-accent: var(--gemma-blue-light);
   --gemma-accent-fill: var(--gemma-blue-light);
 
+  --gemma-surface: #1c1d21;
+  --gemma-surface-sunken: #17181c;
+  --gemma-border: #303239;
+  --gemma-text-muted: var(--gemma-n300);
+  --gemma-text-secondary: var(--gemma-n200);
+
+  --gemma-accent-on-tint: var(--gemma-blue-light);
+  --gemma-header-bg: #17181c;
+  --gemma-header-fg: var(--gemma-n200);
+  --gemma-header-border: #303239;
+
   --gemma-shadow-sm: 0 1px 2px rgb(0 0 0 / 40%);
   --gemma-shadow-md: 0 1px 3px rgb(0 0 0 / 45%), 0 4px 12px rgb(0 0 0 / 30%);
   --gemma-shadow-lg: 0 2px 6px rgb(0 0 0 / 50%), 0 12px 32px rgb(0 0 0 / 40%);
@@ -137,7 +198,12 @@
    Everything downstream (links, header, focus rings, selection) picks
    these up, so component sheets never need to name a brand colour.
    =================================================================== */
-:root {
+/* Material scopes its palette at `[data-md-color-scheme=X][data-md-color-primary=Y]`
+   (specificity 0,2,0). A plain `[data-md-color-scheme=X]` is 0,1,0 and loses regardless of
+   load order, so the second attribute is required rather than decorative. */
+:root,
+[data-md-color-scheme="default"][data-md-color-primary],
+[data-md-color-scheme="default"] {
   --md-primary-fg-color: var(--gemma-blue-deep);
   --md-primary-fg-color--light: var(--gemma-blue-light);
   --md-primary-fg-color--dark: var(--gemma-navy-800);
@@ -147,6 +213,7 @@
   --md-typeset-a-color: var(--gemma-accent);
 }
 
+[data-md-color-scheme="slate"][data-md-color-primary],
 [data-md-color-scheme="slate"] {
   --md-primary-fg-color: var(--gemma-ink);
   --md-primary-fg-color--light: var(--gemma-blue-light);
diff --git a/docs/stylesheets/layout.css b/docs/stylesheets/layout.css
index 446179a..78b641f 100644
--- a/docs/stylesheets/layout.css
+++ b/docs/stylesheets/layout.css
@@ -1,122 +1,184 @@
 /* ===================================================================
-   Global Typography & Layout Refinement (desktop only)
+   LAYOUT — page grid, column widths, typography scale.
+
+   Owns: how much room text gets and how big it is.
+   Does NOT own: colours (gemma.css), header/nav chrome (nav.css),
+   components (components.css).
+
+   The shape:
+
+     sidebar 15rem │ ←auto→ │ content 55rem │ ←auto→ │ toc 13rem
+
+   The sidebar sits flush left and the *content* is centred in whatever
+   space is left, rather than the content stretching to fill it. Long
+   measures are the main thing that makes documentation tiring to read;
+   at the old 100rem the lines ran to ~180 characters.
    =================================================================== */
-@media screen and (min-width: 60em) {
-  /* 1. Base font size anchor */
-  html,
-  body {
-    font-size: 100% !important;
-  }
 
-  /* 2. Expanded Sidebar & Navigation Widths */
-  .md-sidebar,
-  .md-sidebar--primary,
-  .md-sidebar--secondary,
-  .md-sidebar__inner,
-  .md-sidebar__scrollwrap,
-  .md-nav {
-    width: 20rem !important;
-  }
+/* -------------------------------------------------------------------
+   Root scale
 
-  /* 3. Site-wide Container Max Width */
-  .md-grid {
-    max-width: 100rem !important;
-  }
+   Material ships `html { font-size: 125% }`, which makes 1rem = 20px and
+   every rem value in this file 25% larger than it reads. Anchoring to
+   100% keeps the scale honest: 0.875rem is 14px, as intended.
+   ------------------------------------------------------------------- */
+html {
+  font-size: 100%;
+}
 
-  /* 4. Body Typography */
-  .md-typeset {
-    font-size: 0.97rem !important;
-    line-height: 1.7 !important;
-  }
+/* -------------------------------------------------------------------
+   Grid
+   ------------------------------------------------------------------- */
+.md-grid {
+  max-width: 100%;
+}
 
-  .md-typeset p,
-  .md-typeset ul,
-  .md-typeset ol {
-    line-height: 1.7;
-    margin-bottom: 0.95em;
-  }
+.md-main__inner {
+  margin-top: 0;
+}
 
-  /* 5. Headings */
-  .md-typeset h1 {
-    font-size: 2.45rem !important;
-    line-height: 1.25;
-    margin-bottom: 0.6em;
-  }
+/* The sidebar stays flush left at every width. Content and the table of
+   contents form a pair that is centred in the space the sidebar leaves,
+   using auto margins on the pair's outer edges — so the table of contents
+   follows the text instead of being pinned to the viewport edge.
 
-  .md-typeset h2 {
-    font-size: 1.75rem !important;
-    line-height: 1.35;
-    margin-top: 1.6em;
-    margin-bottom: 0.5em;
+   Capping `.md-main__inner` instead would centre all three columns and
+   push the sidebar inward, which is not what a docs rail should do. */
+@media screen and (min-width: 76.25em) {
+  .md-sidebar--primary {
+    width: 17rem;
   }
 
-  .md-typeset h3 {
-    font-size: 1.38rem !important;
-    line-height: 1.4;
+  .md-sidebar--secondary {
+    width: 13rem;
+    margin-right: auto;   /* pairs with .md-content's margin-left */
+    padding-left: var(--gemma-space-6);
   }
 
-  /* 6. Code Blocks & Inline Code */
-  .md-typeset code,
-  .md-typeset pre {
-    font-size: 0.95rem !important;
+  /* Padding lives here, not on `__inner`: the breadcrumb is a *sibling*
+     of `__inner`, so padding applied there left the two misaligned by
+     77px. Constraining the shared parent keeps every child on one edge. */
+  .md-content {
+    max-width: 56rem;
+    margin-left: auto;
+    padding-inline: var(--gemma-space-6);
   }
 
-  .md-typeset pre {
-    line-height: 1.55 !important;
+  /* Pages with `hide: toc` still render the sidebar element, just hidden —
+     so `margin-right: auto` above lands on a zero-width box and every
+     pixel of slack piles up on the left, shunting the column right.
+     Balance it here, and take some of the freed width: hiding the table
+     of contents is usually a request for a wider page. */
+  .md-sidebar--secondary[hidden] ~ .md-content {
+    max-width: 64rem;
+    margin-inline: auto;
   }
 
-  .highlight span.filename,
-  .md-typeset .filename,
-  .md-typeset .code-title,
-  .md-typeset figcaption {
-    font-size: 0.95rem !important;
-    font-weight: 700 !important;
+  /* Material sets an asymmetric 1.2rem margin and its own max-width via
+     `… .md-sidebar--primary:not([hidden]) ~ .md-content .md-content__inner`
+     (0,4,0). A bare `.md-content__inner` rule is 0,1,0 and loses. */
+  .md-content__inner,
+  [dir="ltr"] .md-sidebar--primary:not([hidden]) ~ .md-content .md-content__inner,
+  [dir="ltr"] .md-sidebar--secondary:not([hidden]) ~ .md-content .md-content__inner {
+    max-width: none;
+    margin-inline: 0;
+    padding-inline: 0;
   }
 
-  /* 7. Admonitions & Collapsible Details */
-  .md-typeset .admonition,
-  .md-typeset details {
-    font-size: 0.98rem !important;
+  /* Material reserves 88px of right padding on the sidebar's inner
+     wrapper, leaving the nav only 184px of a 272px rail — which is what
+     wrapped "Image Understanding" onto two lines. */
+  .md-sidebar--primary .md-sidebar__inner {
+    padding-right: var(--gemma-space-2);
   }
+}
 
-  .md-typeset .admonition-title,
-  .md-typeset details summary {
-    font-size: 1.03rem !important;
-    font-weight: 700 !important;
-  }
+/* The breadcrumb carries its own inset, which left it 19px off the heading
+   it sits above. Both now start on the same edge. */
+.md-content .md-path {
+  margin-inline: 0;
+  padding-inline: 0;
+  font-size: 0.75rem;
+}
 
-  /* 8. Tables & Data Displays */
-  .md-typeset table:not([class]),
-  .md-typeset table,
-  .md-typeset th,
-  .md-typeset td {
-    font-size: 0.95rem !important;
-  }
+/* Wide pages: benchmark tables, image-dense visual guides. Add
+   `wide: true` to the page's frontmatter or `.wide` to a container. */
+.md-content__inner:has(.wide),
+body[data-wide] .md-content__inner {
+  max-width: 72rem;
+}
 
-  .md-typeset th,
-  .md-typeset td {
-    padding: 0.75em 1.1em !important;
-  }
+/* Individual blocks can break out of the measure without the whole
+   page doing so. */
+.md-typeset .wide {
+  max-width: none;
+}
 
-  /* 9. Tabbed Content & Provider Switchers */
-  .md-typeset .tabbed-labels>label,
-  .md-typeset .tabbed-set>label,
-  .md-typeset [class*="tabbed"]>label {
-    font-size: 0.98rem !important;
-    font-weight: 500 !important;
-  }
+/* -------------------------------------------------------------------
+   Typography
+   14px base, tighter leading than the previous 1.7. The old setting
+   was airy vertically while cramped horizontally -- the opposite of
+   what long-form reading wants.
+   ------------------------------------------------------------------- */
+.md-typeset {
+  font-size: 0.875rem;
+  line-height: 1.65;
+}
 
-  /* 10. Navigation links, footnotes & TOC */
-  .md-nav__link,
-  .md-tabs__link,
-  .md-sidebar__inner,
-  .md-typeset .footnote,
-  .md-typeset .md-tag,
-  .md-path {
-    font-size: 0.95rem !important;
-  }
+.md-typeset p,
+.md-typeset ul,
+.md-typeset ol {
+  margin-bottom: var(--gemma-space-4);
+}
 
-  .md-sidebar--secondary .md-nav__link {
-    font-size: 0.95rem !important;
-  }
+.md-typeset li {
+  margin-bottom: var(--gemma-space-1);
+}
+
+.md-typeset h1 {
+  margin-bottom: var(--gemma-space-5);
+  font-size: 1.75rem;
+  font-weight: 700;
+  line-height: 1.25;
+  letter-spacing: -0.02em;
+  color: var(--md-default-fg-color);
+}
+
+.md-typeset h2 {
+  margin-top: var(--gemma-space-7);
+  margin-bottom: var(--gemma-space-3);
+  font-size: 1.3rem;
+  font-weight: 700;
+  line-height: 1.3;
+  letter-spacing: -0.01em;
+}
+
+.md-typeset h3 {
+  margin-top: var(--gemma-space-5);
+  margin-bottom: var(--gemma-space-2);
+  font-size: 1.05rem;
+  font-weight: 700;
+  line-height: 1.4;
+}
+
+.md-typeset h4 {
+  margin-top: var(--gemma-space-4);
+  font-size: 0.9rem;
+  font-weight: 700;
+}
+
+/* -------------------------------------------------------------------
+   Reading rhythm
+   ------------------------------------------------------------------- */
+.md-typeset hr {
+  margin: var(--gemma-space-6) 0;
+  border-bottom: 1px solid var(--gemma-border);
+}
+
+.md-typeset a {
+  text-decoration: none;
+}
+
+.md-typeset a:hover {
+  text-decoration: underline;
 }
diff --git a/docs/stylesheets/nav.css b/docs/stylesheets/nav.css
new file mode 100644
index 0000000..a54d207
--- /dev/null
+++ b/docs/stylesheets/nav.css
@@ -0,0 +1,163 @@
+/* ===================================================================
+   NAV — header, top tabs, sidebar and table of contents.
+
+   Owns: the site chrome around the content.
+   Does NOT own: colours (gemma.css), content width (layout.css).
+
+   The header is a near-white tint rather than a slab of brand colour.
+   Brand blue appears once, on the active tab indicator — which is also
+   the one place --gemma-blue is safe, since a 2px rule has no contrast
+   requirement to meet.
+   =================================================================== */
+
+/* -------------------------------------------------------------------
+   Header
+   ------------------------------------------------------------------- */
+.md-header {
+  background-color: var(--gemma-header-bg);
+  color: var(--gemma-header-fg);
+  border-bottom: 1px solid var(--gemma-header-border);
+  box-shadow: none;
+}
+
+.md-header--shadow {
+  box-shadow: none;
+}
+
+.md-header__title {
+  font-size: 0.9rem;
+  font-weight: 700;
+}
+
+.md-header__button {
+  color: var(--gemma-header-fg);
+}
+
+/* Search sits on the tinted header, so it needs a surface of its own. */
+.md-search__form {
+  background-color: var(--gemma-surface);
+  border: 1px solid var(--gemma-border);
+  border-radius: var(--gemma-radius-md);
+  box-shadow: none;
+}
+
+.md-search__form:hover {
+  background-color: var(--gemma-surface);
+  border-color: var(--gemma-n200);
+}
+
+.md-search__input {
+  font-size: 0.8rem;
+}
+
+.md-search__input::placeholder,
+.md-search__icon {
+  color: var(--gemma-text-muted);
+}
+
+/* -------------------------------------------------------------------
+   Top tabs
+   The active tab is the strongest signal on the page chrome: brand
+   colour plus a 2px rule, rather than a faint weight change.
+   ------------------------------------------------------------------- */
+.md-tabs {
+  background-color: var(--gemma-header-bg);
+  color: var(--gemma-header-fg);
+  border-bottom: 1px solid var(--gemma-header-border);
+  box-shadow: none;
+}
+
+.md-tabs__list {
+  gap: var(--gemma-space-1);
+}
+
+.md-tabs__link {
+  margin-top: 0;
+  padding: var(--gemma-space-3) var(--gemma-space-3);
+  font-size: 0.8rem;
+  font-weight: 500;
+  opacity: 1;
+  color: var(--gemma-text-muted);
+  border-bottom: 2px solid transparent;
+}
+
+.md-tabs__link:hover {
+  color: var(--md-default-fg-color);
+}
+
+/* Zensical marks the active tab on the <li>, not the <a>. */
+.md-tabs__item--active > .md-tabs__link,
+.md-tabs__item--active > .md-tabs__link:hover,
+.md-tabs__link--active {
+  color: var(--gemma-accent-on-tint);
+  font-weight: 600;
+  border-bottom-color: var(--gemma-blue);
+}
+
+/* -------------------------------------------------------------------
+   Sidebar
+   Quiet by default; the active item is the only thing with weight.
+   ------------------------------------------------------------------- */
+.md-nav {
+  font-size: 0.8rem;
+}
+
+.md-nav__title {
+  font-size: 0.7rem;
+  font-weight: 700;
+  letter-spacing: 0.04em;
+  text-transform: uppercase;
+  color: var(--gemma-text-muted);
+}
+
+.md-nav__link {
+  margin-block: 0;
+  padding-block: var(--gemma-space-1);
+  color: var(--gemma-text-secondary);
+}
+
+.md-nav__link:hover {
+  color: var(--md-default-fg-color);
+}
+
+/* Material's own rule is `.md-nav--primary .md-nav__item .md-nav__link--active`
+   (0,3,0); anything less specific silently loses. */
+.md-nav--primary .md-nav__item .md-nav__link--active,
+.md-nav--primary .md-nav__item .md-nav__link--active:hover,
+.md-nav__item .md-nav__link--active {
+  font-weight: 600;
+  color: var(--gemma-accent);
+}
+
+/* Section labels inside the sidebar (Text, Multimodal, Advanced…). */
+.md-nav__item--section > .md-nav__link {
+  font-size: 0.72rem;
+  font-weight: 700;
+  letter-spacing: 0.03em;
+  text-transform: uppercase;
+  color: var(--gemma-text-muted);
+}
+
+/* Indentation alone carries the nesting. An earlier hairline rail drew a
+   line down the outside of the whole sidebar, because Material wraps even
+   the top level in a nested list. */
+.md-nav__list .md-nav__list {
+  margin-left: var(--gemma-space-2);
+  padding-left: var(--gemma-space-3);
+}
+
+/* -------------------------------------------------------------------
+   Table of contents
+   ------------------------------------------------------------------- */
+.md-sidebar--secondary .md-nav__title {
+  padding-bottom: var(--gemma-space-2);
+}
+
+.md-sidebar--secondary .md-nav__link {
+  font-size: 0.78rem;
+  color: var(--gemma-text-muted);
+}
+
+.md-sidebar--secondary .md-nav__link--active {
+  color: var(--gemma-accent);
+}
diff --git a/docs/tuning/keras-lora.md b/docs/tuning/keras-lora.md
index b4c5f35..e119ab9 100644
--- a/docs/tuning/keras-lora.md
+++ b/docs/tuning/keras-lora.md
@@ -7,7 +7,7 @@ Generative artificial intelligent (AI) models like Gemma are effective at a vari
 
 ## Setup
 
-To complete this tutorial, you will first need to complete the setup instructions at [Gemma setup](../get-started/setup.md). The Gemma setup instructions show you how to do the following:
+To complete this tutorial, you will first need to complete the setup instructions at [Gemma setup](../get-started/quickstart.md). The Gemma setup instructions show you how to do the following:
 
 - Get access to Gemma on [kaggle.com](https://kaggle.com).
 - Select a Colab runtime with sufficient resources to tune the Gemma model you want to run. [Learn more](../models/gemma-4/index.md#parameter-sizes-and-quantization).
diff --git a/docs/tuning/spoken-language.md b/docs/tuning/spoken-language.md
index a6ff236..1ca226d 100644
--- a/docs/tuning/spoken-language.md
+++ b/docs/tuning/spoken-language.md
@@ -154,7 +154,7 @@ respectively.
 To set the environment variables:
 
 1. Obtain your Kaggle username and your token key by following the instructions in the [Kaggle documentation](https://www.kaggle.com/docs/api#authentication).
-2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/setup.md#get-access-to-gemma) page.
+2. Get access to the Gemma model by following the *Get access to Gemma* instructions in the [Gemma Setup](../get-started/quickstart.md) page.
 3. Create environment variable files for the project, by creating a `.env` text file at *each* these locations in your clone of the project:
 
    ``` { .no-copy .output }
diff --git a/scripts/link_map.toml b/scripts/link_map.toml
index 664ab3b..acc9c2d 100644
--- a/scripts/link_map.toml
+++ b/scripts/link_map.toml
@@ -104,7 +104,7 @@
 "/personal-code-assistant" = "learn/personal-code-assistant.md"
 
 # Now ported (were [planned] in Step 0.5)
-"/setup" = "get-started/setup.md"
+"/setup" = "get-started/quickstart.md"        # legacy setup page removed
 "/core/prompt-structure" = "get-started/prompt-structure.md"
 "/mtp/overview" = "get-started/capabilities/mtp.md"
 "/mtp/mtp" = "get-started/capabilities/mtp.md"
@@ -158,7 +158,6 @@
 "deployment/huggingface.md#vision" = "generate-text-from-image-data"
 "deployment/index.md#select-a-variant" = "select-a-gemma-variant"
 "deployment/index.md#run-generation" = "run-generation-and-inference-requests"
-"get-started/setup.md#get-access" = "get-access-to-gemma"
 "learn/business-email-assistant.md#compute_requirements" = "hardware-requirements"
 "learn/personal-code-assistant.md#compute_requirements" = "hardware-requirements"
 "learn/personal-code-assistant.md#extension" = "vs-code-extension-project"
diff --git a/scripts/sources.toml b/scripts/sources.toml
index d0b5f1a..27bd76b 100644
--- a/scripts/sources.toml
+++ b/scripts/sources.toml
@@ -22,11 +22,6 @@
 # Ported in Step 0.6 to resolve links that Step 0.5 pointed at planned paths. These pages are
 # not in the nav yet -- Step 2.1 places them.
 
-[[page]]
-path = "get-started/setup.md"
-source = "https://ai.google.dev/gemma/docs/setup.md.txt"
-frozen = true
-
 [[page]]
 path = "get-started/prompt-structure.md"
 source = "https://ai.google.dev/gemma/docs/core/prompt-structure.md.txt"
diff --git a/zensical.toml b/zensical.toml
index 4f6c8f9..7ea46f3 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -16,6 +16,8 @@ Copyright &copy; 2026 Google DeepMind
 extra_css = [
   "stylesheets/gemma.css",       # tokens: palette, spacing, radius, shadow
   "stylesheets/layout.css",      # page grid, sidebars, typography scale
+  "stylesheets/nav.css",         # header, tabs, sidebar, table of contents
+  "stylesheets/content.css",     # tables, admonitions, inline code, tabs
   "stylesheets/components.css",  # cards, badges, figures, steps, embeds
   "stylesheets/code.css",        # code and output blocks
   "stylesheets/tokens.css",      # chat-token badges
@@ -57,7 +59,6 @@ nav = [
       { "Multi-Token Prediction" = "get-started/capabilities/mtp.md" },
       "get-started/prompt-formatting.md",
       { "Prompt structure (Gemma 1-3)" = "get-started/prompt-structure.md" },
-      { "Legacy setup" = "get-started/setup.md" },
     ]},
   ]},
 

From a8f4a4c4830d75c5f682ee29f20f6c003e1a4a1e Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Tue, 4 Aug 2026 16:07:56 +0200
Subject: [PATCH 14/20] Fix misaligning pages

---
 docs/stylesheets/layout.css | 31 +++++++++++++++----------------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/docs/stylesheets/layout.css b/docs/stylesheets/layout.css
index 78b641f..20c0773 100644
--- a/docs/stylesheets/layout.css
+++ b/docs/stylesheets/layout.css
@@ -7,12 +7,12 @@
 
    The shape:
 
-     sidebar 15rem │ ←auto→ │ content 55rem │ ←auto→ │ toc 13rem
+     sidebar 17rem │ ←auto→ │ content 56rem │ toc 17rem │ ←auto→
 
-   The sidebar sits flush left and the *content* is centred in whatever
-   space is left, rather than the content stretching to fill it. Long
-   measures are the main thing that makes documentation tiring to read;
-   at the old 100rem the lines ran to ~180 characters.
+   The sidebar stays flush left while equal-width navigation rails keep
+   the article centred in the viewport. Long measures are the main thing
+   that makes documentation tiring to read; at the old 100rem the lines
+   ran to ~180 characters.
    =================================================================== */
 
 /* -------------------------------------------------------------------
@@ -37,10 +37,9 @@ html {
   margin-top: 0;
 }
 
-/* The sidebar stays flush left at every width. Content and the table of
-   contents form a pair that is centred in the space the sidebar leaves,
-   using auto margins on the pair's outer edges — so the table of contents
-   follows the text instead of being pinned to the viewport edge.
+/* The sidebar stays flush left at every width. Equal-width navigation
+   rails bookend the content, while auto margins absorb any extra space so
+   the table of contents follows the text instead of hugging the viewport.
 
    Capping `.md-main__inner` instead would centre all three columns and
    push the sidebar inward, which is not what a docs rail should do. */
@@ -50,7 +49,9 @@ html {
   }
 
   .md-sidebar--secondary {
-    width: 13rem;
+    /* Match the primary rail so the article itself, rather than the
+       article-and-TOC pair, is centred in the viewport. */
+    width: 17rem;
     margin-right: auto;   /* pairs with .md-content's margin-left */
     padding-left: var(--gemma-space-6);
   }
@@ -64,13 +65,11 @@ html {
     padding-inline: var(--gemma-space-6);
   }
 
-  /* Pages with `hide: toc` still render the sidebar element, just hidden —
-     so `margin-right: auto` above lands on a zero-width box and every
-     pixel of slack piles up on the left, shunting the column right.
-     Balance it here, and take some of the freed width: hiding the table
-     of contents is usually a request for a wider page. */
+  /* Pages with `hide: toc` still render the sidebar element, just hidden.
+     Let the wider content occupy the regular content + TOC footprint. This
+     preserves the regular page's left edge while using the freed rail. */
   .md-sidebar--secondary[hidden] ~ .md-content {
-    max-width: 64rem;
+    max-width: 73rem;
     margin-inline: auto;
   }
 

From d385b9967ff7fdb6fe43ed893d250504c391af4f Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Tue, 4 Aug 2026 16:50:39 +0200
Subject: [PATCH 15/20] Major cleanup

---
 .github/workflows/deploy.yml             |  12 +-
 PLAN.md                                  |  53 ++-
 README.md                                |  31 +-
 docs/contributing/components.md          |  15 +-
 docs/models/paligemma/index.md           |   4 +-
 docs/models/paligemma/prompting.md       |   2 +-
 docs/models/shieldgemma/model-card-v2.md |   2 +
 docs/stylesheets/code.css                |  10 -
 docs/stylesheets/components.css          |   2 +-
 docs/stylesheets/content.css             |   5 +
 docs/stylesheets/gemma.css               |   6 +
 docs/stylesheets/home-chrome.css         | 120 -------
 docs/stylesheets/home-stage.css          |  81 -----
 docs/stylesheets/home.css                | 345 +++++++++++--------
 docs/stylesheets/layout.css              |  13 -
 docs/stylesheets/motion.css              |   7 +-
 main.py                                  |   6 -
 overrides/home.html                      | 421 ++---------------------
 pyproject.toml                           |   4 +-
 scripts/lint_docs.py                     |  57 ++-
 uv.lock                                  |   2 +-
 zensical.toml                            |   7 +-
 22 files changed, 391 insertions(+), 814 deletions(-)
 delete mode 100644 docs/stylesheets/code.css
 delete mode 100644 docs/stylesheets/home-chrome.css
 delete mode 100644 docs/stylesheets/home-stage.css
 delete mode 100644 main.py

diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 1b09b24..790c15d 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -30,13 +30,15 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
 
+      - name: Test notebook conversion
+        run: uv run --frozen python scripts/test_convert_notebook.py
+
+      - name: Lint docs
+        run: uv run --frozen python scripts/lint_docs.py --baseline
+
       - name: Build docs
         run: |
-          uv run \
-            --with zensical \
-            --index-url https://pypi.zensical.org/simple/ \
-            --extra-index-url https://pypi.org/simple/ \
-            zensical build
+          uv run --frozen zensical build
 
       - name: Upload Pages artifact
         uses: actions/upload-pages-artifact@56afc609e74202658d3ffba0e8f6dda462b719fa # v3
diff --git a/PLAN.md b/PLAN.md
index 3d5fde4..729fd7d 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -82,7 +82,10 @@ uv run zensical serve
 
 ---
 
-## Current state (audit, Aug 2026)
+## Original state (audit, Aug 2026)
+
+This table is the migration's starting baseline, retained for historical context. Current
+status is recorded in the completed phase sections and the maintenance baseline below.
 
 | Metric | Value |
 |---|---|
@@ -108,6 +111,25 @@ Known defects:
 
 ---
 
+## Maintenance baseline (2026-08-04)
+
+- [x] Zensical is pinned exactly (`zensical==0.0.51`) and deployment runs with
+      `uv run --frozen`, so a package release cannot silently restyle production.
+- [x] Deployment gates the build on converter tests and baseline-aware docs lint.
+- [x] Stylesheets consolidated from 11 to 8: the active homepage CSS moved out of its template,
+      three obsolete homepage sheets were removed, and the one-rule `code.css` was folded into
+      `content.css`.
+- [x] CSS lint rejects template `<style>` blocks in addition to enforcing registration, token
+      use, and the 250-line limit.
+- [x] Internal-link lint validates `#fragments`, including same-page links. The three real broken
+      fragments from the August build were repaired. The larger missing-page list in the captured
+      output was transient incremental-build state; a clean build is the gate.
+- [x] Replaced the placeholder README and removed the unused `main.py` scaffold.
+- [x] Import, conversion, and image-vendoring scripts remain in place by decision. Demo/vendor
+      images are untouched until original PNGs replace them.
+
+---
+
 ## Target navigation
 
 Eight top-level tabs. Capabilities stay under **Get started** — they are fundamental Gemma 4
@@ -575,7 +597,7 @@ Cost is ~195 path updates (39 files, 90 outbound links, 61 inbound, 44 asset ref
       shadow (sm/md/lg), chat-token categories, all with dark-scheme variants.
 - [x] Material's `--md-*` primary/accent/link variables bound to Gemma tokens, so component
       sheets never name a brand colour.
-- [x] `code.css`, `tokens.css` and the landing-page sheets refactored to consume tokens.
+- [x] `content.css`, `tokens.css`, and the landing-page stylesheet consume tokens.
 - [x] `cookbook.css` folded into `components.css` as a generalised card grid
       (`.cookbook-*` kept as aliases — converter output and existing pages emit them).
 - [x] New **`css`** lint check: raw hex only in `gemma.css`, every sheet registered in
@@ -595,22 +617,21 @@ So `--gemma-blue` is a **fill** colour — buttons, bars, icons, step markers 
 text. Text uses `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in
 dark. The header uses `blue-deep` for the same reason.
 
-**Stylesheet layout** (9 files, largest 229 lines):
+**Stylesheet layout** (8 files, each under 250 lines):
 
 | File | Owns |
 |---|---|
 | `gemma.css` | Tokens. **Only file with raw hex.** |
 | `layout.css` | Page grid, sidebars, typography scale |
+| `nav.css` | Header, tabs, sidebars, table of contents |
+| `content.css` | Tables, admonitions, code, content tabs |
 | `components.css` | Cards, badges, figures, steps, embeds |
-| `code.css` | Code and output blocks |
 | `tokens.css` | Chat-token badges |
-| `home-chrome.css` | Landing page: header, tabs, search |
-| `home-stage.css` | Landing page: backdrop |
-| `home.css` | Landing page: hero and feature grid |
+| `home.css` | Landing page: chrome, backdrop, hero |
 | `motion.css` | Transitions + reduced-motion opt-out |
 
-`home.css` was 367 lines and got split three ways — the `css` check caught it immediately,
-which is the check earning its keep on day one.
+The homepage is deliberately one concern again: obsolete styles for an earlier DOM were removed,
+while the live template styles moved into the registered, linted `home.css`.
 
 ### Step 1.2 — Component library ✅
 
@@ -627,8 +648,8 @@ vanishing.
 
 ### Step 1.3 — Motion ✅
 
-- [x] `motion.css` — all transitions in one file, so "does this site animate?" has a single
-      answer and the `prefers-reduced-motion` opt-out cannot be forgotten in a component sheet.
+- [x] `motion.css` owns shared transitions and the site-wide reduced-motion opt-out. The isolated
+      homepage text animation checks the same preference before starting.
 - [x] Blanket reduced-motion rule; anything added later inherits it.
 - [x] Focus rings never animate — keyboard users need them immediately.
 - **Verify:** enable *Reduce motion* in OS settings, reload, confirm hover effects are instant.
@@ -899,8 +920,8 @@ Measured against `docs.stripe.com` rather than eyeballed. What actually differed
 - [x] **Layout reshaped.** Sidebar sits flush left; the *content* is centred in the space that
       remains, rather than stretching to fill it. That is the single biggest change — at 1600px
       lines ran to ~180 characters.
-- [x] `.wide` opt-out for benchmark tables and image-dense guides (the 7-column Gemma 4 table
-      fits at 880px: 814px in an 842px container).
+- [x] Pages that hide the table of contents can use the freed rail without changing the regular
+      article's left edge (the 7-column Gemma 4 table fits at the regular 880px measure).
 - [x] **Header is a near-white tint** (`--gemma-n50`), not a slab of brand blue. Blue appears
       once: the active tab, as colour + a 2px rule.
 - [x] New `nav.css` (chrome) and `content.css` (tables, admonitions, collapsibles, inline code,
@@ -920,7 +941,7 @@ Measured against `docs.stripe.com` rather than eyeballed. What actually differed
 **Layout shape** (verified at 1366px and 2000px):
 
 ```
-sidebar 17rem │  ←auto→  │ content 56rem │ toc 13rem │  ←auto→
+sidebar 17rem │  ←auto→  │ content 56rem │ toc 17rem │  ←auto→
    flush left            └── centred as a pair, TOC follows the text ──┘
 ```
 
@@ -931,7 +952,7 @@ columns instead pushes the sidebar inward, which a docs rail should not do.
 **`hide: toc` pages get their own rule.** Material still renders the sidebar element, just
 `hidden`, so the pair's `margin-right: auto` lands on a zero-width box and every pixel of
 slack piles up on the left, shunting the column right. Those pages centre on their own and
-widen to 64rem — hiding the table of contents is usually a request for a wider page.
+widen to 73rem — hiding the table of contents is usually a request for a wider page.
 
 Padding lives on `.md-content`, not `.md-content__inner`: the breadcrumb is a **sibling** of
 `__inner`, so padding there left the two misaligned by 77px. Breadcrumb, `h1` and body text
@@ -1210,7 +1231,7 @@ port_docs.py  ->  vendor_images.py  ->  lint_docs.py --baseline
 
 *Agents: record surprises, deviations, and anything that changes the plan here.*
 
-- Zensical version pinned via `uv.lock` (`zensical>=0.0.51`). Some Material features listed in
+- Zensical is pinned exactly via `pyproject.toml` and `uv.lock` (`zensical==0.0.51`). Some Material features listed in
   `zensical.toml` may not be implemented yet — verify before relying on them.
 
 **2026-08-03 (Step 0.1)**
diff --git a/README.md b/README.md
index 670da9e..9ab4f4a 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,29 @@
-# docs
-A test space for documentation
+# Gemma documentation
+
+The Google DeepMind Gemma documentation, ported to
+[Zensical](https://github.com/zensical/zensical).
+
+## Local development
+
+The project uses [uv](https://docs.astral.sh/uv/) and pins the documentation
+toolchain in `uv.lock`.
+
+```bash
+uv sync --frozen
+uv run --frozen zensical serve
+```
+
+The preview is available at <http://localhost:8000/>.
+
+## Verification
+
+Run these checks before committing documentation or styling changes:
+
+```bash
+uv run --frozen python scripts/test_convert_notebook.py
+uv run --frozen python scripts/lint_docs.py --baseline
+uv run --frozen zensical build
+```
+
+See `PLAN.md` for the migration roadmap and `docs/contributing/components.md`
+for the CSS tokens and reusable components.
diff --git a/docs/contributing/components.md b/docs/contributing/components.md
index 33bb77b..1df3e96 100644
--- a/docs/contributing/components.md
+++ b/docs/contributing/components.md
@@ -166,10 +166,12 @@ Standard Zensical admonitions, used throughout. The converter produces these fro
 
 ## Motion
 
-All transitions live in `docs/stylesheets/motion.css`, including a blanket
+Shared component transitions live in `docs/stylesheets/motion.css`, including a blanket
 `prefers-reduced-motion` opt-out — anything added later inherits it. Motion is feedback
 (something is interactive, something changed), never decoration.
 
+The isolated homepage animation also checks that preference before it starts.
+
 To check the reduced-motion path, enable *Reduce motion* in your OS accessibility settings and
 reload; hover effects should become instant.
 
@@ -179,13 +181,12 @@ reload; hover effects should become instant.
 |---|---|
 | `gemma.css` | Tokens. The only file with raw hex. |
 | `layout.css` | Page grid, sidebars, typography scale |
+| `nav.css` | Header, tabs, sidebars, table of contents |
+| `content.css` | Tables, admonitions, code, content tabs |
 | `components.css` | Everything on this page |
-| `code.css` | Code and output blocks |
 | `tokens.css` | Chat-token badges (`<start_of_turn>` etc.) |
-| `home-chrome.css` | Landing page: header, tabs, search |
-| `home-stage.css` | Landing page: backdrop |
-| `home.css` | Landing page: hero and feature grid |
+| `home.css` | Landing page: chrome, backdrop, hero |
 | `motion.css` | Transitions and the reduced-motion opt-out |
 
-Each stays under 250 lines so it can be read end-to-end before editing. Adding a ninth
-stylesheet for a new concern is the right move; growing one into a catch-all is not.
+Each stays under 250 lines so it can be read end-to-end before editing. Adding a stylesheet
+for a new concern is the right move; growing one into a catch-all is not.
diff --git a/docs/models/paligemma/index.md b/docs/models/paligemma/index.md
index 55a3d7d..a9b2a2a 100644
--- a/docs/models/paligemma/index.md
+++ b/docs/models/paligemma/index.md
@@ -13,7 +13,7 @@ object detection, and reading text embedded within images.
 PaliGemma 2 is available in 3B, 10B, and 28B parameter sizes, which are based on Gemma 2
 2B, 9B, and 27B models, respectively. The original PaliGemma models are available in the
 3B size. For more information on Gemma model variants, see the
-[Gemma models list](../../get-started/quickstart.md#models-list).
+[Gemma models overview](../index.md).
 PaliGemma model variants support different pixel resolutions for image inputs, including
 224 x 224, 448 x 448, and 896 x 896 pixels.
 
@@ -28,6 +28,8 @@ You can view and download PaliGemma models from the follow sites:
     **Pretrained** PaliGemma models *require* tuning in order to produce useful results. Make sure you perform fine-tuning on these PaliGemma models and test the output *before* deploying them to end users.
 
 
+## Model categories
+
 There are three categories of PaliGemma models:
 
 - **PaliGemma PT** - General purpose pre-trained models that can be fine-tuned on a variety of tasks.
diff --git a/docs/models/paligemma/prompting.md b/docs/models/paligemma/prompting.md
index 5999ffe..660ad99 100644
--- a/docs/models/paligemma/prompting.md
+++ b/docs/models/paligemma/prompting.md
@@ -50,7 +50,7 @@ can find the list of supported languages on
 [GitHub](https://github.com/google/crossmodal-3600/blob/main/web-data/README.md).
 
 !!! Important
-    The [PaliGemma FT](index.md#categories) category of models are trained for specific tasks and *only* support the syntax for the target task.
+    The [PaliGemma FT](index.md#model-categories) category of models are trained for specific tasks and *only* support the syntax for the target task.
 
 
 For a detailed code examples showing how to use this syntax, see the
diff --git a/docs/models/shieldgemma/model-card-v2.md b/docs/models/shieldgemma/model-card-v2.md
index e548d38..0996807 100644
--- a/docs/models/shieldgemma/model-card-v2.md
+++ b/docs/models/shieldgemma/model-card-v2.md
@@ -56,6 +56,8 @@ include any interpretation outside of the safety policy.
 Does the image violate the above policy? Your Answer must start with 'Yes' or 'No'.
 ```
 
+##### Harm types
+
 **Use Case: Image Classification**
 
 | Harm Type | Guideline |
diff --git a/docs/stylesheets/code.css b/docs/stylesheets/code.css
deleted file mode 100644
index 263b2bd..0000000
--- a/docs/stylesheets/code.css
+++ /dev/null
@@ -1,10 +0,0 @@
-/* ===================================================================
-   CODE — output blocks
-   Owns: distinguishing model/program output from source code.
-   Colours come from gemma.css.
-   =================================================================== */
-
-/* Output blocks read as results, not as code you should copy. */
-.md-typeset .output code {
-  background-color: var(--gemma-output-bg);
-}
diff --git a/docs/stylesheets/components.css b/docs/stylesheets/components.css
index b8fca62..ac4e0d7 100644
--- a/docs/stylesheets/components.css
+++ b/docs/stylesheets/components.css
@@ -4,7 +4,7 @@
    Owns: card grids, model badges, figures, video embeds, model-card
          headers, step walkthroughs.
    Does NOT own: colours or scales (gemma.css), page grid (layout.css),
-         code blocks (code.css), chat tokens (tokens.css),
+         code blocks (content.css), chat tokens (tokens.css),
          animation (motion.css).
 
    Every component is documented with live examples in
diff --git a/docs/stylesheets/content.css b/docs/stylesheets/content.css
index 09e34d1..0ebd75e 100644
--- a/docs/stylesheets/content.css
+++ b/docs/stylesheets/content.css
@@ -213,3 +213,8 @@
   color: var(--gemma-text-muted);
   border-left: 2px solid var(--gemma-border);
 }
+
+/* Output blocks read as results, not as source to copy. */
+.md-typeset .output code {
+  background-color: var(--gemma-output-bg);
+}
diff --git a/docs/stylesheets/gemma.css b/docs/stylesheets/gemma.css
index 847d9df..3b893ce 100644
--- a/docs/stylesheets/gemma.css
+++ b/docs/stylesheets/gemma.css
@@ -35,6 +35,12 @@
   --gemma-navy-900: #0e2042;
   --gemma-navy-800: #102a5c;
   --gemma-navy-700: #16366e;
+  --gemma-home-bg: #040d1a;
+  --gemma-home-bg-raised: #07152f;
+  --gemma-home-bg-mid: #040c1e;
+  --gemma-home-bg-deep: #020712;
+  --gemma-blue-strong: #1A4BD6;
+  --gemma-blue-hover: #2563eb;
 
   /* -----------------------------------------------------------------
      Semantic roles — remapped per colour scheme further down.
diff --git a/docs/stylesheets/home-chrome.css b/docs/stylesheets/home-chrome.css
deleted file mode 100644
index c9fa082..0000000
--- a/docs/stylesheets/home-chrome.css
+++ /dev/null
@@ -1,120 +0,0 @@
-/* ===================================================================
-   Landing page — header, tabs and search
-
-   Owns: the transparent navbar, top tabs, search field and modal, and
-         hiding the palette toggle, on the landing page only.
-   Scoped to the landing page via `:has(.gemma-teaser)` so no other
-   page is affected. Colours come from gemma.css.
-   =================================================================== */
-
-/* 1. Hide Dark/Light Mode Palette Toggle on Homepage */
-.gemma-home-header-wrapper .md-header__option,
-.gemma-home-header-wrapper form[data-md-component="palette"] {
-  display: none !important;
-}
-
-/* 2. Transparent Navbar Styling on Homepage (Scoped to wrapper so other pages never get affected!) */
-.gemma-home-header-wrapper .md-header {
-  background-color: transparent !important;
-  box-shadow: none !important;
-  color: var(--gemma-white) !important;
-  border-bottom: 1px solid rgba(255, 255, 255, 0.15) !important;
-  backdrop-filter: blur(8px);
-  -webkit-backdrop-filter: blur(8px);
-}
-
-.gemma-home-header-wrapper .md-header__button,
-.gemma-home-header-wrapper .md-header__topic,
-.gemma-home-header-wrapper .md-header__title,
-.gemma-home-header-wrapper .md-header__title *,
-.gemma-home-header-wrapper .md-logo {
-  color: var(--gemma-white) !important;
-}
-
-/* Top Tabs Navigation on Homepage */
-.gemma-home-header-wrapper .md-tabs {
-  background-color: transparent !important;
-  box-shadow: none !important;
-  border-bottom: 1px solid rgba(255, 255, 255, 0.1) !important;
-}
-
-.gemma-home-header-wrapper .md-tabs__item,
-.gemma-home-header-wrapper .md-tabs__link {
-  color: rgba(255, 255, 255, 0.8) !important;
-  font-weight: 500;
-  transition: color 0.2s ease, opacity 0.2s ease;
-}
-
-.gemma-home-header-wrapper .md-tabs__link:hover,
-.gemma-home-header-wrapper .md-tabs__link:focus,
-.gemma-home-header-wrapper .md-tabs__item--active .md-tabs__link {
-  color: var(--gemma-white) !important;
-  opacity: 1 !important;
-}
-
-.gemma-home-header-wrapper .md-tabs__item--active {
-  border-bottom: 2px solid var(--gemma-blue-light) !important;
-}
-
-/* 3. Search Bar Styling on Homepage */
-.gemma-home-header-wrapper .md-search__button {
-  color: var(--gemma-white) !important;
-  background: rgba(255, 255, 255, 0.12) !important;
-  border-radius: 9999px !important;
-  border: 1px solid rgba(255, 255, 255, 0.2) !important;
-  transition: all 0.2s ease;
-}
-
-.gemma-home-header-wrapper .md-search__button:hover {
-  background: rgba(255, 255, 255, 0.2) !important;
-  border-color: rgba(255, 255, 255, 0.4) !important;
-}
-
-/* Search Modal / Dropdown when clicked on homepage */
-.gemma-home-header-wrapper .md-search__inner {
-  background-color: var(--gemma-ink) !important;
-  color: var(--gemma-white) !important;
-  border: 1px solid rgba(79, 160, 255, 0.4) !important;
-  box-shadow: 0 20px 50px rgba(0, 0, 0, 0.8) !important;
-}
-
-.gemma-home-header-wrapper .md-search__form {
-  background-color: var(--gemma-ink-raised) !important;
-}
-
-.gemma-home-header-wrapper .md-search__input {
-  color: var(--gemma-white) !important;
-  background-color: transparent !important;
-}
-
-.gemma-home-header-wrapper .md-search__input::placeholder {
-  color: rgba(255, 255, 255, 0.55) !important;
-}
-
-.gemma-home-header-wrapper .md-search__icon {
-  color: var(--gemma-blue-light) !important;
-}
-
-.gemma-home-header-wrapper .md-search__output {
-  background-color: var(--gemma-ink) !important;
-}
-
-.gemma-home-header-wrapper .md-search-result__title,
-.gemma-home-header-wrapper .md-search-result__article,
-.gemma-home-header-wrapper .md-search-result__link {
-  color: var(--gemma-white) !important;
-}
-
-.gemma-home-header-wrapper .md-search-result__link:hover,
-.gemma-home-header-wrapper .md-search-result__link--active {
-  background-color: rgba(49, 134, 255, 0.25) !important;
-}
-
-.gemma-home-header-wrapper .md-search-result__teaser {
-  color: rgba(255, 255, 255, 0.7) !important;
-}
-
-.gemma-home-header-wrapper mark {
-  background-color: rgba(79, 160, 255, 0.35) !important;
-  color: var(--gemma-blue-light) !important;
-}
diff --git a/docs/stylesheets/home-stage.css b/docs/stylesheets/home-stage.css
deleted file mode 100644
index c8ed26a..0000000
--- a/docs/stylesheets/home-stage.css
+++ /dev/null
@@ -1,81 +0,0 @@
-/* ===================================================================
-   Landing page — backdrop
-
-   Owns: the full-viewport gradient, ambient glow blobs and dot-matrix
-         overlay behind the hero.
-   Scoped to the landing page via `:has(.gemma-teaser)` so no other
-   page is affected. Colours come from gemma.css.
-   =================================================================== */
-
-/* 4. Homepage Main Container & Fixed Background Gradients covering entire viewport! */
-.gemma-home-container {
-  position: relative;
-  min-height: calc(100vh - 110px);
-  width: 100%;
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  padding: 1rem 2rem 0.5rem;
-  overflow: hidden;
-  box-sizing: border-box;
-  color: var(--gemma-white);
-}
-
-.gemma-home-bg {
-  position: fixed;
-  top: 0;
-  left: 0;
-  width: 100vw;
-  height: 100vh;
-  z-index: 0;
-  pointer-events: none;
-  background: 
-    radial-gradient(circle at 80% 25%, rgba(79, 160, 255, 0.6) 0%, transparent 55%),
-    radial-gradient(circle at 20% 75%, rgba(49, 134, 255, 0.65) 0%, transparent 60%),
-    radial-gradient(circle at 50% 50%, rgba(52, 107, 241, 0.7) 0%, transparent 70%),
-    linear-gradient(135deg, var(--gemma-navy-800) 0%, var(--gemma-navy-700) 45%, var(--gemma-navy-900) 75%, var(--gemma-ink) 100%);
-}
-
-/* Ambient glow blobs in Gemma blues */
-.gemma-home-glow {
-  position: absolute;
-  border-radius: 50%;
-  filter: blur(80px);
-  opacity: 0.6;
-}
-.gemma-glow-1 {
-  width: 500px;
-  height: 500px;
-  background: var(--gemma-blue);
-  top: -100px;
-  left: 10%;
-}
-.gemma-glow-2 {
-  width: 600px;
-  height: 600px;
-  background: var(--gemma-blue-deep);
-  top: 20%;
-  right: -100px;
-}
-.gemma-glow-3 {
-  width: 450px;
-  height: 450px;
-  background: var(--gemma-blue-light);
-  bottom: -50px;
-  left: 35%;
-}
-
-/* Subtle dot matrix pattern overlay */
-.gemma-home-grid {
-  position: absolute;
-  top: 0;
-  left: 0;
-  width: 100%;
-  height: 100%;
-  background-image: 
-    radial-gradient(rgba(255, 255, 255, 0.1) 1px, transparent 1px),
-    radial-gradient(rgba(79, 160, 255, 0.12) 1px, transparent 1px);
-  background-size: 40px 40px;
-  background-position: 0 0, 20px 20px;
-  opacity: 0.7;
-}
diff --git a/docs/stylesheets/home.css b/docs/stylesheets/home.css
index 279ec21..1038683 100644
--- a/docs/stylesheets/home.css
+++ b/docs/stylesheets/home.css
@@ -1,185 +1,250 @@
-/* ===================================================================
-   Landing page — hero and feature grid
-
-   Owns: the two-column hero layout, call-to-action, feature cards and
-         the landing-page footer.
-   Scoped to the landing page via `:has(.gemma-teaser)` so no other
-   page is affected. Colours come from gemma.css.
-   =================================================================== */
-
-/* 5. Homepage Content Layout (2-Column on Desktop for No-Scroll Viewport Fit) */
-.gemma-home-content {
-  position: relative;
-  z-index: 1;
-  max-width: 1280px;
-  width: 100%;
-  margin: auto 0;
-  display: flex;
-  flex-direction: column;
-  gap: 2.5rem;
+/* Landing page chrome, backdrop, and hero. The :has() scope keeps these
+   deliberate dark-theme overrides off ordinary documentation pages. */
+
+html:has(.gemma-teaser),
+body:has(.gemma-teaser),
+body:has(.gemma-teaser) .md-container,
+body:has(.gemma-teaser) .md-main {
+  height: 100%;
+  max-height: 100vh;
+  margin: 0;
+  overflow: hidden;
+  background-color: var(--gemma-home-bg);
+  scrollbar-width: none;
 }
 
-@media (min-width: 1024px) {
-  .gemma-home-content {
-    display: grid;
-    grid-template-columns: 1.1fr 0.9fr;
-    gap: 2rem;
-    align-items: center;
-  }
+body:has(.gemma-teaser) * {
+  scrollbar-width: none;
 }
 
-/* Left Pane: Hero Text & CTA */
-.gemma-hero-pane {
-  display: flex;
-  flex-direction: column;
-  align-items: flex-start;
-  text-align: left;
-  gap: 1.25rem;
+body:has(.gemma-teaser) *::-webkit-scrollbar,
+html:has(.gemma-teaser)::-webkit-scrollbar {
+  display: none;
 }
 
-@media (max-width: 1023px) {
-  .gemma-hero-pane {
-    align-items: center;
-    text-align: center;
-  }
+body:has(.gemma-teaser) .md-header,
+body:has(.gemma-teaser) .md-tabs {
+  background-color: transparent !important;
+  box-shadow: none !important;
+  color: var(--gemma-white) !important;
+  --md-default-fg-color: color-mix(in srgb, var(--gemma-white) 82%, transparent);
+  --md-default-fg-color--light: color-mix(in srgb, var(--gemma-white) 56%, transparent);
+  --md-default-fg-color--lighter: color-mix(in srgb, var(--gemma-white) 32%, transparent);
+  --md-default-fg-color--lightest: color-mix(in srgb, var(--gemma-white) 12%, transparent);
+  --md-default-bg-color: var(--gemma-ink);
+  --md-typeset-color: var(--gemma-white);
+  --md-typeset-a-color: var(--gemma-blue-light);
+  --md-primary-fg-color: var(--gemma-blue-deep);
+  --md-primary-bg-color: var(--gemma-white);
+  --md-accent-fg-color: var(--gemma-blue-light);
 }
 
-.gemma-badge {
-  display: inline-flex;
-  align-items: center;
-  gap: 0.5rem;
-  padding: 0.35rem 1.1rem;
-  border-radius: 9999px;
-  background: rgba(49, 134, 255, 0.2);
-  border: 1px solid rgba(79, 160, 255, 0.4);
-  color: var(--gemma-white);
-  font-size: 0.85rem;
-  font-weight: 600;
-  letter-spacing: 0.02em;
-  backdrop-filter: blur(8px);
+body:has(.gemma-teaser) .md-header {
+  border-bottom: 0 !important;
 }
 
-.gemma-badge-dot {
-  width: 8px;
-  height: 8px;
-  border-radius: 50%;
-  background-color: var(--gemma-blue-light);
-  box-shadow: 0 0 10px var(--gemma-blue-light);
+body:has(.gemma-teaser) .md-tabs {
+  border-bottom: 1px solid color-mix(in srgb, var(--gemma-white) 8%, transparent) !important;
 }
 
-.gemma-title {
-  font-size: clamp(2.25rem, 4.5vw, 3.5rem);
-  font-weight: 800;
-  line-height: 1.1;
-  letter-spacing: -0.03em;
-  color: var(--gemma-white);
-  margin: 0;
-  text-shadow: 0 0 30px rgba(0, 0, 0, 0.6);
+body:has(.gemma-teaser) [data-md-component="palette"],
+body:has(.gemma-teaser) .md-footer,
+body:has(.gemma-teaser) [data-md-component="footer"] {
+  display: none !important;
 }
 
-.gemma-title-gradient {
-  background: linear-gradient(135deg, var(--gemma-white) 0%, var(--gemma-blue-light) 45%, var(--gemma-blue) 100%);
-  -webkit-background-clip: text;
-  -webkit-text-fill-color: transparent;
-  background-clip: text;
+body:has(.gemma-teaser) :is(
+  .md-header__title,
+  .md-header__topic,
+  .md-header__button,
+  .md-header__source,
+  .md-tabs__link
+) {
+  color: color-mix(in srgb, var(--gemma-white) 85%, transparent) !important;
 }
 
-.gemma-subtitle {
-  font-size: clamp(0.95rem, 1.4vw, 1.15rem);
-  line-height: 1.5;
-  color: rgba(255, 255, 255, 0.9);
-  max-width: 580px;
-  margin: 0;
-  font-weight: 400;
-  text-shadow: 0 0 15px rgba(0, 0, 0, 0.6);
+body:has(.gemma-teaser) :is(
+  .md-header__title,
+  .md-tabs__link--active,
+  .md-tabs__link:hover,
+  .md-header__button:hover
+) {
+  color: var(--gemma-white) !important;
+  opacity: 1 !important;
 }
 
-/* Right Pane: 2x2 Compact Feature Grid (Guaranteed No-Scroll Fit on Desktop!) */
-.gemma-grid-pane {
-  width: 100%;
+body:has(.gemma-teaser) :is(.md-header__button, .md-tabs__link) :is(svg, .md-icon),
+body:has(.gemma-teaser) .md-search :is(svg, .md-icon, .md-search__icon, .md-search__options) {
+  color: var(--gemma-white) !important;
+  fill: none !important;
+  stroke: currentColor !important;
 }
 
-.gemma-compact-grid {
-  display: grid;
-  grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
-  gap: 1rem;
-  width: 100%;
+body:has(.gemma-teaser) .md-header .md-search :is(.md-search__button, .md-search__form) {
+  color: color-mix(in srgb, var(--gemma-white) 85%, transparent) !important;
+  background-color: color-mix(in srgb, var(--gemma-white) 15%, transparent) !important;
+  border: 1px solid color-mix(in srgb, var(--gemma-white) 20%, transparent) !important;
 }
 
-@media (min-width: 1024px) {
-  .gemma-compact-grid {
-    grid-template-columns: repeat(2, minmax(0, 1fr));
-  }
+body:has(.gemma-teaser) .md-header .md-search :is(.md-search__button, .md-search__form):hover {
+  color: var(--gemma-white) !important;
+  background-color: color-mix(in srgb, var(--gemma-white) 25%, transparent) !important;
+  border-color: color-mix(in srgb, var(--gemma-white) 35%, transparent) !important;
 }
 
-.gemma-card {
-  background: rgba(32, 33, 36, 0.65);
-  border: 1px solid rgba(79, 160, 255, 0.35);
-  border-radius: 0.85rem;
-  padding: 1.1rem;
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  text-decoration: none !important;
-  transition: all 0.25s cubic-bezier(0.4, 0, 0.2, 1);
-  backdrop-filter: blur(16px);
-  -webkit-backdrop-filter: blur(16px);
-  box-shadow: 0 10px 25px rgba(0, 0, 0, 0.35);
+body:has(.gemma-teaser) .md-header .md-search__input {
+  color: var(--gemma-white) !important;
+  background-color: transparent !important;
 }
 
-.gemma-card:hover,
-.gemma-card:focus {
-  background: rgba(49, 134, 255, 0.28);
-  border-color: var(--gemma-white);
-  box-shadow: 0 15px 35px rgba(49, 134, 255, 0.45);
-  transform: translateY(-3px);
+body:has(.gemma-teaser) .md-header .md-search__input::placeholder {
+  color: color-mix(in srgb, var(--gemma-white) 70%, transparent) !important;
+}
+
+.gemma-bg {
+  position: absolute;
+  inset: 0;
+  height: 100vh;
+  overflow: hidden;
+  pointer-events: none;
+  background: radial-gradient(
+    circle at 50% 35%,
+    var(--gemma-home-bg-raised) 0%,
+    var(--gemma-home-bg-mid) 55%,
+    var(--gemma-home-bg-deep) 100%
+  );
+}
+
+.gemma-wave-svg {
+  display: block;
+  width: 100%;
+  height: 100%;
+  object-fit: cover;
+}
+
+.gemma-teaser {
+  position: absolute;
+  z-index: 2;
+  top: 50%;
+  left: 50%;
+  width: min(90%, 60rem);
+  text-align: center;
+  transform: translate(-50%, -50%);
 }
 
-.gemma-card-header {
+.gemma-name {
+  display: grid;
+  grid-template-columns: 1fr auto 1fr;
+  align-items: baseline;
+  width: 100%;
+  margin: 0 auto -0.15em;
+  color: var(--gemma-white);
+  font-size: clamp(2.4rem, 6.8vw, 6.2rem);
+  font-weight: 800;
+  line-height: 1.15;
+  letter-spacing: -0.03em;
+  user-select: none;
+}
+
+.gemma-prefix-slot,
+.gemma-suffix-slot {
   display: flex;
-  justify-content: space-between;
   align-items: center;
-  margin-bottom: 0.4rem;
+  height: 1.15em;
+  overflow: hidden;
 }
 
-.gemma-card-icon {
-  font-size: 1.5rem;
+.gemma-prefix-slot {
+  justify-self: end;
+  justify-content: flex-end;
 }
 
-.gemma-card-arrow {
-  color: var(--gemma-blue-light);
-  font-size: 1.15rem;
-  font-weight: 700;
-  transition: transform 0.2s ease, color 0.2s ease;
+.gemma-suffix-slot {
+  justify-self: start;
+  justify-content: flex-start;
+  white-space: pre;
 }
 
-.gemma-card:hover .gemma-card-arrow {
-  transform: translate(3px, -3px);
+.gemma-cycling {
+  display: inline-block;
   color: var(--gemma-white);
+  will-change: transform, opacity;
 }
 
-.gemma-card-title {
-  color: var(--gemma-white);
-  font-size: 1.05rem;
+.gemma-static {
+  color: transparent;
+  background: linear-gradient(
+    135deg,
+    var(--gemma-blue-light),
+    var(--gemma-blue) 48%,
+    var(--gemma-blue-strong)
+  );
+  background-clip: text;
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  white-space: nowrap;
+}
+
+.gemma-tagline {
+  max-width: 44rem;
+  margin: 0 auto 2.8rem;
+  color: color-mix(in srgb, var(--gemma-white) 65%, transparent);
+  font-size: clamp(1.2rem, 2.2vw, 1.65rem);
+  font-weight: 400;
+  line-height: 1.5;
+  letter-spacing: 0.01em;
+}
+
+.gemma-actions {
+  display: flex;
+  justify-content: center;
+}
+
+.gemma-btn {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  padding: 0.95rem 2.8rem;
+  color: var(--gemma-white) !important;
+  background-color: var(--gemma-blue);
+  border: 1px solid color-mix(in srgb, var(--gemma-white) 15%, transparent);
+  border-radius: var(--gemma-radius-md);
+  box-shadow: 0 6px 24px color-mix(in srgb, var(--gemma-blue) 40%, transparent);
+  font-size: 1.22rem;
   font-weight: 700;
-  margin: 0 0 0.35rem 0;
-  letter-spacing: -0.01em;
+  text-decoration: none !important;
+  transition: background-color 250ms, border-color 250ms, box-shadow 250ms, transform 250ms;
 }
 
-.gemma-card-desc {
-  color: rgba(255, 255, 255, 0.75);
-  font-size: 0.82rem;
-  line-height: 1.4;
-  margin: 0;
+.gemma-btn:hover {
+  background-color: var(--gemma-blue-hover);
+  border-color: var(--gemma-white);
+  box-shadow: 0 8px 32px color-mix(in srgb, var(--gemma-blue) 60%, transparent);
+  transform: translateY(-2px);
 }
 
-/* 6. Homepage Footer */
-.gemma-footer {
-  position: relative;
-  z-index: 1;
-  text-align: center;
-  padding-top: 1rem;
-  color: rgba(255, 255, 255, 0.6);
-  font-size: 0.8rem;
-  width: 100%;
+@media (max-width: 48rem) {
+  .gemma-name { font-size: clamp(1.8rem, 6vw, 2.6rem); }
+  .gemma-tagline { margin-bottom: 2.2rem; font-size: 1.25rem; }
+  .gemma-btn { padding: 0.85rem 2.4rem; font-size: 1.12rem; }
+}
+
+@media (max-width: 31.25rem) {
+  .gemma-name { font-size: clamp(1.35rem, 6.2vw, 1.75rem); }
+  .gemma-tagline { font-size: 1.05rem; }
+}
+
+@media (max-height: 30rem) {
+  html:has(.gemma-teaser),
+  body:has(.gemma-teaser),
+  body:has(.gemma-teaser) .md-container,
+  body:has(.gemma-teaser) .md-main {
+    height: auto;
+    min-height: 100%;
+    max-height: none;
+    overflow: auto;
+  }
+
+  .gemma-bg { position: fixed; }
+  .gemma-teaser { position: relative; inset: auto; margin: 4rem auto 2rem; transform: none; }
 }
diff --git a/docs/stylesheets/layout.css b/docs/stylesheets/layout.css
index 20c0773..7c263cb 100644
--- a/docs/stylesheets/layout.css
+++ b/docs/stylesheets/layout.css
@@ -100,19 +100,6 @@ html {
   font-size: 0.75rem;
 }
 
-/* Wide pages: benchmark tables, image-dense visual guides. Add
-   `wide: true` to the page's frontmatter or `.wide` to a container. */
-.md-content__inner:has(.wide),
-body[data-wide] .md-content__inner {
-  max-width: 72rem;
-}
-
-/* Individual blocks can break out of the measure without the whole
-   page doing so. */
-.md-typeset .wide {
-  max-width: none;
-}
-
 /* -------------------------------------------------------------------
    Typography
    14px base, tighter leading than the previous 1.7. The old setting
diff --git a/docs/stylesheets/motion.css b/docs/stylesheets/motion.css
index 9b4d09e..d3d5499 100644
--- a/docs/stylesheets/motion.css
+++ b/docs/stylesheets/motion.css
@@ -1,10 +1,9 @@
 /* ===================================================================
    MOTION
 
-   Owns: transitions and animation, and the reduced-motion opt-out.
-   Kept in one file so "does this site animate?" has a single answer,
-   and so the `prefers-reduced-motion` escape hatch can't be forgotten
-   in a component sheet.
+   Owns: shared component transitions and the reduced-motion opt-out.
+   The landing page has one isolated text animation in its template;
+   it checks the same preference before starting.
 
    Docs are read, not played with: motion here is feedback (something
    is interactive, something changed), never decoration.
diff --git a/main.py b/main.py
deleted file mode 100644
index 90ee61d..0000000
--- a/main.py
+++ /dev/null
@@ -1,6 +0,0 @@
-def main():
-    print("Hello from docs!")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/overrides/home.html b/overrides/home.html
index a08b567..f2530c8 100644
--- a/overrides/home.html
+++ b/overrides/home.html
@@ -1,370 +1,24 @@
 {% extends "main.html" %}
 
-{% block styles %}
-{{ super() }}
-<style>
-  /* ===================================================================
-     VIEWPORT & SCROLL LOCK  (scoped via :has(.gemma-teaser))
-     =================================================================== */
-  html:has(.gemma-teaser),
-  body:has(.gemma-teaser),
-  html:has(.gemma-teaser) .md-container,
-  body:has(.gemma-teaser) .md-container,
-  html:has(.gemma-teaser) .md-main,
-  body:has(.gemma-teaser) .md-main {
-    height: 100% !important;
-    max-height: 100vh !important;
-    margin: 0 !important;
-    overflow: hidden !important;
-    background-color: #040d1a !important;
-    scrollbar-width: none !important;
-  }
-
-  html:has(.gemma-teaser) *,
-  body:has(.gemma-teaser) * {
-    scrollbar-width: none !important;
-  }
-
-  html:has(.gemma-teaser) *::-webkit-scrollbar,
-  body:has(.gemma-teaser) *::-webkit-scrollbar,
-  html:has(.gemma-teaser)::-webkit-scrollbar,
-  body:has(.gemma-teaser)::-webkit-scrollbar {
-    display: none !important;
-    width: 0 !important;
-    height: 0 !important;
-  }
-
-  /* ===================================================================
-     HEADER & TABS — transparent + white text on homepage
-     =================================================================== */
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-tabs,
-  body:has(.gemma-teaser) .md-header,
-  body:has(.gemma-teaser) .md-tabs {
-    background-color: transparent !important;
-    box-shadow: none !important;
-    color: #ffffff !important;
-    --md-default-fg-color: hsla(230, 15%, 90%, 0.82) !important;
-    --md-default-fg-color--light: hsla(230, 15%, 90%, 0.56) !important;
-    --md-default-fg-color--lighter: hsla(230, 15%, 90%, 0.32) !important;
-    --md-default-fg-color--lightest: hsla(230, 15%, 90%, 0.12) !important;
-    --md-default-bg-color: hsla(230, 15%, 14%, 1) !important;
-    --md-default-bg-color--light: hsla(230, 15%, 14%, 0.54) !important;
-    --md-default-bg-color--lighter: hsla(230, 15%, 14%, 0.26) !important;
-    --md-default-bg-color--lightest: hsla(230, 15%, 14%, 0.07) !important;
-    --md-typeset-color: #ffffff !important;
-    --md-typeset-a-color: #5488e8 !important;
-    --md-primary-fg-color: #4051b5 !important;
-    --md-primary-bg-color: #ffffff !important;
-    --md-accent-fg-color: #4287ff !important;
-    --md-search-bg-color: rgba(255, 255, 255, 0.15) !important;
-    --md-search-form-bg-color: rgba(255, 255, 255, 0.15) !important;
-    --md-search-form-icon-color: rgba(255, 255, 255, 0.85) !important;
-    --md-search-form-input-color: #ffffff !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header,
-  body:has(.gemma-teaser) .md-header {
-    border-bottom: none !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-tabs,
-  body:has(.gemma-teaser) .md-tabs {
-    border-bottom: 1px solid rgba(255, 255, 255, 0.08) !important;
-  }
-
-  body:has(.gemma-teaser) .md-header--shadow {
-    box-shadow: none !important;
-  }
-
-  /* Hide palette toggle on homepage only (CSS hide, not DOM removal, so instant nav preserves it) */
-  html:has(.gemma-teaser) [data-md-component="palette"],
-  body:has(.gemma-teaser) [data-md-component="palette"] {
-    display: none !important;
-  }
-
-  /* Hide footer and bottom-right 'Next' button on homepage only */
-  html:has(.gemma-teaser) .md-footer,
-  body:has(.gemma-teaser) .md-footer,
-  html:has(.gemma-teaser) [data-md-component="footer"],
-  body:has(.gemma-teaser) [data-md-component="footer"] {
-    display: none !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__title,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__topic,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__button,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__source,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-tabs__link,
-  body:has(.gemma-teaser) .md-header__title,
-  body:has(.gemma-teaser) .md-header__topic,
-  body:has(.gemma-teaser) .md-header__button,
-  body:has(.gemma-teaser) .md-header__source,
-  body:has(.gemma-teaser) .md-tabs__link {
-    color: rgba(255, 255, 255, 0.85) !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__title,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-tabs__link--active,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-tabs__link:hover,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__button:hover,
-  body:has(.gemma-teaser) .md-header__title,
-  body:has(.gemma-teaser) .md-tabs__link--active,
-  body:has(.gemma-teaser) .md-tabs__link:hover,
-  body:has(.gemma-teaser) .md-header__button:hover {
-    color: #ffffff !important;
-    opacity: 1 !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__button svg,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header__button .md-icon,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-tabs__link svg,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-tabs__link .md-icon,
-  body:has(.gemma-teaser) .md-header__button svg,
-  body:has(.gemma-teaser) .md-header__button .md-icon,
-  body:has(.gemma-teaser) .md-tabs__link svg,
-  body:has(.gemma-teaser) .md-tabs__link .md-icon {
-    color: #ffffff !important;
-    fill: none !important;
-    stroke: currentColor !important;
-  }
-
-  /* ===================================================================
-     SEARCH BAR — styled for dark homepage
-     =================================================================== */
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__button,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__form,
-  body:has(.gemma-teaser) .md-header .md-search__button,
-  body:has(.gemma-teaser) .md-header .md-search__form {
-    background-color: rgba(255, 255, 255, 0.15) !important;
-    color: rgba(255, 255, 255, 0.85) !important;
-    border: 1px solid rgba(255, 255, 255, 0.2) !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__button:hover,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__form:hover,
-  body:has(.gemma-teaser) .md-header .md-search__button:hover,
-  body:has(.gemma-teaser) .md-header .md-search__form:hover {
-    background-color: rgba(255, 255, 255, 0.25) !important;
-    color: #ffffff !important;
-    border-color: rgba(255, 255, 255, 0.35) !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__input,
-  body:has(.gemma-teaser) .md-header .md-search__input {
-    background-color: transparent !important;
-    color: #ffffff !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__input::placeholder,
-  body:has(.gemma-teaser) .md-header .md-search__input::placeholder {
-    color: rgba(255, 255, 255, 0.7) !important;
-  }
-
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__button svg,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__button .md-icon,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__icon,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__icon svg,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__options,
-  html:has(.gemma-teaser) [data-md-color-scheme] .md-header .md-search__options svg,
-  body:has(.gemma-teaser) .md-header .md-search__button svg,
-  body:has(.gemma-teaser) .md-header .md-search__button .md-icon,
-  body:has(.gemma-teaser) .md-header .md-search__icon,
-  body:has(.gemma-teaser) .md-header .md-search__icon svg,
-  body:has(.gemma-teaser) .md-header .md-search__options,
-  body:has(.gemma-teaser) .md-header .md-search__options svg {
-    color: #ffffff !important;
-    fill: none !important;
-    stroke: currentColor !important;
-  }
-
-  /* ===================================================================
-     BACKGROUND — subtle centered radial gradient
-     =================================================================== */
-  .gemma-bg {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100vh;
-    background: radial-gradient(circle at 50% 35%, #07152f 0%, #040c1e 55%, #020712 100%);
-    z-index: 0;
-    overflow: hidden;
-    pointer-events: none;
-  }
-
-  .gemma-wave-svg {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    object-fit: cover;
-    display: block;
-  }
-
-  /* ===================================================================
-     TEASER — centered content container
-     =================================================================== */
-  .gemma-teaser {
-    position: absolute;
-    top: 50%;
-    left: 50%;
-    transform: translate(-50%, -50%);
-    width: 90%;
-    max-width: 960px;
-    z-index: 2;
-    text-align: center;
-  }
-
-  /* ===================================================================
-     ANIMATED NAME — 3-column grid keeps "Gemma" pinned center
-     =================================================================== */
-  .gemma-name {
-    display: grid;
-    grid-template-columns: 1fr auto 1fr;
-    align-items: baseline;
-    width: 100%;
-    margin: 0 auto -0.15em;
-    font-size: clamp(2.4rem, 6.8vw, 6.2rem);
-    font-weight: 800;
-    letter-spacing: -0.03em;
-    line-height: 1.15;
-    color: #fff;
-    user-select: none;
-  }
-
-  /* Prefix slot — right-aligned so it butts up against "Gemma" */
-  .gemma-prefix-slot {
-    justify-self: end;
-    overflow: hidden;
-    height: 1.15em;
-    display: flex;
-    align-items: center;
-    justify-content: flex-end;
-  }
-
-  /* Suffix slot — left-aligned so it extends away from "Gemma" */
-  .gemma-suffix-slot {
-    justify-self: start;
-    overflow: hidden;
-    height: 1.15em;
-    display: flex;
-    align-items: center;
-    justify-content: flex-start;
-    white-space: pre;
-  }
-
-  /* The cycling text spans inside prefix/suffix slots — pure white */
-  .gemma-cycling {
-    display: inline-block;
-    will-change: transform, opacity;
-    color: #ffffff;
-  }
-
-  /* Static "Gemma" — always visible, rich slightly darker Gemma blue gradient */
-  .gemma-static {
-    background: linear-gradient(135deg, #4FA0FF 0%, #3186FF 48%, #1A4BD6 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    background-clip: text;
-    white-space: nowrap;
-  }
-
-  /* ===================================================================
-     TAGLINE
-     =================================================================== */
-  .gemma-tagline {
-    color: rgba(255, 255, 255, 0.65);
-    font-size: clamp(1.2rem, 2.2vw, 1.65rem);
-    line-height: 1.5;
-    max-width: 700px;
-    margin: 0 auto 2.8rem;
-    font-weight: 400;
-    letter-spacing: 0.01em;
-  }
-
-  /* ===================================================================
-     CTA BUTTON
-     =================================================================== */
-  .gemma-actions {
-    display: flex;
-    justify-content: center;
-  }
-
-  .gemma-btn {
-    display: inline-flex;
-    align-items: center;
-    justify-content: center;
-    padding: 0.95rem 2.8rem;
-    font-size: 1.22rem;
-    font-weight: 700;
-    border-radius: 8px;
-    text-decoration: none !important;
-    transition: all 0.25s cubic-bezier(0.4, 0, 0.2, 1);
-    background-color: #3186FF;
-    color: #ffffff !important;
-    border: 1px solid rgba(255, 255, 255, 0.15);
-    box-shadow: 0 6px 24px rgba(49, 134, 255, 0.4);
-  }
-
-  .gemma-btn:hover {
-    background-color: #2563eb;
-    border-color: #ffffff;
-    transform: translateY(-2px);
-    box-shadow: 0 8px 32px rgba(49, 134, 255, 0.6);
-  }
-
-  /* ===================================================================
-     RESPONSIVE (Prevents mobile cutoff for long names like DiffusionGemma)
-     =================================================================== */
-  @media (max-width: 768px) {
-    .gemma-name {
-      font-size: clamp(1.8rem, 6vw, 2.6rem);
-    }
-
-    .gemma-tagline {
-      font-size: 1.25rem;
-      margin-bottom: 2.2rem;
-    }
-
-    .gemma-btn {
-      padding: 0.85rem 2.4rem;
-      font-size: 1.12rem;
-    }
-  }
-
-  @media (max-width: 500px) {
-    .gemma-name {
-      font-size: clamp(1.35rem, 6.2vw, 1.75rem);
-    }
-
-    .gemma-tagline {
-      font-size: 1.05rem;
-    }
-  }
-</style>
-{% endblock %}
-
 {% block container %}
 <div class="gemma-bg">
   <svg class="gemma-wave-svg" viewBox="0 0 1440 900" fill="none" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="xMidYMid slice">
     <defs>
       <linearGradient id="gemmaWaveGrad1" x1="0%" y1="0%" x2="100%" y2="50%">
-        <stop offset="0%" stop-color="#3186FF" stop-opacity="0.55" />
-        <stop offset="50%" stop-color="#4FA0FF" stop-opacity="0.75" />
-        <stop offset="100%" stop-color="#346BF1" stop-opacity="0.15" />
+        <stop offset="0%" stop-color="var(--gemma-blue)" stop-opacity="0.55" />
+        <stop offset="50%" stop-color="var(--gemma-blue-light)" stop-opacity="0.75" />
+        <stop offset="100%" stop-color="var(--gemma-blue-deep)" stop-opacity="0.15" />
       </linearGradient>
       <linearGradient id="gemmaWaveGrad2" x1="100%" y1="100%" x2="0%" y2="0%">
         <stop offset="0%" stop-color="#64B5F6" stop-opacity="0.5" />
-        <stop offset="50%" stop-color="#3186FF" stop-opacity="0.65" />
+        <stop offset="50%" stop-color="var(--gemma-blue)" stop-opacity="0.65" />
         <stop offset="100%" stop-color="#0e244b" stop-opacity="0.2" />
       </linearGradient>
       <linearGradient id="gemmaCoreGlow" x1="0%" y1="50%" x2="100%" y2="50%">
-        <stop offset="0%" stop-color="#3186FF" stop-opacity="0.0" />
-        <stop offset="45%" stop-color="#4FA0FF" stop-opacity="0.45" />
+        <stop offset="0%" stop-color="var(--gemma-blue)" stop-opacity="0" />
+        <stop offset="45%" stop-color="var(--gemma-blue-light)" stop-opacity="0.45" />
         <stop offset="55%" stop-color="#64B5F6" stop-opacity="0.6" />
-        <stop offset="100%" stop-color="#346BF1" stop-opacity="0.0" />
+        <stop offset="100%" stop-color="var(--gemma-blue-deep)" stop-opacity="0" />
       </linearGradient>
       <filter id="waveBlurLow" x="-20%" y="-20%" width="140%" height="140%">
         <feGaussianBlur stdDeviation="6" />
@@ -377,26 +31,16 @@
       </filter>
     </defs>
 
-    <!-- Deep luminous background bloom positioned lower beneath the text baseline -->
     <ellipse cx="720" cy="650" rx="660" ry="150" fill="url(#gemmaCoreGlow)" filter="url(#coreBloom)" opacity="0.75" />
     <ellipse cx="680" cy="670" rx="380" ry="85" fill="#1D53D4" filter="url(#coreBloom)" opacity="0.25" />
 
-    <!-- Layer 1: Wide diffuse underlying wave ribbon (High Blur, lowered Y coordinates) -->
     <path d="M-100 690 C 300 500, 520 790, 850 580 C 1120 420, 1350 650, 1580 550 L 1580 950 L -100 950 Z" fill="url(#gemmaWaveGrad1)" filter="url(#waveBlurHigh)" opacity="0.45" />
-
-    <!-- Layer 2: Mid-layer flowing wave (Low Blur for ethereal softness) -->
     <path d="M-100 660 C 250 460, 480 730, 780 570 C 1080 400, 1320 620, 1580 520 L 1580 950 L -100 950 Z" fill="url(#gemmaWaveGrad2)" filter="url(#waveBlurLow)" opacity="0.55" />
-
-    <!-- Layer 3: Sharp glowing crest arcs representing the flowing energy stream -->
     <path d="M-100 660 C 250 460, 480 730, 780 570 C 1080 400, 1320 620, 1580 520" stroke="#64B5F6" stroke-width="3" stroke-linecap="round" fill="none" opacity="0.75" filter="url(#waveBlurLow)" />
-    <path d="M-100 660 C 250 460, 480 730, 780 570 C 1080 400, 1320 620, 1580 520" stroke="#FFFFFF" stroke-width="1.2" stroke-linecap="round" fill="none" opacity="0.65" />
-    
-    <!-- Secondary intersecting glowing strand -->
-    <path d="M-100 720 C 220 640, 490 500, 760 600 C 1030 700, 1280 490, 1580 590" stroke="#4FA0FF" stroke-width="2.5" fill="none" opacity="0.6" filter="url(#waveBlurLow)" />
+    <path d="M-100 660 C 250 460, 480 730, 780 570 C 1080 400, 1320 620, 1580 520" stroke="var(--gemma-white)" stroke-width="1.2" stroke-linecap="round" fill="none" opacity="0.65" />
+    <path d="M-100 720 C 220 640, 490 500, 760 600 C 1030 700, 1280 490, 1580 590" stroke="var(--gemma-blue-light)" stroke-width="2.5" fill="none" opacity="0.6" filter="url(#waveBlurLow)" />
     <path d="M-100 720 C 220 640, 490 500, 760 600 C 1030 700, 1280 490, 1580 590" stroke="#90CAF9" stroke-width="1" fill="none" opacity="0.5" />
-
-    <!-- Third dynamic flowing arc -->
-    <path d="M-50 570 C 350 680, 600 470, 900 640 C 1180 800, 1380 560, 1600 660" stroke="#3186FF" stroke-width="2" fill="none" opacity="0.45" filter="url(#waveBlurLow)" />
+    <path d="M-50 570 C 350 680, 600 470, 900 640 C 1180 800, 1380 560, 1600 660" stroke="var(--gemma-blue)" stroke-width="2" fill="none" opacity="0.45" filter="url(#waveBlurLow)" />
   </svg>
 </div>
 
@@ -411,14 +55,10 @@
     </div>
   </div>
 
-  <p class="gemma-tagline">
-    Our most capable open models
-  </p>
+  <p class="gemma-tagline">Our most capable open models</p>
 
   <div class="gemma-actions">
-    <a class="gemma-btn" href="get-started/quickstart/">
-      Get Started
-    </a>
+    <a class="gemma-btn" href="get-started/quickstart/">Get Started</a>
   </div>
 </div>
 
@@ -445,7 +85,6 @@
 
     if (!prefixEl || !suffixEl) return;
 
-    // Show first variant immediately
     prefixEl.textContent = variants[0].prefix;
     suffixEl.textContent = variants[0].suffix;
     prefixEl.style.transform = 'translateY(0)';
@@ -453,35 +92,29 @@
     suffixEl.style.transform = 'translateY(0)';
     suffixEl.style.opacity = '1';
 
-    var DURATION = 3000;      // ms between cycles
-    var ANIM = 400;       // ms for slide transition
-    var EASE = 'cubic-bezier(0.4, 0, 0.2, 1)';
+    if (window.matchMedia('(prefers-reduced-motion: reduce)').matches) return;
+
+    var duration = 3000;
+    var animation = 400;
+    var easing = 'cubic-bezier(0.4, 0, 0.2, 1)';
 
     var intervalId = setInterval(function () {
-      // Bail out if we've navigated away (instant navigation)
       if (!document.body.contains(prefixEl)) {
         clearInterval(intervalId);
         return;
       }
 
-      // --- EXIT: slide current text up and fade out ---
-      prefixEl.style.transition = 'transform ' + ANIM + 'ms ' + EASE + ', opacity ' + ANIM + 'ms ease';
-      suffixEl.style.transition = 'transform ' + ANIM + 'ms ' + EASE + ', opacity ' + ANIM + 'ms ease';
+      prefixEl.style.transition = 'transform ' + animation + 'ms ' + easing + ', opacity ' + animation + 'ms ease';
+      suffixEl.style.transition = 'transform ' + animation + 'ms ' + easing + ', opacity ' + animation + 'ms ease';
       prefixEl.style.transform = 'translateY(-120%)';
       prefixEl.style.opacity = '0';
       suffixEl.style.transform = 'translateY(-120%)';
       suffixEl.style.opacity = '0';
 
       setTimeout(function () {
-        // Advance to next variant
         index = (index + 1) % variants.length;
-        var v = variants[index];
-
-        // Swap text content
-        prefixEl.textContent = v.prefix;
-        suffixEl.textContent = v.suffix;
-
-        // --- PRE-ENTER: position below, no transition ---
+        prefixEl.textContent = variants[index].prefix;
+        suffixEl.textContent = variants[index].suffix;
         prefixEl.style.transition = 'none';
         suffixEl.style.transition = 'none';
         prefixEl.style.transform = 'translateY(120%)';
@@ -489,20 +122,18 @@
         suffixEl.style.transform = 'translateY(120%)';
         suffixEl.style.opacity = '0';
 
-        // Force reflow so the browser registers the position reset
         void prefixEl.offsetHeight;
 
-        // --- ENTER: slide up into view ---
         requestAnimationFrame(function () {
-          prefixEl.style.transition = 'transform ' + ANIM + 'ms ' + EASE + ', opacity ' + ANIM + 'ms ease';
-          suffixEl.style.transition = 'transform ' + ANIM + 'ms ' + EASE + ', opacity ' + ANIM + 'ms ease';
+          prefixEl.style.transition = 'transform ' + animation + 'ms ' + easing + ', opacity ' + animation + 'ms ease';
+          suffixEl.style.transition = 'transform ' + animation + 'ms ' + easing + ', opacity ' + animation + 'ms ease';
           prefixEl.style.transform = 'translateY(0)';
           prefixEl.style.opacity = '1';
           suffixEl.style.transform = 'translateY(0)';
           suffixEl.style.opacity = '1';
         });
-      }, ANIM + 30);
-    }, DURATION);
+      }, animation + 30);
+    }, duration);
   })();
 </script>
-{% endblock %}
\ No newline at end of file
+{% endblock %}
diff --git a/pyproject.toml b/pyproject.toml
index bf318a8..5b6db03 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,14 +1,14 @@
 [project]
 name = "docs"
 version = "0.1.0"
-description = "Add your description here"
+description = "Google DeepMind Gemma documentation built with Zensical"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = []
 
 [dependency-groups]
 dev = [
-    "zensical>=0.0.51",
+    "zensical==0.0.51",
 ]
 
 [[tool.uv.index]]
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index d826e7c..212249e 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -262,6 +262,30 @@ def outside_code(page: Page) -> Iterator[tuple[int, str]]:
 MD_LINK = re.compile(r"!?\[[^\]]*\]\(([^)\s]+)(?:\s+\"[^\"]*\")?\)")
 MD_IMAGE = re.compile(r"!\[[^\]]*\]\(([^)\s]+)")
 HTML_SRC = re.compile(r"<img[^>]+src=[\"']([^\"']+)[\"']")
+HEADING = re.compile(r"^(#{1,6})\s+(.+?)\s*$")
+EXPLICIT_ID = re.compile(r"(?:\{[^}]*#([\w-]+)[^}]*\}|\bid=[\"']([^\"']+)[\"'])")
+
+
+def slugify_heading(heading: str) -> str:
+    """Mirror Python-Markdown's default TOC slug generation."""
+    value = re.sub(r"<[^>]+>", "", heading)
+    value = re.sub(r"\[([^\]]*)\]\([^)]*\)", r"\1", value)
+    value = re.sub(r"[*`_]", "", value)
+    value = re.sub(r"\{:?[^}]*\}", "", value)
+    value = re.sub(r"[^\w\s-]", "", value).strip().lower()
+    return re.sub(r"[-\s]+", "-", value)
+
+
+def anchors_for(page: Page) -> set[str]:
+    """Return heading slugs and explicit IDs available on a page."""
+    anchors: set[str] = set()
+    for _, line in outside_code(page):
+        heading = HEADING.match(line)
+        if heading:
+            anchors.add(slugify_heading(heading.group(2)))
+        for explicit in EXPLICIT_ID.finditer(line):
+            anchors.add(explicit.group(1) or explicit.group(2))
+    return anchors
 
 
 def check_links(ctx: Context) -> Iterable[Finding]:
@@ -315,21 +339,34 @@ def check_images(ctx: Context) -> Iterable[Finding]:
 
 
 def check_internal(ctx: Context) -> Iterable[Finding]:
-    """Relative links must resolve to a file that exists. Prose only -- see check_images."""
+    """Relative links must resolve to a real file and fragment."""
+    pages_by_path = {page.path.resolve(): page for page in ctx.pages}
+    anchors_by_path = {path: anchors_for(page) for path, page in pages_by_path.items()}
+
     for page in ctx.pages:
         for lineno, line in outside_code(page):
             for match in MD_LINK.finditer(line):
                 target = match.group(1)
-                if target.startswith(("http://", "https://", "#", "mailto:", "data:", "/")):
+                if target.startswith(("http://", "https://", "mailto:", "data:", "/")):
                     continue
                 if line[match.start()] == "!":  # images handled by check_images
                     continue
-                resolved = (page.path.parent / target.split("#")[0]).resolve()
+
+                path_part, separator, fragment = target.partition("#")
+                resolved = page.path.resolve() if not path_part else (page.path.parent / path_part).resolve()
                 if not resolved.exists():
                     yield Finding(
                         "internal", page.repo_rel, lineno,
                         f"broken internal link: {target}",
                     )
+                    continue
+
+                if separator and fragment and resolved in anchors_by_path:
+                    if fragment not in anchors_by_path[resolved]:
+                        yield Finding(
+                            "internal", page.repo_rel, lineno,
+                            f"broken internal anchor: {target}",
+                        )
 
 
 def check_stubs(ctx: Context) -> Iterable[Finding]:
@@ -553,6 +590,18 @@ def check_css(ctx: Context) -> Iterable[Finding]:
         if not (STYLESHEET_DIR / name).exists():
             yield Finding("css", "zensical.toml", 0, f"extra_css lists a missing file: {name}")
 
+    # Template style blocks bypass every rule above. Presentation belongs in registered sheets
+    # so its load order and token use remain testable.
+    overrides_dir = REPO_ROOT / "overrides"
+    if overrides_dir.exists():
+        for path in sorted(overrides_dir.rglob("*.html")):
+            for number, line in enumerate(path.read_text(encoding="utf-8").splitlines(), start=1):
+                if "<style" in line.lower():
+                    yield Finding(
+                        "css", path.relative_to(REPO_ROOT).as_posix(), number,
+                        "inline style block -- move styles into a registered stylesheet",
+                    )
+
 
 CHECKS: dict[str, Callable[[Context], Iterable[Finding]]] = {
     "links": check_links,
@@ -571,7 +620,7 @@ def check_css(ctx: Context) -> Iterable[Finding]:
 CHECK_HELP = {
     "links": "links back to ai.google.dev/gemma/docs",
     "images": "hotlinked, inlined, or missing images",
-    "internal": "relative links that don't resolve",
+    "internal": "relative links or fragments that don't resolve",
     "stubs": "placeholder pages and leftover example.com",
     "frontmatter": "missing or incomplete agent frontmatter",
     "orphans": "pages missing from nav, nav entries missing files",
diff --git a/uv.lock b/uv.lock
index ea40763..be7e4c5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -45,7 +45,7 @@ dev = [
 [package.metadata]
 
 [package.metadata.requires-dev]
-dev = [{ name = "zensical", specifier = ">=0.0.51" }]
+dev = [{ name = "zensical", specifier = "==0.0.51" }]
 
 [[package]]
 name = "jinja2"
diff --git a/zensical.toml b/zensical.toml
index 7ea46f3..22c23aa 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -17,13 +17,10 @@ extra_css = [
   "stylesheets/gemma.css",       # tokens: palette, spacing, radius, shadow
   "stylesheets/layout.css",      # page grid, sidebars, typography scale
   "stylesheets/nav.css",         # header, tabs, sidebar, table of contents
-  "stylesheets/content.css",     # tables, admonitions, inline code, tabs
+  "stylesheets/content.css",     # tables, admonitions, code, tabs
   "stylesheets/components.css",  # cards, badges, figures, steps, embeds
-  "stylesheets/code.css",        # code and output blocks
   "stylesheets/tokens.css",      # chat-token badges
-  "stylesheets/home-chrome.css", # landing page: header, tabs, search
-  "stylesheets/home-stage.css",  # landing page: backdrop gradients
-  "stylesheets/home.css",        # landing page: hero and feature grid
+  "stylesheets/home.css",        # landing page: chrome, backdrop, hero
   "stylesheets/motion.css",      # transitions + reduced-motion opt-out
 ]
 # `include_yaml` exposes data/models.yml as `models.*` in page templates -- see data/models.yml

From f6a1ce63c4cfcb8d0840e8e5dcbb13f3b59e19f8 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Wed, 5 Aug 2026 18:40:06 +0200
Subject: [PATCH 16/20] Create content header

---
 .github/workflows/lint.yml                    |    6 +-
 .lintbaseline.json                            |   80 +-
 PLAN-ARCHIVE.md                               |  954 ++++++++++++
 PLAN.md                                       | 1377 ++++-------------
 data/frameworks.yml                           |  114 ++
 data/models.yml                               |  112 ++
 docs/community/discord.md                     |    4 +
 docs/community/gemma-skills.md                |    4 +
 docs/community/gemmaverse.md                  |    4 +
 docs/contributing/components.md               |   68 +
 docs/cookbook.md                              |   15 +-
 docs/demos/concurrent.md                      |    4 +
 docs/deployment/google-cloud.md               |    7 +
 docs/deployment/huggingface.md                |    9 +-
 docs/deployment/index.md                      |    6 +
 docs/deployment/keras.md                      |    7 +
 docs/deployment/langchain.md                  |    7 +
 docs/deployment/litert.md                     |    7 +
 docs/deployment/llamacpp.md                   |   12 +-
 docs/deployment/lmstudio.md                   |    2 +
 docs/deployment/mlx.md                        |   27 +-
 docs/deployment/mobile.md                     |    7 +
 docs/deployment/ollama.md                     |   22 +-
 docs/deployment/qat.md                        |    4 +-
 docs/deployment/sglang.md                     |    6 +-
 docs/deployment/vllm.md                       |   12 +-
 docs/deployment/web.md                        |    7 +
 docs/get-started/capabilities/audio.md        |   11 +-
 .../capabilities/function-calling.md          |   10 +-
 docs/get-started/capabilities/image.md        |   10 +-
 docs/get-started/capabilities/mtp.md          |    6 +
 docs/get-started/capabilities/text.md         |   12 +-
 docs/get-started/capabilities/thinking.md     |   10 +-
 docs/get-started/capabilities/video.md        |   10 +-
 docs/get-started/capabilities/vision.md       |    6 +
 docs/get-started/prompt-formatting.md         |    3 +
 docs/get-started/prompt-structure.md          |    6 +
 docs/get-started/quickstart.md                |   16 +-
 docs/index.md                                 |    7 +-
 docs/javascripts/model-data.js                |  111 ++
 docs/javascripts/model-switcher.js            |  241 +++
 docs/learn/business-email-assistant.md        |    7 +
 docs/learn/personal-code-assistant.md         |    7 +
 docs/models/datagemma/index.md                |    6 +
 docs/models/diffusiongemma/explained.md       |    6 +
 docs/models/diffusiongemma/index.md           |    6 +
 docs/models/diffusiongemma/inference.md       |    7 +
 docs/models/diffusiongemma/model-card.md      |    6 +
 docs/models/embeddinggemma/fine-tuning.md     |    7 +
 docs/models/embeddinggemma/index.md           |    6 +
 docs/models/embeddinggemma/inference.md       |    7 +
 docs/models/embeddinggemma/model-card.md      |    6 +
 docs/models/functiongemma/fine-tuning.md      |    7 +
 docs/models/functiongemma/formatting.md       |    6 +
 docs/models/functiongemma/full-sequence.md    |    7 +
 docs/models/functiongemma/index.md            |    6 +
 docs/models/functiongemma/inference.md        |    7 +
 docs/models/functiongemma/model-card.md       |    6 +
 docs/models/gemma-1/index.md                  |    6 +
 docs/models/gemma-2/index.md                  |    6 +
 docs/models/gemma-3/index.md                  |    6 +
 docs/models/gemma-3n/index.md                 |    6 +
 docs/models/gemma-3n/model-card.md            |    6 +
 docs/models/gemma-4/index.md                  |    6 +
 docs/models/gemma-4/model-card.md             |    6 +
 docs/models/gemma-aps/index.md                |    6 +
 docs/models/gemma-scope/index.md              |    6 +
 docs/models/index.md                          |    6 +-
 docs/models/paligemma/fine-tuning.md          |    7 +
 docs/models/paligemma/index.md                |    6 +
 docs/models/paligemma/inference.md            |    7 +
 docs/models/paligemma/model-card-v1.md        |    6 +
 docs/models/paligemma/model-card-v2.md        |    6 +
 docs/models/paligemma/prompting.md            |    6 +
 docs/models/recurrentgemma/fine-tuning.md     |    7 +
 docs/models/recurrentgemma/index.md           |    6 +
 docs/models/recurrentgemma/inference.md       |    7 +
 docs/models/recurrentgemma/model-card.md      |    6 +
 docs/models/releases.md                       |    5 +
 docs/models/shieldgemma/index.md              |    6 +
 docs/models/shieldgemma/model-card-v1.md      |    6 +
 docs/models/shieldgemma/model-card-v2.md      |    6 +
 docs/stylesheets/gemma.css                    |    4 +
 docs/stylesheets/page-context.css             |  175 +++
 docs/tools/index.md                           |    4 +
 docs/tools/model-selector.md                  |    4 +
 docs/tools/vram-calculator.md                 |    4 +
 docs/tuning/distributed.md                    |    7 +
 docs/tuning/full-finetune.md                  |    7 +
 docs/tuning/keras-lora.md                     |    7 +
 docs/tuning/mobile-actions.md                 |    7 +
 docs/tuning/overview.md                       |    6 +
 docs/tuning/qlora.md                          |   13 +-
 docs/tuning/spoken-language.md                |    7 +
 docs/tuning/vision-qlora.md                   |   11 +-
 .../tutorials/visual-guides/diffusiongemma.md |    1 +
 docs/tutorials/visual-guides/gemma-4-12b.md   |    1 +
 docs/tutorials/visual-guides/gemma-4.md       |    1 +
 docs/tutorials/visual-guides/mtp.md           |    1 +
 overrides/partials/content.html               |   27 +
 overrides/partials/model-names.html           |   92 ++
 overrides/partials/page-context.html          |   43 +
 scripts/backfill_frontmatter.py               |  373 +++++
 scripts/check_fidelity.py                     |   14 +-
 scripts/frontmatter_schema.py                 |  249 +++
 scripts/generate_model_names.py               |  153 ++
 scripts/lint_docs.py                          |  176 ++-
 zensical.toml                                 |   12 +-
 108 files changed, 3769 insertions(+), 1230 deletions(-)
 create mode 100644 PLAN-ARCHIVE.md
 create mode 100644 data/frameworks.yml
 create mode 100644 docs/javascripts/model-data.js
 create mode 100644 docs/javascripts/model-switcher.js
 create mode 100644 docs/stylesheets/page-context.css
 create mode 100644 overrides/partials/content.html
 create mode 100644 overrides/partials/model-names.html
 create mode 100644 overrides/partials/page-context.html
 create mode 100644 scripts/backfill_frontmatter.py
 create mode 100644 scripts/frontmatter_schema.py
 create mode 100644 scripts/generate_model_names.py

diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 7eaa9fb..8bfce56 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -25,7 +25,11 @@ jobs:
         with:
           python-version: "3.12"
 
-      # Both scripts are stdlib-only (tomllib requires 3.11+), so there is nothing to install.
+      # The converter tests are stdlib-only (tomllib requires 3.11+). The linter needs PyYAML
+      # to validate frontmatter, which is list-valued and cannot be parsed by splitting lines.
+      - name: Install linter dependencies
+        run: pip install pyyaml==6.0.3
+
       - name: Converter golden tests
         run: python scripts/test_convert_notebook.py
 
diff --git a/.lintbaseline.json b/.lintbaseline.json
index 7682944..e9ede8f 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -13,86 +13,8 @@
     "modeldata",
     "css"
   ],
-  "count": 85,
+  "count": 7,
   "known": [
-    "frontmatter|docs/community/discord.md|no frontmatter block",
-    "frontmatter|docs/community/gemma-skills.md|no frontmatter block",
-    "frontmatter|docs/community/gemmaverse.md|no frontmatter block",
-    "frontmatter|docs/cookbook.md|missing required key: description",
-    "frontmatter|docs/demos/concurrent.md|no frontmatter block",
-    "frontmatter|docs/deployment/google-cloud.md|no frontmatter block",
-    "frontmatter|docs/deployment/huggingface.md|no frontmatter block",
-    "frontmatter|docs/deployment/index.md|no frontmatter block",
-    "frontmatter|docs/deployment/keras.md|no frontmatter block",
-    "frontmatter|docs/deployment/langchain.md|no frontmatter block",
-    "frontmatter|docs/deployment/litert.md|no frontmatter block",
-    "frontmatter|docs/deployment/mobile.md|no frontmatter block",
-    "frontmatter|docs/deployment/web.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/audio.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/function-calling.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/image.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/mtp.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/text.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/thinking.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/video.md|no frontmatter block",
-    "frontmatter|docs/get-started/capabilities/vision.md|no frontmatter block",
-    "frontmatter|docs/get-started/prompt-formatting.md|missing required key: description",
-    "frontmatter|docs/get-started/prompt-formatting.md|missing required key: title",
-    "frontmatter|docs/get-started/prompt-structure.md|no frontmatter block",
-    "frontmatter|docs/get-started/quickstart.md|missing required key: description",
-    "frontmatter|docs/get-started/quickstart.md|missing required key: title",
-    "frontmatter|docs/learn/business-email-assistant.md|no frontmatter block",
-    "frontmatter|docs/learn/personal-code-assistant.md|no frontmatter block",
-    "frontmatter|docs/models/datagemma/index.md|no frontmatter block",
-    "frontmatter|docs/models/diffusiongemma/explained.md|no frontmatter block",
-    "frontmatter|docs/models/diffusiongemma/index.md|no frontmatter block",
-    "frontmatter|docs/models/diffusiongemma/inference.md|no frontmatter block",
-    "frontmatter|docs/models/diffusiongemma/model-card.md|no frontmatter block",
-    "frontmatter|docs/models/embeddinggemma/fine-tuning.md|no frontmatter block",
-    "frontmatter|docs/models/embeddinggemma/index.md|no frontmatter block",
-    "frontmatter|docs/models/embeddinggemma/inference.md|no frontmatter block",
-    "frontmatter|docs/models/embeddinggemma/model-card.md|no frontmatter block",
-    "frontmatter|docs/models/functiongemma/fine-tuning.md|no frontmatter block",
-    "frontmatter|docs/models/functiongemma/formatting.md|no frontmatter block",
-    "frontmatter|docs/models/functiongemma/full-sequence.md|no frontmatter block",
-    "frontmatter|docs/models/functiongemma/index.md|no frontmatter block",
-    "frontmatter|docs/models/functiongemma/inference.md|no frontmatter block",
-    "frontmatter|docs/models/functiongemma/model-card.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-1/index.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-2/index.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-3/index.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-3n/index.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-3n/model-card.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-4/index.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-4/model-card.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-aps/index.md|no frontmatter block",
-    "frontmatter|docs/models/gemma-scope/index.md|no frontmatter block",
-    "frontmatter|docs/models/index.md|no frontmatter block",
-    "frontmatter|docs/models/paligemma/fine-tuning.md|no frontmatter block",
-    "frontmatter|docs/models/paligemma/index.md|no frontmatter block",
-    "frontmatter|docs/models/paligemma/inference.md|no frontmatter block",
-    "frontmatter|docs/models/paligemma/model-card-v1.md|no frontmatter block",
-    "frontmatter|docs/models/paligemma/model-card-v2.md|no frontmatter block",
-    "frontmatter|docs/models/paligemma/prompting.md|no frontmatter block",
-    "frontmatter|docs/models/recurrentgemma/fine-tuning.md|no frontmatter block",
-    "frontmatter|docs/models/recurrentgemma/index.md|no frontmatter block",
-    "frontmatter|docs/models/recurrentgemma/inference.md|no frontmatter block",
-    "frontmatter|docs/models/recurrentgemma/model-card.md|no frontmatter block",
-    "frontmatter|docs/models/releases.md|no frontmatter block",
-    "frontmatter|docs/models/shieldgemma/index.md|no frontmatter block",
-    "frontmatter|docs/models/shieldgemma/model-card-v1.md|no frontmatter block",
-    "frontmatter|docs/models/shieldgemma/model-card-v2.md|no frontmatter block",
-    "frontmatter|docs/tools/index.md|no frontmatter block",
-    "frontmatter|docs/tools/model-selector.md|no frontmatter block",
-    "frontmatter|docs/tools/vram-calculator.md|no frontmatter block",
-    "frontmatter|docs/tuning/distributed.md|no frontmatter block",
-    "frontmatter|docs/tuning/full-finetune.md|no frontmatter block",
-    "frontmatter|docs/tuning/keras-lora.md|no frontmatter block",
-    "frontmatter|docs/tuning/mobile-actions.md|no frontmatter block",
-    "frontmatter|docs/tuning/overview.md|no frontmatter block",
-    "frontmatter|docs/tuning/qlora.md|no frontmatter block",
-    "frontmatter|docs/tuning/spoken-language.md|no frontmatter block",
-    "frontmatter|docs/tuning/vision-qlora.md|no frontmatter block",
     "stubs|docs/community/discord.md|stub: 46 chars of content (minimum 400)",
     "stubs|docs/community/gemma-skills.md|stub: 62 chars of content (minimum 400)",
     "stubs|docs/community/gemmaverse.md|placeholder link: example.com",
diff --git a/PLAN-ARCHIVE.md b/PLAN-ARCHIVE.md
new file mode 100644
index 0000000..cb10933
--- /dev/null
+++ b/PLAN-ARCHIVE.md
@@ -0,0 +1,954 @@
+# Gemma Docs — Archive
+
+Completed work and the running log of lessons learned. Split out of `PLAN.md` on 2026-08-05,
+which had grown to 1,395 lines with five steps recorded twice — once as a plan and once as an
+outcome.
+
+**`PLAN.md` is the working document.** This file is the record: what was done, what it cost,
+and what surprised us. Nothing here is outstanding work.
+
+---
+
+## Starting point
+
+This table is the migration's starting baseline, retained for historical context. Current
+status is recorded in the completed phase sections and the maintenance baseline below.
+
+| Metric | Value |
+|---|---|
+| Markdown files | 80 |
+| Nav entries | 76 |
+| Real ported content (>1.5 KB) | 44 |
+| One-line stubs (<250 B) | 34 |
+| Pages with frontmatter | 9 / 80 |
+| Links still pointing at `ai.google.dev/gemma/docs` | 122 |
+| Images hotlinked to `ai.google.dev/static` | 18 (0 vendored) |
+| Orphan pages (exist, not in nav) | 4 |
+| Orphan stylesheet (exists, not in `extra_css`) | 1 (`custom.css`) |
+
+Known defects:
+
+- Malformed code fences from the notebook converter — e.g. `docs/models/gemma-4/index.md:117`
+  wraps prose in a ```` ```python ```` fence mid-sentence.
+- 7 prompt-template blocks tagged `python` that are plain text.
+- ~16 unconverted devsite `Note:` / `Key Point:` / `Caution:` callouts.
+- `docs/models/paligemma/fine-tuning.md` is 435 KB (base64 images inlined by the converter).
+- `docs/community/gemmaverse.md` still ships `example.com` placeholder cards.
+- Modality data disagrees across `quickstart.md`, `models/index.md:14`, and `models/gemma-4/index.md:45`.
+
+
+---
+
+## Phase 0 — Foundation
+
+*Nothing here is glamorous. Everything else compounds on top of it, and CI checks stop the
+debt from coming back.*
+
+### Step 0.1 — Repo hygiene and CI guardrails ✅
+- [x] `scripts/lint_docs.py` with 9 checks: `links`, `images`, `internal`, `stubs`,
+      `frontmatter`, `orphans`, `fences`, `admonitions`, `size`.
+- [x] Baseline mechanism (`.lintbaseline.json`) so CI fails only on *new* violations while the
+      backlog is burned down. **Shrinking the baseline to zero is the definition of done for Phase 0.**
+- [x] `.github/workflows/lint.yml` — runs on push, PR, and dispatch. Stdlib-only, no install step.
+- [x] Deleted `docs/stylesheets/custom.css` (dead: not in `extra_css`, fully superseded by
+      `layout.css`, and would have conflicted on `.md-grid` max-width and sidebar width).
+- **Verify:** `uv run python scripts/lint_docs.py --baseline` → exit 0.
+      `uv run python scripts/lint_docs.py` → full 300-finding backlog.
+- **Done when:** ✅ regression test confirmed — adding an upstream link exits 1, reverting exits 0.
+
+**Baseline at 2026-08-03 (the Phase 0 burn-down list):**
+
+| Check | Count | Notes |
+|---|---|---|
+| `frontmatter` | 81 | 71 pages with no block + missing keys elsewhere |
+| `links` | 122 | 5 of these are inside code blocks |
+| `images` | 52 | 18 hotlinked, 34 inline base64 (all in `paligemma/fine-tuning.md`) |
+| `stubs` | 33 | |
+| `fences` | 7 | prompt templates tagged `python` |
+| `orphans` | 4 | |
+| `size` | 1 | `paligemma/fine-tuning.md` at 435 KB |
+| `internal` | 0 | all relative links resolve |
+| `admonitions` | 0 | **converter already handles these correctly** — 36 converted |
+| **Total** | **300** | |
+
+Useful commands:
+
+```bash
+uv run python scripts/lint_docs.py --list
+```
+
+```bash
+uv run python scripts/lint_docs.py --check links,images
+```
+
+```bash
+uv run python scripts/lint_docs.py --write-baseline
+```
+
+### Step 0.2 — Fix the notebook converter ✅
+- [x] **Wrapped list continuations** no longer eaten as code. Devsite wraps long bullets with an
+      indented continuation line; the 4-space rule fenced it as `python`. The guard meant to
+      prevent this (`lines[i-1].strip().startswith("    ")`) could never fire — `.strip()`
+      removes the very indentation it tests. Continuations are now rejoined onto their bullet,
+      which restores the original sentence exactly (markdown renders `foo\n  bar` in a list as
+      `foo bar`, so nothing about the content changes).
+- [x] **Language detection** gained a `text` fallback: `bash` → `python` → `text`. Detection now
+      uses Python *syntax* signals, deliberately not bare English words like "for"/"with"/"from",
+      which is why prompt templates were being classed as code.
+- [x] **Indented bullets no longer split a block.** A bullet may not *start* a block (that would
+      eat indented lists), but once a block is underway an indented bullet is block content —
+      previously one ASR prompt became a fence plus orphaned bullets.
+- [x] **Base64 extraction** via `--assets-dir` / `--assets-url`. Anchored on `![...](...)` so a
+      data URI inside real code is left alone.
+- [x] Golden tests: `scripts/test_convert_notebook.py` + 4 fixtures in `scripts/fixtures/`,
+      each a minimised reproduction of a real defect. 11 tests, wired into CI.
+- **Verify:** `uv run python scripts/test_convert_notebook.py` → 11 passing.
+- **Done when:** ✅ re-converting `core.md.txt` produces the QAT bullets intact with **zero**
+      python fences; `audio.md.txt` yields 8 `python`, 2 `bash`, 2 `text` fences, both ASR
+      prompts correctly `text`.
+
+**Content-fidelity check** (word multiset, source vs. output):
+
+| Source | Dropped | Verdict |
+|---|---|---|
+| `core.md.txt` | `NOTE` ×1 | the `> [!NOTE]` → `!!! Note` conversion. Additions are cookbook-card markup. |
+| `audio.md.txt` | 111, all badge/metadata | `colab_logo`, `notebook-site-button.png`, `View`, `Run`, `<br>` — the intended stripping. |
+
+**Zero prose lost in either.** Regenerate goldens with `--update`, then review the diff —
+those files are the specification.
+
+```bash
+uv run python scripts/test_convert_notebook.py
+```
+
+### Step 0.3 — Re-port Models, targeted repair for Get Started
+
+Two different strategies, because the two sections have different histories:
+
+- **Models is a straight port.** No hand-authoring, so re-converting is safe and correct. ✅ Done.
+- **Get Started is hand-edited and reviewed.** `quickstart.md` has a hand-authored model table,
+  the `npx skills add` admonition, and `hide: toc` frontmatter — none of it in upstream's
+  `get_started.md.txt`. Re-converting would destroy that. Repair defects in place.
+
+#### 0.3a — Porting infrastructure ✅
+- [x] `scripts/sources.toml` — manifest mapping each ported page to its upstream source.
+      **A page absent from the manifest must never be re-converted.** Also the origin of the
+      `source_url` frontmatter in Step 3.1.
+- [x] `scripts/check_fidelity.py` — word-multiset comparison proving a conversion changed markup
+      and nothing else. Compares *source minus what the converter is designed to drop* vs. output,
+      reusing the converter's own `is_badge_line` rather than a parallel word blocklist, so the
+      two can't drift apart.
+- [x] `scripts/port_docs.py` — batch porter. **Fidelity-gated: a page that drops prose is never
+      written.** Caches sources under `.sources/` (gitignored).
+- [x] New converter fix: **consecutive blank lines no longer split a code block.** Notebook
+      exports routinely leave two blank lines mid-cell; the old lookahead checked only one line
+      ahead, so one function became two fences (found at `paligemma/inference.md`, `binary_mask`).
+- [x] New linter check: two same-language fences separated only by blank lines — the signature of
+      a split block. Found **9 across 7 files**, well beyond the one reported.
+
+#### 0.3b — Models re-ported ✅
+- [x] All **37** manifest pages re-ported. Fidelity: 37 faithful, 0 with dropped prose.
+      Re-running is idempotent (all "unchanged").
+- [x] `paligemma/fine-tuning.md`: **435 KB → 51 KB**, 34 images extracted to `docs/assets/paligemma/`.
+- [x] `models/releases.md`: 58-byte stub → full port (+4.2 KB).
+- **Verify:** `uv run python scripts/port_docs.py --filter models/ --dry-run` → 37 ok, 0 failed.
+
+#### 0.3c — Get Started targeted repair ✅
+
+Diffing each page against a fresh conversion first confirmed both carry real hand-edits, so
+repair-in-place was the correct call:
+
+| Page | Hand-edits preserved |
+|---|---|
+| `capabilities/image.md` | title recased, `## Summary` edit, `<!-- TODO: Port over -->` block |
+| `capabilities/audio.md` | hand-added `# Audio Understanding`, `## Summary` edit, TODO block |
+
+- [x] `capabilities/audio.md` — two ASR prompt templates `python` → `text`.
+- [x] `capabilities/audio.md:81` — unconverted `> > [!NOTE]` callout → `!!! Note`.
+- [x] `capabilities/image.md` — three split blocks rejoined. Diff is exactly 6 lines
+      (the fence pairs); no content touched.
+- [x] New linter coverage: GitHub-style `> [!NOTE]` callouts. The `admonitions` check only
+      knew about devsite `Note:` prefixes, so this defect was invisible.
+- **Verify:** `uv run python scripts/lint_docs.py --check fences,admonitions` → zero.
+- **Done when:** ✅ zero fence and admonition findings site-wide; remaining diff vs. a fresh
+      conversion is hand-edits only.
+
+**Technique worth reusing:** diffing a page against a fresh conversion cleanly separates
+*bugs* from *hand-edits*. Do this before touching any page whose history is unclear — it is the
+manual form of the `--divergence` check and it is what proved re-porting would have been wrong here.
+
+### Phase 0 progress: 300 → 285
+
+| Check | Start | Now | |
+|---|---|---|---|
+| `fences` | 7 | **0** | ✅ cleared |
+| `size` | 1 | **0** | ✅ cleared |
+| `admonitions` | 0 | **0** | ✅ 1 found and fixed |
+| `internal` | 0 | **0** | ✅ |
+| `images` | 52 | **18** | base64 done; 18 hotlinked remain → Step 0.4 |
+| `stubs` | 33 | **32** | → Steps 2.2–2.4 |
+| `orphans` | 4 | **4** | → Step 2.1 |
+| `frontmatter` | 81 | **81** | → Step 3.1 |
+| `links` | 122 | **150** | +28 from `releases.md` gaining content → Step 0.5 |
+
+**Backlog after the Models port — 300 → 290:**
+
+| Check | Was | Now | |
+|---|---|---|---|
+| `images` | 52 | **18** | 34 base64 extracted |
+| `size` | 1 | **0** | cleared |
+| `fences` | 7 | **5** | model-card fences fixed; 5 left, all in Get Started |
+| `stubs` | 33 | **32** | `releases.md` now real content |
+| `links` | 122 | **150** | +28 from `releases.md` gaining content — Step 0.5 rewrites them |
+| `frontmatter` / `orphans` / `internal` / `admonitions` | 81 / 4 / 0 / 0 | unchanged | later steps |
+
+```bash
+uv run python scripts/port_docs.py --filter models/ --dry-run
+```
+
+```bash
+uv run python scripts/check_fidelity.py --manifest --cache-dir .sources
+```
+
+### Step 0.4 — Vendor all images ✅
+- [x] `scripts/vendor_images.py` — downloads remote images into `docs/assets/<section>/` and
+      rewrites the references. Section = the page's parent directory, matching the layout
+      `port_docs.py` already uses. Original filenames are kept so any asset traces back to its
+      upstream URL. A failed download leaves the reference untouched (a broken local path is
+      worse than a working remote one) and the script is idempotent, so re-running retries.
+- [x] All **18** hotlinked images vendored, 18/18 succeeded. `docs/assets/` is now 8.2 MB.
+- [x] Fidelity checker taught to ignore image *targets* on both sides — only alt text is prose.
+      Without this, vendoring read as 6 pages losing prose and would have **blocked those pages
+      from ever being re-ported**, since `port_docs.py` is fidelity-gated.
+- [ ] ~~Dark-mode variants~~ — **deferred to Phase 1** with the rest of the design system.
+- **Verify:** ✅ zero `ai.google.dev/static` references in the built `site/`; all 214 local
+      `<img>` sources resolve on disk.
+- **Done when:** ✅ the site renders fully with no network access to Google.
+
+!!! warning "Ordering: port, then vendor"
+    Conversion restores the upstream image URLs, so **re-porting a page undoes its vendoring**
+    (confirmed: re-porting `models/gemma-4/model-card.md` would revert one image, +21 bytes).
+    Always run `vendor_images.py` after `port_docs.py`. `port_docs.py` prints this reminder, and
+    since the `images` check now sits at zero, a missed run fails CI.
+
+```bash
+uv run python scripts/vendor_images.py --dry-run
+```
+
+### Step 0.4b — CI hardening (zizmor / org policy) ✅
+
+The org runs [zizmor](https://docs.zizmor.sh/) on workflows and **fails on any Medium/High
+finding**, not just the mandatory-check list. Both workflows now pass clean.
+
+- [x] All 6 action references pinned to commit SHAs in `lint.yml` and `deploy.yml`
+      (`unpinned-uses`, High — this is what broke the build).
+- [x] `lint.yml` given an explicit `permissions: contents: read` block.
+- [x] `deploy.yml` permissions narrowed: `contents: read` at workflow level, with
+      `pages: write` / `id-token: write` scoped to the `deploy` job alone. At workflow level they
+      were also being handed to the `build` job, which only needs to read (`excessive-permissions`, High).
+- [x] `persist-credentials: false` on both checkouts (`artipacked`).
+- **Verify:** `uvx zizmor --persona=regular .github/workflows/` → "No findings to report."
+
+!!! note "Updating a pin"
+    Pins carry a `# v4` comment so the intended version stays readable. To move one:
+
+    ```bash
+    gh api repos/actions/checkout/git/ref/tags/v4 --jq '.object.sha'
+    ```
+
+    For annotated tags, dereference: `gh api repos/<repo>/git/tags/<sha> --jq '.object.sha'`.
+
+### Step 0.5 — Rewrite internal links ✅
+
+- [x] `scripts/link_map.toml` — reviewable data, not logic buried in a script. Three sections:
+      `[ported]` (target exists), `[planned]` (eventual path; tracked as known-pending),
+      `[delink]` (drop the link, keep the text).
+- [x] `scripts/rewrite_links.py` — applies the map, computing each relative path per page.
+      An unmapped target is **reported and left alone**; silently dropping a link is worse than
+      leaving one to fix.
+- [x] **144 prose links rewritten**: 122 ported, 20 planned, 2 delinked. Zero unmapped.
+- [x] **CodeGemma delinked** (decision: not being ported). Both references in `releases.md`
+      keep their text — "Release of CodeGemma v1.1." still reads correctly.
+- [x] `--fix-anchors` pass: 12 fragments repaired. devsite used `#model_data`, Zensical emits
+      `#model-data`; 8 were the mechanical underscore→hyphen case, 4 needed the explicit
+      `[anchors]` override table because the heading text itself changed.
+- **Verify:** `uv run python scripts/lint_docs.py --check links` → zero. Both passes idempotent.
+- **Done when:** ✅ zero outbound links to the site we're replacing.
+
+**Left external on purpose** — 5 URLs inside code blocks: four are sample data the reader
+fetches (`img_url = "https://.../cat.png"`) and one is a BibTeX citation. Rewriting those would
+break copy-paste and misattribute the citation. `check_links` now skips code blocks for this reason.
+
+**5 anchors remain unresolved**, none of them actionable now:
+
+| Anchor | Why |
+|---|---|
+| `huggingface.md#audio`, `#vision` | target is a stub; resolves in Step 2.2 |
+| `quickstart.md#models-list` | section removed during hand-editing |
+| `paligemma/index.md#categories` | **broken upstream too** — no such section on ai.google.dev |
+| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
+
+The last two were dead links on the original site; the port reproduced them faithfully.
+Inventing a target would be an editorial guess, so they are left alone pending a content decision.
+
+```bash
+uv run python scripts/rewrite_links.py --report
+```
+
+### Phase 0 progress: 300 → 137
+
+| Check | Start | Now | |
+|---|---|---|---|
+| `links` | 122 | **0** | ✅ cleared |
+| `images` | 52 | **0** | ✅ cleared |
+| `fences` | 7 | **0** | ✅ cleared |
+| `size` | 1 | **0** | ✅ cleared |
+| `admonitions` | 0 | **0** | ✅ |
+| `internal` | 0 | 20 | known-pending: links to planned pages |
+| `stubs` | 33 | 32 | → Steps 2.2–2.4 |
+| `orphans` | 4 | 4 | → Step 2.1 |
+| `frontmatter` | 81 | 81 | → Step 3.1 |
+
+
+- [ ] Map all 122 `ai.google.dev/gemma/docs/*` links to local paths.
+- [ ] For pages not yet ported, point at the eventual local path and let the linter flag it as
+      a known-pending target (don't leave live external links).
+- **Verify:** `uv run python scripts/lint_docs.py --check links`
+- **Done when:** zero outbound links to the site we're replacing.
+
+### Step 0.5b — Port the pages Step 0.5 pointed at ✅
+
+Step 0.5 left 19 build warnings by design (links aimed at eventual paths). Every one of those
+targets turned out to exist upstream, so porting them was cheaper than leaving the warnings —
+and it advanced Step 2.4 at the same time.
+
+- [x] **10 pages ported**, all fidelity-clean:
+
+| Page | Source | Size |
+|---|---|---|
+| `get-started/setup.md` | `/setup` | 2.5 KB |
+| `get-started/prompt-structure.md` | `/core/prompt-structure` | 2.2 KB |
+| `get-started/capabilities/mtp.md` | `/mtp/overview` | 2.8 KB |
+| `deployment/index.md` | `/run` | 10.7 KB |
+| `deployment/huggingface.md` | `/core/huggingface_inference` | 11.9 KB (**replaced a stub**) |
+| `deployment/mobile-actions.md` | `/mobile-actions` | 4.2 KB |
+| `tuning/spoken-language.md` | `/spoken-language/task-specific-tuning` | 22.9 KB |
+| `learn/business-email-assistant.md` | `/business-email-assistant` | 20.5 KB |
+| `learn/personal-code-assistant.md` | `/personal-code-assistant` | 22.6 KB |
+| `models/gemma-aps/index.md` | `/gemma-aps` | 1.8 KB |
+
+- [x] **The MTP gap is closed.** It was the flagship Gemma 4 feature with zero documentation.
+- [x] Link map grew to 75 `[ported]` entries; 23 more anchor overrides applied.
+- [x] Fixed a mis-targeted mapping: `/core/prompt-system-instructions` (no such upstream page)
+      was sent to `paligemma/prompting.md`, but the referring context is core Gemma
+      `start_of_turn`/`end_of_turn` tags, so it now points at `get-started/prompt-structure.md`.
+- **Verify:** build warnings **19 → 5**; all four pipeline stages idempotent.
+
+**5 warnings remain, all deliberate:**
+
+| Warning | Status |
+|---|---|
+| `deployment/keras.md` ×2 | planned page — Step 2.1 decides whether "Run > Local" gains Keras |
+| `quickstart.md#models-list` | section removed during your hand-editing |
+| `paligemma/index.md#categories` | **broken upstream too** |
+| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
+
+The last three are left alone on purpose: picking a target is an editorial decision, and the
+dangling anchor is a useful signal that a section is genuinely missing. **Needs a content call.**
+
+!!! warning "The 9 new pages are not in the nav"
+    `orphans` rose 4 → 13. That is expected — Step 2.1 places them. Do not "fix" it by
+    deleting pages.
+
+### Step 0.6 — Model capability matrix as single source of truth ✅
+
+- [x] `data/models.yml` — every Gemma 4 size with parameters, architecture, modalities in/out,
+      context window, memory at each quantization, and target platform.
+- [x] Wired into `zensical.toml` via the macros plugin's `include_yaml`, exposed as `models.*`.
+- [x] `quickstart.md` and `models/index.md` tables **generated** from it.
+- [x] New `modeldata` lint check guarding the one table that can't be generated (below).
+- **Verify:** ✅ changed `context: 128K` → `TESTVALUE`, rebuilt, both tables updated; reverted,
+      both reverted. Drift check verified by injecting a wrong figure — caught it.
+
+**The modality contradiction is resolved.** Confirmed by Maarten (2026-08-03):
+
+| Model | Input | Note |
+|---|---|---|
+| E2B, E4B, 12B | Text, Image, Audio, Video | full multimodal |
+| 26B A4B, 31B | Text, Image, Video | **no audio**; video is processed as individual frames |
+
+This matches `models/gemma-4/index.md:45` ("Audio... featured natively on the E2B, E4B and 12B
+models"). The stale source was the `quickstart.md` table, which omitted video entirely and is
+now generated.
+
+!!! warning "One table is deliberately *not* generated"
+    `models/gemma-4/index.md` is a straight port (`scripts/sources.toml`), so a template there
+    would be wiped by the next `port_docs.py` run. Its memory figures were copied into
+    `data/models.yml` instead, and the new **`modeldata`** check compares the two — so if
+    upstream revises a number, it surfaces as a finding rather than a silent contradiction.
+
+```bash
+uv run python scripts/lint_docs.py --check modeldata
+```
+
+---
+
+
+### Step 0.7 — Rendering fixes from review
+
+All fixed **in the converter**, not per-page, so re-porting preserves them.
+
+- [x] **Devsite card grids → plain bullets.** Upstream's own `.md.txt` export mangles its card
+      HTML into an empty bullet + `####` heading + paragraph. Now collapsed to
+      `- **Title** — description`. Affected shieldgemma, recurrentgemma, datagemma,
+      gemma-scope, paligemma, gemma-aps (14 cards).
+- [x] **Videos embedded, not linked.** A standalone `[Video](youtube…)` becomes a responsive
+      16:9 `youtube-nocookie` iframe (lazy-loaded). 8 pages.
+- [x] **Model-card header centred.** Upstream wraps the banner/links/license block in
+      `<p align="center">`, which the export drops. Restored as `.model-card-header`.
+- [x] **Code inside list items stays inside.** Fences were emitted at column 0, breaking out of
+      the list so everything after started a new one — `embeddinggemma/inference.md`'s RAG
+      section. Blocks are now dedented by their own indent and the fence indented to its item.
+- [x] **Nested lists tightened.** A blank line between a bullet and its own sub-list made
+      Markdown render it "loose", wrapping items in `<p>` inconsistently.
+- [x] **`> NOTE:` blockquotes → admonitions.** 4 in functiongemma pages. The `**Note:**` lines in
+      `capabilities/thinking.md` are inside model-output blocks and were correctly left verbatim.
+- [x] **Gemma 1/2/3 model cards reference themselves.** Upstream's "Model Page: Gemma → /core"
+      resolved to Gemma 4 here, so the Gemma 2 card advertised Gemma 4 as its model page.
+      New `[page_overrides]` in `link_map.toml` handles per-page mappings.
+- [x] `docs/stylesheets/components.css` added (video embeds, model-card header) — the first
+      piece of the Phase 1 modular stylesheet layout.
+
+**The fidelity gate caught these transformations and blocked 8 pages** until
+`check_fidelity.py` was taught that video URLs and empty `[](url)` duplicates are markup, not
+prose. Working as designed — a transformation must be declared before it can land.
+
+### Phase 0 outcome — 300 → 136
+
+| Check | Start | Now | |
+|---|---|---|---|
+| `links` | 122 | **0** | ✅ |
+| `images` | 52 | **0** | ✅ |
+| `fences` | 7 | **0** | ✅ |
+| `size` | 1 | **0** | ✅ |
+| `admonitions` | 0 | **0** | ✅ |
+| `modeldata` | — | **0** | ✅ new check |
+| `internal` | 0 | 2 | `deployment/keras.md` — Step 2.1 decision |
+| `stubs` | 33 | 31 | → Steps 2.2–2.4 |
+| `orphans` | 4 | 13 | → Step 2.1 (9 newly ported pages await nav) |
+| `frontmatter` | 81 | 90 | → Step 3.1 |
+
+**Everything remaining is scheduled work, not defects.** The three checks that still have
+findings are all waiting on Phase 2 (nav, content) or Phase 3 (frontmatter schema).
+
+---
+
+## Phase 1 — Design system
+
+### Step 1.1 — Token layer ✅
+
+- [x] `docs/stylesheets/gemma.css` — palette, spacing (1–7), radius (sm/md/lg/pill),
+      shadow (sm/md/lg), chat-token categories, all with dark-scheme variants.
+- [x] Material's `--md-*` primary/accent/link variables bound to Gemma tokens, so component
+      sheets never name a brand colour.
+- [x] `content.css`, `tokens.css`, and the landing-page stylesheet consume tokens.
+- [x] `cookbook.css` folded into `components.css` as a generalised card grid
+      (`.cookbook-*` kept as aliases — converter output and existing pages emit them).
+- [x] New **`css`** lint check: raw hex only in `gemma.css`, every sheet registered in
+      `extra_css`, none over 250 lines.
+- **Verify:** `uv run python scripts/lint_docs.py --check css` → zero.
+- **Done when:** ✅ zero hex outside `gemma.css`; every sheet under 250 lines.
+
+**Contrast drove a real decision.** Measured against the palette:
+
+| Pairing | Ratio | |
+|---|---|---|
+| `#3186FF` on white | 3.51:1 | **fails AA for body text** |
+| `#346BF1` on white | 4.65:1 | passes |
+| `#4FA0FF` on ink | 5.98:1 | passes |
+
+So `--gemma-blue` is a **fill** colour — buttons, bars, icons, step markers — and never body
+text. Text uses `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in
+dark. The header uses `blue-deep` for the same reason.
+
+**Stylesheet layout** (8 files, each under 250 lines):
+
+| File | Owns |
+|---|---|
+| `gemma.css` | Tokens. **Only file with raw hex.** |
+| `layout.css` | Page grid, sidebars, typography scale |
+| `nav.css` | Header, tabs, sidebars, table of contents |
+| `content.css` | Tables, admonitions, code, content tabs |
+| `components.css` | Cards, badges, figures, steps, embeds |
+| `tokens.css` | Chat-token badges |
+| `home.css` | Landing page: chrome, backdrop, hero |
+| `motion.css` | Transitions + reduced-motion opt-out |
+
+The homepage is deliberately one concern again: obsolete styles for an earlier DOM were removed,
+while the live template styles moved into the registered, linted `home.css`.
+
+### Step 1.2 — Component library ✅
+
+- [x] Card grid, model badges (`--accent`, `--muted`), framed figures with light/dark image
+      swap, numbered steps with a connecting rail, video embeds, model-card header.
+- [x] `docs/contributing/components.md` — every component with a **live example** plus the
+      markup to copy, the colour-token table with contrast guidance, and the stylesheet map.
+      Added to nav under *Contributing*.
+- **Verify:** the components page renders every component in both themes.
+
+`.badge--muted` exists for the Step 4.2 switcher: a capability the selected model lacks is
+struck through rather than hidden, so the reader learns the limitation instead of it silently
+vanishing.
+
+### Step 1.3 — Motion ✅
+
+- [x] `motion.css` owns shared transitions and the site-wide reduced-motion opt-out. The isolated
+      homepage text animation checks the same preference before starting.
+- [x] Blanket reduced-motion rule; anything added later inherits it.
+- [x] Focus rings never animate — keyboard users need them immediately.
+- **Verify:** enable *Reduce motion* in OS settings, reload, confirm hover effects are instant.
+
+Motion is feedback (something is interactive, something changed), never decoration.
+
+
+### Step 1.4 — Stripe-inspired refinement
+
+Measured against `docs.stripe.com` rather than eyeballed. What actually differed:
+
+| | Stripe | Was | Now |
+|---|---|---|---|
+| Content width | 800px | **1600px** | **880px** |
+| Body size | 14px | 15.5px | **14px** |
+| Line height | 1.43 | 1.7 | 1.65 |
+| Neutral ramp | 13 steps | none | **12 steps**, blue-tinted |
+| Brand use | accent only | **full header** | accent only |
+
+- [x] **Layout reshaped.** Sidebar sits flush left; the *content* is centred in the space that
+      remains, rather than stretching to fill it. That is the single biggest change — at 1600px
+      lines ran to ~180 characters.
+- [x] Pages that hide the table of contents can use the freed rail without changing the regular
+      article's left edge (the 7-column Gemma 4 table fits at the regular 880px measure).
+- [x] **Header is a near-white tint** (`--gemma-n50`), not a slab of brand blue. Blue appears
+      once: the active tab, as colour + a 2px rule.
+- [x] New `nav.css` (chrome) and `content.css` (tables, admonitions, collapsibles, inline code,
+      content tabs) — Stripe-inspired, tinted surfaces over heavy borders.
+
+**Four bugs found only because the result was measured in a browser:**
+
+1. **Body text was 18px, not 14px.** Material ships `html { font-size: 125% }`, so every `rem`
+   in the file was 25% larger than it read. Anchored to 100%.
+2. **The active-tab rule never matched.** Zensical marks the active tab on the `<li>`
+   (`md-tabs__item--active`), not the `<a>`.
+3. **Material's palette outranks a plain scheme selector.** It scopes at
+   `[data-md-color-scheme=X][data-md-color-primary=Y]` (0,2,0); ours was (0,1,0) and lost
+   silently regardless of load order. Dark-mode links and inline code stayed light.
+4. **Admonition tints outranked the base rule** at `.md-typeset .admonition.note` (0,3,1).
+
+**Layout shape** (verified at 1366px and 2000px):
+
+```
+sidebar 17rem │  ←auto→  │ content 56rem │ toc 17rem │  ←auto→
+   flush left            └── centred as a pair, TOC follows the text ──┘
+```
+
+The sidebar stays flush left at every width. Content and the table of contents are centred
+*as a pair* via auto margins on the pair's outer edges — capping and centring all three
+columns instead pushes the sidebar inward, which a docs rail should not do.
+
+**`hide: toc` pages get their own rule.** Material still renders the sidebar element, just
+`hidden`, so the pair's `margin-right: auto` lands on a zero-width box and every pixel of
+slack piles up on the left, shunting the column right. Those pages centre on their own and
+widen to 73rem — hiding the table of contents is usually a request for a wider page.
+
+Padding lives on `.md-content`, not `.md-content__inner`: the breadcrumb is a **sibling** of
+`__inner`, so padding there left the two misaligned by 77px. Breadcrumb, `h1` and body text
+now share one left edge.
+
+**Contrast verified in the live page, both schemes — everything passes AA:**
+
+| | Light | Dark |
+|---|---|---|
+| Body text | 21.0 | 15.4 |
+| Links | 4.65 | 7.27 |
+| Active tab | 4.92 | — |
+| Table header / muted | 6.38 | 8.39 |
+
+Two tokens exist purely to hold that line: `--gemma-text-muted` uses `n600` (n500 measured
+4.46 on the tint), and `--gemma-accent-on-tint` (`#2F63E6`) carries small brand-coloured text
+on tinted surfaces, where plain `blue-deep` measures 4.41.
+
+
+---
+
+## Phase 2 — Navigation & content
+
+### Step 2.1 — Restructure nav ✅
+
+- [x] Nav rewritten to the 8-tab target: **Home · Get started · Models · Run · Fine-tune ·
+      Learn · Demos · Ecosystem**.
+- [x] **All 13 orphans resolved.** `orphans` 13 → **0**.
+- [x] Cookbook folded into *Learn*; the old Tools tab folded into *Demos* as
+      "Interactive tools"; `contributing/components.md` placed under *Ecosystem → Contribute*.
+- **Verify:** `uv run python scripts/lint_docs.py --check orphans` → zero.
+- **Done when:** ✅ zero orphans, zero broken nav entries, 8 top-level tabs confirmed in the
+      rendered header.
+
+How each orphan was resolved:
+
+| Orphan | Resolution |
+|---|---|
+| `get-started/index.md` | **Deleted** — a strict subset of `quickstart.md` with zero inbound links |
+| `capabilities/vision.md` | **Ported** from upstream (was a stub); now the parent of Image/Video |
+| `deployment/keras.md` | **Ported** — clears the last 2 `internal` findings and fills *Run → Local* |
+| `models/index.md`, `tools/index.md` | placed as section landing pages |
+| the 9 pages from Step 0.5b | placed in Run / Fine-tune / Learn / Models |
+
+**Directory question settled: `models/` stays flat.** Grouping is nav-only, exactly so a
+model's URL survives reclassification — when the next generation ships and Gemma 4 moves to
+*Previous generations*, no URL changes. The rationale is recorded in the nav file itself.
+
+**One more converter fix:** consecutive `{ .no-copy .output }` blocks separated only by blank
+lines are now merged. Upstream splits a single stream of terminal output across several fences
+(Keras `.summary()` prints a title then a table), which rendered as two separate result boxes.
+Only *output* blocks are merged — two adjacent code blocks may legitimately be separate cells.
+
+### Phase 2 progress: 136 → 120
+
+| Check | Now | |
+|---|---|---|
+| `links` `images` `internal` `fences` `admonitions` `size` `modeldata` `css` `orphans` | **0** | ✅ all clear |
+| `stubs` | 30 | → Steps 2.2–2.4 |
+| `frontmatter` | 90 | → Step 3.1 |
+
+**Only two checks still have findings**, and both are scheduled work. Build warnings: 5 → **3**.
+
+
+
+### Step 2.2 — Local engine pages (the differentiator)
+
+These are *written*, not ported — upstream's versions are three paragraphs each. One step per
+engine so they can be tested and handed over independently.
+
+**These are quick references, not manuals** (decided 2026-08-03). Users want working code
+fast, and a short page is a page that stays correct. Roughly 100 lines each.
+
+Template — in this order, nothing else:
+
+1. **Install** — the one command per platform
+2. **Run** — the one command that works
+3. **Model tags / repositories** — a table, every Gemma 4 size
+4. **Multimodal** — only where it differs from plain text, with a note when a size lacks a modality
+5. **Server** — the OpenAI-compatible snippet
+
+Deliberately **not** included: context-length deep dives, troubleshooting tables, importing
+your own fine-tunes, "next steps" link lists. Each is either engine documentation that we would
+have to keep in sync, or a link the sidebar already provides. A single `!!! tip` is allowed
+where a default will actively surprise someone (Ollama's 4K context, for instance).
+
+- [x] 2.2a Ollama ✅ *(written)*
+- [x] 2.2b llama.cpp ✅ *(written)*
+- [x] 2.2c LM Studio ✅ *(written)*
+- [x] 2.2d MLX ✅ *(written)*
+- [x] 2.2e Transformers ✅ *(ported — upstream `core/huggingface_inference` is already complete)*
+- [x] 2.2f vLLM ✅ *(written)*
+- [x] 2.2g SGLang ✅ *(written)*
+- [x] 2.2h LiteRT / mobile / WebGPU ✅ *(ported — upstream pages are already concise link hubs)*
+- [x] 2.2i Google Cloud ✅ *(ported)*
+- [x] Also filled: Keras, LangChain, Quantization & QAT. **The Run section has zero stubs.**
+
+**Nav labels are short, page titles are not** (2026-08-03). The sidebar shows "Hugging Face
+Transformers", "LangChain", "Keras"; the page H1s stay as ported ("Run Gemma with Hugging Face
+Transformers"). Labels live in `zensical.toml`, so no ported prose is touched.
+
+**`mobile-actions.md` moved `deployment/` → `tuning/`.** Its H1 is "Fine-tune FunctionGemma 270M
+for Mobile Actions" — a fine-tuning guide that was sitting under Run.
+
+
+- **Verify per page:** every command runs as written on a clean machine.
+
+**Sourcing rule (2026-08-03).** Every command, flag and model identifier on these pages is
+taken from a primary source — the engine's own repository docs, its published model registry,
+or the upstream Gemma docs. Nothing is written from memory. Where a claim can't be verified it
+is omitted rather than guessed.
+
+Verified sources used so far:
+
+| Page | Sources |
+|---|---|
+| Ollama | `ollama.com/library/gemma4/tags` (tags, sizes, context) · `ollama/ollama` `docs/api`, `docs/api/openai-compatibility.mdx`, `docs/capabilities/vision.mdx`, `docs/context-length.mdx` · upstream `/integrations/ollama` |
+| llama.cpp | `ggml-org/llama.cpp` `docs/install.md`, `docs/multimodal.md` · `huggingface.co/ggml-org` (5 GGUF repos) · `huggingface.co/google` (5 QAT GGUF repos) · upstream `/integrations/llamacpp` |
+
+**Throughput numbers are deliberately absent.** Measured tok/s can't be sourced from
+documentation, and inventing it would be worse than omitting it. The pages give the verifiable
+performance levers instead — GPU-offload checks, context defaults, quantization choice. Add real
+figures when someone can measure them.
+
+**Unresolved discrepancy — Ollama download sizes.** `ollama.com/library/gemma4/tags` states
+E2B 7.2 GB, E4B 9.6 GB, **12B 7.6 GB**, 26B 18 GB, 31B 20 GB. That makes E4B *larger* than 12B,
+which contradicts the validated Q4_0 column in `models/gemma-4/index.md` (E4B 4.5 GB,
+12B 6.7 GB — monotonic, as expected). Verified verbatim from the tags page twice.
+
+Most likely the default tag for each size uses a different quantization, but that is a guess and
+**no explanation appears on the page**. An earlier draft rationalised it as a Per-Layer
+Embeddings effect; that was wrong and was removed. If someone can confirm the per-tag quant
+levels, add them to the table — otherwise leave the figures unexplained.
+
+Two corrections to upstream found while writing:
+
+- Upstream's Ollama page lists "four sizes: E2B, E4B, 26B and 31B" and **omits 12B**, which the
+  Ollama library does publish.
+- **Ollama exposes Gemma 4 as text + image only** — no audio — while llama.cpp supports image,
+  audio *and* video. Neither page upstream mentions this, and it's the kind of thing that costs
+  someone an afternoon.
+
+
+- **Done when:** the page is genuinely more useful than the upstream equivalent.
+
+### Step 2.3 — Fill remaining stubs (partly done)
+
+*The 7 stubs still open are tracked in `PLAN.md` under "Current status".*
+
+- [x] **Fine-tune section ported** — all six stubs filled from upstream: `tune`,
+      `huggingface_text_finetune_qlora`, `huggingface_text_full_finetune`,
+      `huggingface_vision_finetune_qlora`, `lora_tuning`, `distributed_tuning`.
+      These are validated tutorials, so porting beats authoring.
+
+**`slugify` bug fixed.** `rewrite_links.py` stripped parenthesised text, so
+"Google Kubernetes Engine (GKE)" produced `google-kubernetes-engine` while Python-Markdown
+emits `google-kubernetes-engine-gke`. Anchors computed from it silently never matched. It now
+mirrors the real algorithm: reduce markdown to visible text, then drop non-word characters —
+parentheses contribute their contents, they are not removed.
+
+
+
+### Step 2.4 — Port remaining upstream pages (partly done)
+
+- [x] ~~CodeGemma~~ — **not being ported** (decision: 2026-08-03). Existing references are
+      delinked, keeping the text. Do not add it back without a new decision.
+- [x] `run`, `core/keras_inference`, `core/huggingface_inference`, `core/lora_tuning`,
+      `core/prompt-structure`, `gemma-aps`, `spoken-language/task-specific-tuning`,
+      `conversions/hf-to-mediapipe-task`, `mobile-actions` — ported and frozen.
+- [x] ~~`setup`~~ — **deleted, not ported** (decision: 2026-08-04). The legacy setup page was
+      superseded by `get-started/quickstart.md`.
+- [x] Application tutorials → Learn: `business-email-assistant`, `personal-code-assistant`.
+
+*The 6 pages still awaiting a keep-or-drop decision are listed in `PLAN.md`.*
+
+---
+
+## Maintenance baseline (2026-08-04)
+
+- [x] Zensical is pinned exactly (`zensical==0.0.51`) and deployment runs with
+      `uv run --frozen`, so a package release cannot silently restyle production.
+- [x] Deployment gates the build on converter tests and baseline-aware docs lint.
+- [x] Stylesheets consolidated from 11 to 8: the active homepage CSS moved out of its template,
+      three obsolete homepage sheets were removed, and the one-rule `code.css` was folded into
+      `content.css`.
+- [x] CSS lint rejects template `<style>` blocks in addition to enforcing registration, token
+      use, and the 250-line limit.
+- [x] Internal-link lint validates `#fragments`, including same-page links. The three real broken
+      fragments from the August build were repaired. The larger missing-page list in the captured
+      output was transient incremental-build state; a clean build is the gate.
+- [x] Replaced the placeholder README and removed the unused `main.py` scaffold.
+- [x] Import, conversion, and image-vendoring scripts remain in place by decision. Demo/vendor
+      images are untouched until original PNGs replace them.
+
+
+---
+
+## Notes
+
+*Agents: record surprises, deviations, and anything that changes the plan here.*
+
+- Zensical is pinned exactly via `pyproject.toml` and `uv.lock` (`zensical==0.0.51`). Some Material features listed in
+  `zensical.toml` may not be implemented yet — verify before relying on them.
+
+**2026-08-03 (Step 0.1)**
+
+- ⚠️ **Two workflows both deploy to GitHub Pages on push to `main`** — `docs.yml` and
+  `deploy.yml`. They will race; `deploy.yml` has a `concurrency: pages` group, `docs.yml`
+  doesn't. They also install zensical differently (`docs.yml` → plain PyPI, `deploy.yml` → uv +
+  `pypi.zensical.org`). **Needs a decision from Maarten** — `deploy.yml` looks like the intended
+  one. Left both in place rather than deleting unilaterally.
+- **Correction to the original audit:** devsite callouts are *not* an issue. The converter
+  already converts them (36 admonitions present, 0 unconverted). The earlier "~16" figure came
+  from a combined grep pattern that also matched `.md.txt` and `colab.research` strings.
+  Step 0.2's admonition work is therefore already done — verify with a fixture rather than rewrite.
+- `check_images` and `check_internal` scan **outside code blocks only**. Markdown-like syntax
+  inside snippets is code, not a reference (e.g. an f-string `{render_inline(image, resize=(64,64))}`
+  in `paligemma/fine-tuning.md:515` was a false positive).
+- `check_links` scans **everywhere**, including code blocks — 5 upstream links live inside
+  snippets. Some may be intentional (e.g. downloading a sample asset); review case by case in Step 0.5.
+- `Finding.key()` deliberately excludes line numbers so edits above a known finding don't
+  resurrect it as "new".
+- The 34 base64 images are all in `paligemma/fine-tuning.md`. Fixing that one file clears the
+  `size` check and 34 of the 52 `images` findings at once — good first target for Step 0.3/0.4.
+
+**2026-08-03 (Step 0.2)**
+
+- ⚠️ **Pages have been hand-edited since their port.** There is currently no way to tell a
+  straight port from a hand-edited page automatically — hence `--divergence` in Step 0.3 and
+  `source_url` frontmatter in Step 3.1. Until one of those lands, **assume any page may contain
+  hand-work and never re-convert in bulk.**
+- **Get Started is reviewed and approved by Maarten.** Repair defects in place; do not re-convert.
+- Only 6 files have converter-class defects. The other 294 backlog findings are links, images,
+  frontmatter, and nav — all outside the converter's remit.
+- The converter's `text` fallback matters more than it looks: an early version of the Python
+  detector matched `\bfor\b` inside "for formatting the answer", which is exactly how prose got
+  classified as code. Keep the detector on *syntax* signals, never bare English words.
+
+**2026-08-03 (Step 0.3)**
+
+- **`scripts/sources.toml` is the authority on what may be re-converted.** Absent from the
+  manifest = hand-authored or hand-edited = never re-convert. Add a page to it only after
+  confirming it is a straight port.
+- Fidelity checking must compare against *source minus designed drops*, not raw source.
+  A naive comparison flags every Colab/Kaggle badge as lost prose — that produced 10 false
+  failures before `strip_designed_drops` reused the converter's own `is_badge_line`.
+- `check_fidelity.py` and `port_docs.py` **share `cache_name()`**. They must: keying the source
+  cache on the bare filename collided every `index.md` across all model directories onto one
+  entry, making the two tools disagree (0 failures vs 26).
+- `--dry-run` extracts images to a temp directory. Without that it wrote 34 files to
+  `docs/assets/` while claiming to change nothing.
+- Two blank lines mid-cell is common in notebook exports — worth checking for whenever a new
+  converter bug is suspected.
+
+**2026-08-03 (Step 0.4)**
+
+- **Conversion and vendoring fight each other.** Converting restores upstream image URLs;
+  vendoring replaces them with local paths. Always port first, then vendor. See *Pipeline order*.
+- Any check that gates writes (as `port_docs.py` does on fidelity) must be **precise about what
+  is allowed to change**, or it locks pages out of future edits. Vendoring tripped the fidelity
+  check on 6 pages until image targets were excluded from the word comparison.
+- `docs/assets/` is 8.2 MB, most of it `capabilities/` (7 notebook-output PNGs, 5.5 MB). If repo
+  size becomes a concern, those are the candidates for recompression — not the diagrams.
+- The site's `404.html` uses root-absolute `/assets/` paths. That is correct (a 404 can be served
+  from any depth) and will look like a broken link to any checker resolving paths relative to the
+  file. Don't "fix" it.
+- ⚠️ **Any new workflow must be zizmor-clean before it is pushed.** Pin every action to a SHA,
+  set an explicit `permissions:` block, and scope write permissions to the job that needs them.
+  Check locally with `uvx zizmor --persona=regular .github/workflows/` — it catches everything
+  CI does and takes seconds. The org fails on Medium/High, which is broader than the published
+  mandatory-check list.
+- **Why zizmor appeared only now, on this branch.** The scanner is "GitHub Actions Scan"
+  (`.github/workflows/github_actions_scan.yml`), injected org-wide by `google-gemma` — it is not
+  in this repo. It triggers on **`pull_request_target`**, and every commit before this branch went
+  *straight to main via push*, so it had never run. `foundation` is the repo's first pull request.
+  The pre-existing `docs.yml` and `deploy.yml` did not pass the policy; they were never scanned.
+- **The scan is diff-scoped.** It flagged only `lint.yml` while `deploy.yml` sat unmodified on the
+  same branch with four unpinned actions. Pre-existing workflow debt therefore stays invisible
+  until a PR touches that file — worth auditing a workflow *before* you need to edit it under
+  time pressure.
+
+**2026-08-05 (Step 3.1)**
+
+- **`title` is not always metadata.** Zensical synthesises an H1 from `title` when a page has
+  no heading of its own, and leaves an existing heading alone. Five pages rely on this. A
+  first attempt assumed the title *always* replaced the H1 and dropped ten disambiguated
+  model-card titles to avoid a content change that could not actually happen — checking the
+  built HTML rather than reasoning about the renderer settled it in one command.
+- **A frontmatter key is either authored or computed, never both.** Backfilling
+  `estimated_tokens` by hand would have guaranteed drift the moment anyone edited a page.
+  The linter now rejects hand-written computed keys outright, so Step 3.2 can land without
+  first cleaning up after Step 3.1.
+- **The linter now needs PyYAML.** Frontmatter carries lists (`models`, `frameworks`), and the
+  old line-splitting parser read `[gemma-4]` as the string `"[gemma-4]"`. `lint.yml` installs
+  it explicitly — the workflow's "stdlib-only, nothing to install" assumption no longer holds.
+- **The backfill script found two of its own bugs through the schema it writes.** `models:
+  [releases.md]` (a filename read as a model id) and a nav *section* label leaking onto a page
+  title. Both were caught by the vocabulary check within seconds of the first run. Validating
+  against a repository-derived vocabulary rather than a free-text field is what made that work.
+- **Derived descriptions need a human pass.** 76 were extracted from body text; 24 were then
+  rewritten by hand. The extractor flags its own weak output — truncated, duplicated, or
+  fallback — which is what made the review tractable. Two upstream patterns caused most of it:
+  pages that open with the boilerplate "Gemma is a family of…" paragraph before saying what
+  the page covers, and `#` comments inside bash fences being read as headings.
+- **Provenance gap closed.** All 63 pages in `sources.toml` now carry `source_url`, including
+  the `get-started/**` pages ported before the manifest existed.
+- ⚠️ **`zensical build` reports 28 "page does not exist" warnings that are false positives.**
+  Every one checked resolves correctly in the built HTML (`../models/gemma-4/index.md` →
+  `../../models/gemma-4/`). They are pre-existing and unrelated to frontmatter, but they make
+  the build output useless as a signal — worth chasing before anyone trusts it as a gate.
+
+**2026-08-05 (schema revision + plan split)**
+
+- **A wildcard and an omission cannot mean different things if they look the same.** `models:
+  [all]` was being written on twelve pages that are not about a model at all — community, tools,
+  the cookbook. "Applies to every model" and "is about no model" need different renderings in the
+  page context header, so the wildcard was removed: absence now means the second, and the first
+  has no page that needs it.
+- **A bare size id is ambiguous and always will be.** `12b` says nothing about which generation;
+  Gemma 3 and Gemma 4 both ship one. Values are now `family` or `family/size`, with the
+  separator `/` rather than `:` — `[gemma-4:12b]` risks being read as a mapping in YAML flow
+  context, and the frontmatter is parsed by both our linter and Zensical.
+- **`ported_at` removed.** It was a second, staler copy of what git already records, and every
+  value in it came from `git log` in the first place. `source_url` alone answers the question the
+  key existed for: *what was this page ported from?*
+- **`data/frameworks.yml` added.** The framework vocabulary was a hardcoded set in the schema
+  module; it now sits beside `data/models.yml` with display names, categories and the docs page
+  for each. Both the linter and the Step 4.2 switcher need that metadata, and neither should read
+  it out of a Python literal.
+- **`PLAN.md` split at 1,395 lines.** Five completed steps were recorded twice — once as the plan
+  and once as the outcome — and Step 0.7 had drifted below *Open decisions*. Completed work and
+  the notes log moved to `PLAN-ARCHIVE.md`; `PLAN.md` is now 530 lines of what is still ahead.
+  The duplication was not accidental: each step was written as a plan, then a result section was
+  appended without removing the plan. Ticking a box in place would have avoided it.
+- **Tooling lifecycle recorded.** Roughly 2,450 of 3,877 script lines exist only to migrate
+  content off devsite, and all of them are gated on one unmade decision — whether anything else
+  gets ported from upstream. `sources.toml` in particular now duplicates `source_url` on every
+  page it lists.
+
+**2026-08-05 (page context strip)**
+
+- ⚠️ **A frontmatter key silently shadowed a macros variable.** Step 3.1 added `models:` to every
+  page; the macros plugin exposed `data/models.yml` as `models` too. Page metadata wins, so
+  `models.gemma4.sizes` resolved to the page's own `["gemma-4"]` list and both generated tables
+  rendered their Jinja source as literal text on screen. Nothing failed the build, nothing failed
+  the linter — it was only visible by looking at the page. The data variable is now `modeldata`.
+  Worth remembering that the frontmatter namespace and the macros namespace are the same
+  namespace.
+- **Measure the target design, don't recall it.** The first attempt at this strip was a row of
+  bordered chips above the H1. Loading docs.stripe.com and reading the box metrics showed the
+  space above the title is empty, and that their control row is flex/16px-gap/baseline with a
+  hairline rule under it. Two of those — the placement and the rule — were what made the first
+  version look unfinished.
+- **`getComputedStyle().font` returns an empty string** when the font was set via separate
+  properties, so a text-measuring ruler silently inherited the body font and sized the selector
+  to "1…" instead of "12B". Copy the individual font properties, and remember a border-box
+  element's declared width has to cover its own padding.
+- **A native select sizes to its widest option**, which strands a caret next to a short label.
+  Width is set from the selected option's measured text instead.
+- **Opt-in substitution earns its keep immediately.** On `deployment/ollama.md` the `pull`
+  command follows the selector while the tag table three lines below it — which must list every
+  tag — stays untouched. A blanket "rewrite every identifier" pass would have corrupted it.
+
+**2026-08-05 (page context strip, second pass)**
+
+- **The design error was conceptual, not visual.** The selector was driven by `models:`
+  frontmatter, which answers "what is this page about" — a fact. A selector answers "what do you
+  want the commands to target" — a preference. Conflating them produced a control that offered
+  exactly five Gemma 4 sizes on a page whose engine can run anything, and that claimed `E2B` on
+  Quick Start, a page covering all five. Deriving the options from the identifiers actually
+  present in the page's marked code blocks fixes both, and makes the control vanish where there
+  is nothing to switch.
+- **A taxonomy has to answer one question.** `data/frameworks.yml` had `local` / `serve`
+  (where it runs) next to `library` / `tuning` / `orchestration` (what it is), so `transformers`
+  and `llamacpp` — both libraries, both usually local — landed in unrelated groups. Replaced
+  with `url`. If a field's values can't be produced by asking a single question, it is two
+  fields or none.
+- **Stripe's separator is a 1px `<span role="separator">`** with `align-self: stretch` and a
+  background one step darker than the rule closing the row — not a `|` glyph, and not absent as
+  an earlier measurement pass concluded. It was missed because the DOM sweep only collected
+  elements that had text.
+- **Copying a reference design's *treatment* without matching its *structure* fails.** Stripe's
+  toolbar items are peer actions at one weight, so flat styling works. Ours are label→value
+  pairs; flat styling made "Frameworks" and "Sentence Transformers" indistinguishable. Hierarchy
+  had to come from spacing ratio and consistent label/value contrast, which Stripe's page does
+  not demonstrate because it never needs to.
+- ⚠️ **Do not test colour schemes by setting `data-md-color-scheme` from the console.** Anchor
+  colours do not re-resolve that way, which produced a phantom 2.06:1 contrast failure and a
+  long hunt through the cascade. Click the real palette toggle; the same measurement then read
+  13.36:1.
+- **Material's `.md-typeset a` is (0,2,0).** A bare component class for a link inside content is
+  (0,1,0) and loses, so the framework links rendered in link-blue and competed with the selector
+  for attention.
diff --git a/PLAN.md b/PLAN.md
index 729fd7d..d88bb70 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -10,19 +10,22 @@ partners a place to showcase their work.
 **Gemma 4 is front and center.** All other models are documented, but the docs are
 implicitly about Gemma 4 unless stated otherwise.
 
+> Completed work, outcome tables, and the running log of lessons live in
+> [`PLAN-ARCHIVE.md`](PLAN-ARCHIVE.md). This file is only what is still ahead.
+
 ---
 
 ## Working agreement
 
-- **Bite-sized steps.** Each step below is independently testable and roughly one session of work.
-- **Every step ends with a verification command** and a "done when" condition. Run it before moving on.
-- **Maarten does all commits and pushes.** Agents stage nothing and push nothing — finish the step,
-  report what changed, and stop.
+- **Bite-sized steps.** Each step is independently testable and roughly one session of work.
+- **Every step ends with a verification command** and a "done when" condition. Run it before
+  moving on.
+- **Maarten does all commits and pushes.** Agents stage nothing and push nothing — finish the
+  step, report what changed, and stop.
 - **Handover-friendly.** Each step is self-contained enough that a fresh agent can pick it up
-  from this file alone. If you complete a step, tick its box and note anything surprising
-  under *Notes* at the bottom.
-- **Foundation before fun.** Phases 0–2 must be complete and clean before Phase 4 work starts.
-  A WebGPU playground next to 34 "Content coming soon" pages reads as a prototype, not a product.
+  from this file alone. Record anything surprising in the archive's *Notes* log.
+- **Foundation before fun.** Phases 0–2 must be clean before Phase 4 work starts. A WebGPU
+  playground next to "Content coming soon" pages reads as a prototype, not a product.
 
 ### Content fidelity — non-negotiable
 
@@ -32,36 +35,34 @@ that validation is only transferable if the words survive the port intact.
 - A port is a **format** migration, not an editorial one. Convert devsite syntax to Zensical
   syntax, fix links and image paths, repair converter damage — and change nothing else.
 - Do **not** reword, tighten, restructure, "improve", modernize, or re-order ported prose.
-  Do not silently correct something that looks wrong: flag it under *Notes* and ask.
+  Do not silently correct something that looks wrong: flag it and ask.
 - Code snippets are copied verbatim. Changing a flag or an argument invalidates the validation.
 - **New pages may remix.** Combining snippets from several source pages into a new page is fine
   and encouraged — but the snippets themselves are still copied, not rewritten.
-- **Start with a straight port.** Port a page as-is first, confirm it renders well, and only
-  then consider whether it needs a companion page. Don't port and redesign in one motion.
-- Every ported page records its source in frontmatter (`source_url`, `ported_at`) so any page
-  can be diffed against upstream later.
+- **Start with a straight port.** Port a page as-is first, confirm it renders well, and only then
+  consider whether it needs a companion page. Don't port and redesign in one motion.
+- Every ported page records `source_url` in frontmatter, so any page can be diffed against
+  upstream later.
 
 Rule of thumb: if a diff against the source `.md.txt` shows a prose change, it needs a reason
 written down.
 
 ### Import once, then hand-maintain — non-negotiable
 
-**Every page is hand-maintained the moment it is imported.** Maarten edits pages continuously
-to get them looking right; re-running an importer over one would silently discard that work.
+**Every page is hand-maintained the moment it is imported.** Maarten edits pages continuously to
+get them looking right; re-running an importer over one would silently discard that work.
 
-All **64** manifest pages carry `frozen = true`. There are no unfrozen pages, and that is the
+All **63** manifest pages carry `frozen = true`. There are no unfrozen pages, and that is the
 steady state — not a backlog to work through.
 
-- `port_docs.py` skips frozen pages: `frozen (hand-maintained, skipped)`.
-- `check_fidelity.py` skips them too — divergence from upstream is now *intended*, so comparing
-  against the source would produce noise rather than signal.
-- The `source` line stays for provenance: `source_url` frontmatter (Step 3.1) needs it, and it
-  records where the page came from.
+- `port_docs.py` skips frozen pages. `check_fidelity.py` skips them too: divergence from
+  upstream is now *intended*, so comparing would produce noise rather than signal.
+- The `source` line stays for provenance.
 
 **Importing a new page** is the only time a converter runs:
 
 1. Add it to `scripts/sources.toml` *without* `frozen`.
-2. Run the pipeline (`port_docs.py` → `vendor_images.py` → `rewrite_links.py`).
+2. Run the pipeline: `port_docs.py` → `vendor_images.py` → `rewrite_links.py`.
 3. Review the result.
 4. **Set `frozen = true` immediately.** Before any hand-editing, not after.
 
@@ -70,9 +71,27 @@ steady state — not a backlog to work through.
     equivalent change applied by hand. Weigh that before adding converter transforms aimed at
     pages that are already in.
 
-**Visual guides** use `scripts/import_newsletter.py` instead, marked `importer =
-"import_newsletter"`. `gemma-4.md` and `mtp.md` were additionally split by hand after import —
-re-importing either would merge MTP back into the parent guide and drop the pointer.
+**Visual guides** use `scripts/import_newsletter.py` instead (`importer = "import_newsletter"`).
+`gemma-4.md` and `mtp.md` were split by hand after import — re-importing either would merge MTP
+back into the parent guide and drop the pointer.
+
+### Standing rules
+
+Learned the hard way; each one cost a debugging session. Full stories in the archive.
+
+- **Never re-convert in bulk.** See above. There is no automatic way to tell hand-work from a
+  straight port.
+- **Any new workflow must be zizmor-clean before it is pushed.** Pin every action to a SHA, set
+  an explicit `permissions:` block, scope write permissions to the job that needs them. Check
+  with `uvx zizmor --persona=regular .github/workflows/` — seconds, and it catches what CI does.
+  The scan is diff-scoped, so pre-existing workflow debt stays invisible until a PR touches it.
+- **Measure the rendered page, don't reason about the renderer.** Four layout bugs in Step 1.4
+  and one in Step 3.1 were invisible in the source and obvious in one DOM query.
+- **`data/models.yml` and `data/frameworks.yml` are the single sources of truth.** Hand-editing a
+  model table in a page is a bug. Adding a framework name that isn't in the data file is a bug.
+- **Frontmatter keys shadow macros variables.** `models:` frontmatter silently broke
+  `models.gemma4.sizes` in both generated tables. The macros data is exposed as `modeldata.*`
+  for exactly this reason — never name a frontmatter key after an `include_yaml` variable.
 
 Local preview:
 
@@ -82,51 +101,37 @@ uv run zensical serve
 
 ---
 
-## Original state (audit, Aug 2026)
+## Current status
+
+| Phase | State |
+|---|---|
+| 0 — Foundation | ✅ complete |
+| 1 — Design system | ✅ complete |
+| 2 — Navigation & content | 🟡 nav and Run section done; 7 stubs and 6 keep-or-drop decisions left |
+| 3 — Agent-first infrastructure | 🟡 3.1 done; 3.2–3.5 open |
+| 4–8 | not started |
+
+89 pages · 63 ported and frozen · 9 stylesheets · lint backlog **7** (all stubs).
 
-This table is the migration's starting baseline, retained for historical context. Current
-status is recorded in the completed phase sections and the maintenance baseline below.
+**Remaining Phase 2 work**, all blocked on content decisions rather than engineering:
 
-| Metric | Value |
+| Page | Blocked on |
 |---|---|
-| Markdown files | 80 |
-| Nav entries | 76 |
-| Real ported content (>1.5 KB) | 44 |
-| One-line stubs (<250 B) | 34 |
-| Pages with frontmatter | 9 / 80 |
-| Links still pointing at `ai.google.dev/gemma/docs` | 122 |
-| Images hotlinked to `ai.google.dev/static` | 18 (0 vendored) |
-| Orphan pages (exist, not in nav) | 4 |
-| Orphan stylesheet (exists, not in `extra_css`) | 1 (`custom.css`) |
-
-Known defects:
-
-- Malformed code fences from the notebook converter — e.g. `docs/models/gemma-4/index.md:117`
-  wraps prose in a ```` ```python ```` fence mid-sentence.
-- 7 prompt-template blocks tagged `python` that are plain text.
-- ~16 unconverted devsite `Note:` / `Key Point:` / `Caution:` callouts.
-- `docs/models/paligemma/fine-tuning.md` is 435 KB (base64 images inlined by the converter).
-- `docs/community/gemmaverse.md` still ships `example.com` placeholder cards.
-- Modality data disagrees across `quickstart.md`, `models/index.md:14`, and `models/gemma-4/index.md:45`.
+| `community/gemmaverse.md` | real project data (still ships `example.com` cards) |
+| `community/discord.md` | port `/discord` |
+| `community/gemma-skills.md` | content — no upstream source |
+| `cookbook.md` | content decision |
+| `demos/concurrent.md` | content — the app lives in the cookbook repo |
+| `tools/index.md`, `tools/model-selector.md` | **Phase 4** — these *are* the interactive tools |
 
----
+**Upstream pages not ported (6):** `/core/gemma_library`, `/core/pytorch_gemma`,
+`/core/gemma_on_gemini_api`, `/core/deploy_to_cloud_run_from_ai_studio`, `/gemma_chat`,
+`/meme-generator`. Each needs a keep-or-drop decision rather than a straight port —
+`/meme-generator` in particular is a demo app, not reference material.
 
-## Maintenance baseline (2026-08-04)
-
-- [x] Zensical is pinned exactly (`zensical==0.0.51`) and deployment runs with
-      `uv run --frozen`, so a package release cannot silently restyle production.
-- [x] Deployment gates the build on converter tests and baseline-aware docs lint.
-- [x] Stylesheets consolidated from 11 to 8: the active homepage CSS moved out of its template,
-      three obsolete homepage sheets were removed, and the one-rule `code.css` was folded into
-      `content.css`.
-- [x] CSS lint rejects template `<style>` blocks in addition to enforcing registration, token
-      use, and the 250-line limit.
-- [x] Internal-link lint validates `#fragments`, including same-page links. The three real broken
-      fragments from the August build were repaired. The larger missing-page list in the captured
-      output was transient incremental-build state; a clean build is the gate.
-- [x] Replaced the placeholder README and removed the unused `main.py` scaffold.
-- [x] Import, conversion, and image-vendoring scripts remain in place by decision. Demo/vendor
-      images are untouched until original PNGs replace them.
+Deliberately not ported and **not** gaps: `/integrations/{ollama,llamacpp,lmstudio,mlx}`
+(written as quick references instead), `/core/gke` (folded into Google Cloud),
+`/codegemma/*` (dropped by decision).
 
 ---
 
@@ -137,487 +142,56 @@ capabilities and belong in one place where the full picture is visible.
 
 ```
 Home
-Get started    Quickstart · Install & setup · Choose your model
-               Prompt & chat format
+Get started    Quickstart · Prompt & chat format
                Capabilities: Text · Thinking · Function calling
-                             Vision (Image, Video) · Audio
-                             Multi-Token Prediction · Long context
-Models         Gemma 4 (overview, model card, benchmarks)
+                             Vision (Image, Video) · Audio · Multi-Token Prediction
+Models         Gemma 4 (overview, model card)
                Variants: Gemma 3n · DiffusionGemma · FunctionGemma
                          EmbeddingGemma · PaliGemma · ShieldGemma
                Previous generations: Gemma 3 · 2 · 1
-               Research: RecurrentGemma · DataGemma · Gemma Scope
+               Research: RecurrentGemma · DataGemma · Gemma Scope · Gemma APS
                Releases
 Run            Local:  Ollama · llama.cpp · LM Studio · MLX · Transformers
-                       LiteRT · WebGPU · Mobile
-               Serve:  vLLM · SGLang · TGI · Google Cloud (GKE / Cloud Run)
-               Quantization & QAT · Speculative decoding
+                       Keras · LiteRT · Web · Mobile
+               Serve:  vLLM · SGLang · Google Cloud
+               Frameworks: LangChain · Quantization & QAT
 Fine-tune      Overview · QLoRA · Full fine-tune · Vision QLoRA
-               Keras LoRA · Distributed · Dataset prep · Evaluation
-Learn          Visual guides · Tutorials · Notebooks · Videos
+               Keras LoRA · Distributed · Spoken language · Mobile actions
+Learn          Visual guides · Tutorials · Notebooks
 Demos          Interactive tools · Apps & Spaces
-Ecosystem      Partners · Gemmaverse · Discord · Skills · Contribute
+Ecosystem      Gemmaverse · Discord · Skills · Contribute
 ```
 
-**Models stays model-card-centric.** It closely mirrors the upstream structure on purpose —
-model cards are canonical reference and shouldn't be creatively restructured. The improvement
-is in presentation, consistency, and cross-linking, not reorganization.
+**Models stays model-card-centric.** It mirrors the upstream structure on purpose — model cards
+are canonical reference and shouldn't be creatively restructured. The improvement is in
+presentation, consistency, and cross-linking, not reorganization.
 
-**Demos absorbs Tools.** "Interactive tools" = things we built (VRAM calculator, model selector,
-tokenizer playground). "Apps & Spaces" = embedded HF Spaces and showcase apps. This gives Demos
-real substance instead of one stub page.
+`docs/models/` stays **flat**; grouping is nav-only, so a model's URL survives reclassification.
 
-Decisions made: no "Migrating from Gemma 3 → 4" page (users swap one model string).
-Visual guides get fully ported pages, not outbound links.
+Decisions made: no "Migrating from Gemma 3 → 4" page (users swap one model string). Visual
+guides get fully ported pages, not outbound links. Nav labels are short ("Ollama"); page H1s
+stay as written ("Run Gemma with Ollama").
 
 ---
 
 ## Design system
 
-Standardized components throughout — styled boxes, frames, and animations reused everywhere
-rather than invented per page.
-
 **Official Gemma palette:**
 
 | Token | Hex | Use |
 |---|---|---|
-| `--gemma-blue` | `#3186FF` | Primary — links, active nav, primary buttons |
-| `--gemma-blue-deep` | `#346BF1` | Hover/pressed states, gradient anchor |
-| `--gemma-blue-light` | `#4FA0FF` | Accents, highlights, dark-mode primary |
+| `--gemma-blue` | `#3186FF` | **Fill only** — buttons, bars, icons. 3.51:1 on white fails AA for text. |
+| `--gemma-blue-deep` | `#346BF1` | Text on light surfaces (4.65:1) |
+| `--gemma-blue-light` | `#4FA0FF` | Text on dark surfaces (5.98:1) |
 | `--gemma-white` | `#FFFFFF` | Light surface |
 | `--gemma-ink` | `#202124` | Dark surface, body text on light |
 
-All new CSS derives from these. No ad-hoc hex values outside the token file.
-
----
-
-## Phase 0 — Foundation: make the existing content correct
-
-*Nothing here is glamorous. Everything else compounds on top of it, and CI checks stop the
-debt from coming back.*
-
-### Step 0.1 — Repo hygiene and CI guardrails ✅
-- [x] `scripts/lint_docs.py` with 9 checks: `links`, `images`, `internal`, `stubs`,
-      `frontmatter`, `orphans`, `fences`, `admonitions`, `size`.
-- [x] Baseline mechanism (`.lintbaseline.json`) so CI fails only on *new* violations while the
-      backlog is burned down. **Shrinking the baseline to zero is the definition of done for Phase 0.**
-- [x] `.github/workflows/lint.yml` — runs on push, PR, and dispatch. Stdlib-only, no install step.
-- [x] Deleted `docs/stylesheets/custom.css` (dead: not in `extra_css`, fully superseded by
-      `layout.css`, and would have conflicted on `.md-grid` max-width and sidebar width).
-- **Verify:** `uv run python scripts/lint_docs.py --baseline` → exit 0.
-      `uv run python scripts/lint_docs.py` → full 300-finding backlog.
-- **Done when:** ✅ regression test confirmed — adding an upstream link exits 1, reverting exits 0.
-
-**Baseline at 2026-08-03 (the Phase 0 burn-down list):**
-
-| Check | Count | Notes |
-|---|---|---|
-| `frontmatter` | 81 | 71 pages with no block + missing keys elsewhere |
-| `links` | 122 | 5 of these are inside code blocks |
-| `images` | 52 | 18 hotlinked, 34 inline base64 (all in `paligemma/fine-tuning.md`) |
-| `stubs` | 33 | |
-| `fences` | 7 | prompt templates tagged `python` |
-| `orphans` | 4 | |
-| `size` | 1 | `paligemma/fine-tuning.md` at 435 KB |
-| `internal` | 0 | all relative links resolve |
-| `admonitions` | 0 | **converter already handles these correctly** — 36 converted |
-| **Total** | **300** | |
-
-Useful commands:
-
-```bash
-uv run python scripts/lint_docs.py --list
-```
-
-```bash
-uv run python scripts/lint_docs.py --check links,images
-```
-
-```bash
-uv run python scripts/lint_docs.py --write-baseline
-```
-
-### Step 0.2 — Fix the notebook converter ✅
-- [x] **Wrapped list continuations** no longer eaten as code. Devsite wraps long bullets with an
-      indented continuation line; the 4-space rule fenced it as `python`. The guard meant to
-      prevent this (`lines[i-1].strip().startswith("    ")`) could never fire — `.strip()`
-      removes the very indentation it tests. Continuations are now rejoined onto their bullet,
-      which restores the original sentence exactly (markdown renders `foo\n  bar` in a list as
-      `foo bar`, so nothing about the content changes).
-- [x] **Language detection** gained a `text` fallback: `bash` → `python` → `text`. Detection now
-      uses Python *syntax* signals, deliberately not bare English words like "for"/"with"/"from",
-      which is why prompt templates were being classed as code.
-- [x] **Indented bullets no longer split a block.** A bullet may not *start* a block (that would
-      eat indented lists), but once a block is underway an indented bullet is block content —
-      previously one ASR prompt became a fence plus orphaned bullets.
-- [x] **Base64 extraction** via `--assets-dir` / `--assets-url`. Anchored on `![...](...)` so a
-      data URI inside real code is left alone.
-- [x] Golden tests: `scripts/test_convert_notebook.py` + 4 fixtures in `scripts/fixtures/`,
-      each a minimised reproduction of a real defect. 11 tests, wired into CI.
-- **Verify:** `uv run python scripts/test_convert_notebook.py` → 11 passing.
-- **Done when:** ✅ re-converting `core.md.txt` produces the QAT bullets intact with **zero**
-      python fences; `audio.md.txt` yields 8 `python`, 2 `bash`, 2 `text` fences, both ASR
-      prompts correctly `text`.
-
-**Content-fidelity check** (word multiset, source vs. output):
-
-| Source | Dropped | Verdict |
-|---|---|---|
-| `core.md.txt` | `NOTE` ×1 | the `> [!NOTE]` → `!!! Note` conversion. Additions are cookbook-card markup. |
-| `audio.md.txt` | 111, all badge/metadata | `colab_logo`, `notebook-site-button.png`, `View`, `Run`, `<br>` — the intended stripping. |
-
-**Zero prose lost in either.** Regenerate goldens with `--update`, then review the diff —
-those files are the specification.
-
-```bash
-uv run python scripts/test_convert_notebook.py
-```
-
-### Step 0.3 — Re-port Models, targeted repair for Get Started
-
-Two different strategies, because the two sections have different histories:
-
-- **Models is a straight port.** No hand-authoring, so re-converting is safe and correct. ✅ Done.
-- **Get Started is hand-edited and reviewed.** `quickstart.md` has a hand-authored model table,
-  the `npx skills add` admonition, and `hide: toc` frontmatter — none of it in upstream's
-  `get_started.md.txt`. Re-converting would destroy that. Repair defects in place.
-
-#### 0.3a — Porting infrastructure ✅
-- [x] `scripts/sources.toml` — manifest mapping each ported page to its upstream source.
-      **A page absent from the manifest must never be re-converted.** Also the origin of the
-      `source_url` frontmatter in Step 3.1.
-- [x] `scripts/check_fidelity.py` — word-multiset comparison proving a conversion changed markup
-      and nothing else. Compares *source minus what the converter is designed to drop* vs. output,
-      reusing the converter's own `is_badge_line` rather than a parallel word blocklist, so the
-      two can't drift apart.
-- [x] `scripts/port_docs.py` — batch porter. **Fidelity-gated: a page that drops prose is never
-      written.** Caches sources under `.sources/` (gitignored).
-- [x] New converter fix: **consecutive blank lines no longer split a code block.** Notebook
-      exports routinely leave two blank lines mid-cell; the old lookahead checked only one line
-      ahead, so one function became two fences (found at `paligemma/inference.md`, `binary_mask`).
-- [x] New linter check: two same-language fences separated only by blank lines — the signature of
-      a split block. Found **9 across 7 files**, well beyond the one reported.
-
-#### 0.3b — Models re-ported ✅
-- [x] All **37** manifest pages re-ported. Fidelity: 37 faithful, 0 with dropped prose.
-      Re-running is idempotent (all "unchanged").
-- [x] `paligemma/fine-tuning.md`: **435 KB → 51 KB**, 34 images extracted to `docs/assets/paligemma/`.
-- [x] `models/releases.md`: 58-byte stub → full port (+4.2 KB).
-- **Verify:** `uv run python scripts/port_docs.py --filter models/ --dry-run` → 37 ok, 0 failed.
-
-#### 0.3c — Get Started targeted repair ✅
-
-Diffing each page against a fresh conversion first confirmed both carry real hand-edits, so
-repair-in-place was the correct call:
-
-| Page | Hand-edits preserved |
-|---|---|
-| `capabilities/image.md` | title recased, `## Summary` edit, `<!-- TODO: Port over -->` block |
-| `capabilities/audio.md` | hand-added `# Audio Understanding`, `## Summary` edit, TODO block |
-
-- [x] `capabilities/audio.md` — two ASR prompt templates `python` → `text`.
-- [x] `capabilities/audio.md:81` — unconverted `> > [!NOTE]` callout → `!!! Note`.
-- [x] `capabilities/image.md` — three split blocks rejoined. Diff is exactly 6 lines
-      (the fence pairs); no content touched.
-- [x] New linter coverage: GitHub-style `> [!NOTE]` callouts. The `admonitions` check only
-      knew about devsite `Note:` prefixes, so this defect was invisible.
-- **Verify:** `uv run python scripts/lint_docs.py --check fences,admonitions` → zero.
-- **Done when:** ✅ zero fence and admonition findings site-wide; remaining diff vs. a fresh
-      conversion is hand-edits only.
-
-**Technique worth reusing:** diffing a page against a fresh conversion cleanly separates
-*bugs* from *hand-edits*. Do this before touching any page whose history is unclear — it is the
-manual form of the `--divergence` check and it is what proved re-porting would have been wrong here.
-
-### Phase 0 progress: 300 → 285
-
-| Check | Start | Now | |
-|---|---|---|---|
-| `fences` | 7 | **0** | ✅ cleared |
-| `size` | 1 | **0** | ✅ cleared |
-| `admonitions` | 0 | **0** | ✅ 1 found and fixed |
-| `internal` | 0 | **0** | ✅ |
-| `images` | 52 | **18** | base64 done; 18 hotlinked remain → Step 0.4 |
-| `stubs` | 33 | **32** | → Steps 2.2–2.4 |
-| `orphans` | 4 | **4** | → Step 2.1 |
-| `frontmatter` | 81 | **81** | → Step 3.1 |
-| `links` | 122 | **150** | +28 from `releases.md` gaining content → Step 0.5 |
-
-**Backlog after the Models port — 300 → 290:**
-
-| Check | Was | Now | |
-|---|---|---|---|
-| `images` | 52 | **18** | 34 base64 extracted |
-| `size` | 1 | **0** | cleared |
-| `fences` | 7 | **5** | model-card fences fixed; 5 left, all in Get Started |
-| `stubs` | 33 | **32** | `releases.md` now real content |
-| `links` | 122 | **150** | +28 from `releases.md` gaining content — Step 0.5 rewrites them |
-| `frontmatter` / `orphans` / `internal` / `admonitions` | 81 / 4 / 0 / 0 | unchanged | later steps |
-
-```bash
-uv run python scripts/port_docs.py --filter models/ --dry-run
-```
-
-```bash
-uv run python scripts/check_fidelity.py --manifest --cache-dir .sources
-```
-
-### Step 0.4 — Vendor all images ✅
-- [x] `scripts/vendor_images.py` — downloads remote images into `docs/assets/<section>/` and
-      rewrites the references. Section = the page's parent directory, matching the layout
-      `port_docs.py` already uses. Original filenames are kept so any asset traces back to its
-      upstream URL. A failed download leaves the reference untouched (a broken local path is
-      worse than a working remote one) and the script is idempotent, so re-running retries.
-- [x] All **18** hotlinked images vendored, 18/18 succeeded. `docs/assets/` is now 8.2 MB.
-- [x] Fidelity checker taught to ignore image *targets* on both sides — only alt text is prose.
-      Without this, vendoring read as 6 pages losing prose and would have **blocked those pages
-      from ever being re-ported**, since `port_docs.py` is fidelity-gated.
-- [ ] ~~Dark-mode variants~~ — **deferred to Phase 1** with the rest of the design system.
-- **Verify:** ✅ zero `ai.google.dev/static` references in the built `site/`; all 214 local
-      `<img>` sources resolve on disk.
-- **Done when:** ✅ the site renders fully with no network access to Google.
-
-!!! warning "Ordering: port, then vendor"
-    Conversion restores the upstream image URLs, so **re-porting a page undoes its vendoring**
-    (confirmed: re-porting `models/gemma-4/model-card.md` would revert one image, +21 bytes).
-    Always run `vendor_images.py` after `port_docs.py`. `port_docs.py` prints this reminder, and
-    since the `images` check now sits at zero, a missed run fails CI.
-
-```bash
-uv run python scripts/vendor_images.py --dry-run
-```
-
-### Step 0.4b — CI hardening (zizmor / org policy) ✅
-
-The org runs [zizmor](https://docs.zizmor.sh/) on workflows and **fails on any Medium/High
-finding**, not just the mandatory-check list. Both workflows now pass clean.
-
-- [x] All 6 action references pinned to commit SHAs in `lint.yml` and `deploy.yml`
-      (`unpinned-uses`, High — this is what broke the build).
-- [x] `lint.yml` given an explicit `permissions: contents: read` block.
-- [x] `deploy.yml` permissions narrowed: `contents: read` at workflow level, with
-      `pages: write` / `id-token: write` scoped to the `deploy` job alone. At workflow level they
-      were also being handed to the `build` job, which only needs to read (`excessive-permissions`, High).
-- [x] `persist-credentials: false` on both checkouts (`artipacked`).
-- **Verify:** `uvx zizmor --persona=regular .github/workflows/` → "No findings to report."
-
-!!! note "Updating a pin"
-    Pins carry a `# v4` comment so the intended version stays readable. To move one:
-
-    ```bash
-    gh api repos/actions/checkout/git/ref/tags/v4 --jq '.object.sha'
-    ```
-
-    For annotated tags, dereference: `gh api repos/<repo>/git/tags/<sha> --jq '.object.sha'`.
-
-### Step 0.5 — Rewrite internal links ✅
-
-- [x] `scripts/link_map.toml` — reviewable data, not logic buried in a script. Three sections:
-      `[ported]` (target exists), `[planned]` (eventual path; tracked as known-pending),
-      `[delink]` (drop the link, keep the text).
-- [x] `scripts/rewrite_links.py` — applies the map, computing each relative path per page.
-      An unmapped target is **reported and left alone**; silently dropping a link is worse than
-      leaving one to fix.
-- [x] **144 prose links rewritten**: 122 ported, 20 planned, 2 delinked. Zero unmapped.
-- [x] **CodeGemma delinked** (decision: not being ported). Both references in `releases.md`
-      keep their text — "Release of CodeGemma v1.1." still reads correctly.
-- [x] `--fix-anchors` pass: 12 fragments repaired. devsite used `#model_data`, Zensical emits
-      `#model-data`; 8 were the mechanical underscore→hyphen case, 4 needed the explicit
-      `[anchors]` override table because the heading text itself changed.
-- **Verify:** `uv run python scripts/lint_docs.py --check links` → zero. Both passes idempotent.
-- **Done when:** ✅ zero outbound links to the site we're replacing.
-
-**Left external on purpose** — 5 URLs inside code blocks: four are sample data the reader
-fetches (`img_url = "https://.../cat.png"`) and one is a BibTeX citation. Rewriting those would
-break copy-paste and misattribute the citation. `check_links` now skips code blocks for this reason.
-
-**5 anchors remain unresolved**, none of them actionable now:
-
-| Anchor | Why |
-|---|---|
-| `huggingface.md#audio`, `#vision` | target is a stub; resolves in Step 2.2 |
-| `quickstart.md#models-list` | section removed during hand-editing |
-| `paligemma/index.md#categories` | **broken upstream too** — no such section on ai.google.dev |
-| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
-
-The last two were dead links on the original site; the port reproduced them faithfully.
-Inventing a target would be an editorial guess, so they are left alone pending a content decision.
-
-```bash
-uv run python scripts/rewrite_links.py --report
-```
-
-### Phase 0 progress: 300 → 137
-
-| Check | Start | Now | |
-|---|---|---|---|
-| `links` | 122 | **0** | ✅ cleared |
-| `images` | 52 | **0** | ✅ cleared |
-| `fences` | 7 | **0** | ✅ cleared |
-| `size` | 1 | **0** | ✅ cleared |
-| `admonitions` | 0 | **0** | ✅ |
-| `internal` | 0 | 20 | known-pending: links to planned pages |
-| `stubs` | 33 | 32 | → Steps 2.2–2.4 |
-| `orphans` | 4 | 4 | → Step 2.1 |
-| `frontmatter` | 81 | 81 | → Step 3.1 |
-
-
-- [ ] Map all 122 `ai.google.dev/gemma/docs/*` links to local paths.
-- [ ] For pages not yet ported, point at the eventual local path and let the linter flag it as
-      a known-pending target (don't leave live external links).
-- **Verify:** `uv run python scripts/lint_docs.py --check links`
-- **Done when:** zero outbound links to the site we're replacing.
-
-### Step 0.5b — Port the pages Step 0.5 pointed at ✅
-
-Step 0.5 left 19 build warnings by design (links aimed at eventual paths). Every one of those
-targets turned out to exist upstream, so porting them was cheaper than leaving the warnings —
-and it advanced Step 2.4 at the same time.
+Use `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in dark, rather
+than naming a brand colour directly.
 
-- [x] **10 pages ported**, all fidelity-clean:
-
-| Page | Source | Size |
-|---|---|---|
-| `get-started/setup.md` | `/setup` | 2.5 KB |
-| `get-started/prompt-structure.md` | `/core/prompt-structure` | 2.2 KB |
-| `get-started/capabilities/mtp.md` | `/mtp/overview` | 2.8 KB |
-| `deployment/index.md` | `/run` | 10.7 KB |
-| `deployment/huggingface.md` | `/core/huggingface_inference` | 11.9 KB (**replaced a stub**) |
-| `deployment/mobile-actions.md` | `/mobile-actions` | 4.2 KB |
-| `tuning/spoken-language.md` | `/spoken-language/task-specific-tuning` | 22.9 KB |
-| `learn/business-email-assistant.md` | `/business-email-assistant` | 20.5 KB |
-| `learn/personal-code-assistant.md` | `/personal-code-assistant` | 22.6 KB |
-| `models/gemma-aps/index.md` | `/gemma-aps` | 1.8 KB |
-
-- [x] **The MTP gap is closed.** It was the flagship Gemma 4 feature with zero documentation.
-- [x] Link map grew to 75 `[ported]` entries; 23 more anchor overrides applied.
-- [x] Fixed a mis-targeted mapping: `/core/prompt-system-instructions` (no such upstream page)
-      was sent to `paligemma/prompting.md`, but the referring context is core Gemma
-      `start_of_turn`/`end_of_turn` tags, so it now points at `get-started/prompt-structure.md`.
-- **Verify:** build warnings **19 → 5**; all four pipeline stages idempotent.
-
-**5 warnings remain, all deliberate:**
-
-| Warning | Status |
-|---|---|
-| `deployment/keras.md` ×2 | planned page — Step 2.1 decides whether "Run > Local" gains Keras |
-| `quickstart.md#models-list` | section removed during your hand-editing |
-| `paligemma/index.md#categories` | **broken upstream too** |
-| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
-
-The last three are left alone on purpose: picking a target is an editorial decision, and the
-dangling anchor is a useful signal that a section is genuinely missing. **Needs a content call.**
-
-!!! warning "The 9 new pages are not in the nav"
-    `orphans` rose 4 → 13. That is expected — Step 2.1 places them. Do not "fix" it by
-    deleting pages.
-
-### Step 0.6 — Model capability matrix as single source of truth ✅
-
-- [x] `data/models.yml` — every Gemma 4 size with parameters, architecture, modalities in/out,
-      context window, memory at each quantization, and target platform.
-- [x] Wired into `zensical.toml` via the macros plugin's `include_yaml`, exposed as `models.*`.
-- [x] `quickstart.md` and `models/index.md` tables **generated** from it.
-- [x] New `modeldata` lint check guarding the one table that can't be generated (below).
-- **Verify:** ✅ changed `context: 128K` → `TESTVALUE`, rebuilt, both tables updated; reverted,
-      both reverted. Drift check verified by injecting a wrong figure — caught it.
-
-**The modality contradiction is resolved.** Confirmed by Maarten (2026-08-03):
-
-| Model | Input | Note |
-|---|---|---|
-| E2B, E4B, 12B | Text, Image, Audio, Video | full multimodal |
-| 26B A4B, 31B | Text, Image, Video | **no audio**; video is processed as individual frames |
-
-This matches `models/gemma-4/index.md:45` ("Audio... featured natively on the E2B, E4B and 12B
-models"). The stale source was the `quickstart.md` table, which omitted video entirely and is
-now generated.
-
-!!! warning "One table is deliberately *not* generated"
-    `models/gemma-4/index.md` is a straight port (`scripts/sources.toml`), so a template there
-    would be wiped by the next `port_docs.py` run. Its memory figures were copied into
-    `data/models.yml` instead, and the new **`modeldata`** check compares the two — so if
-    upstream revises a number, it surfaces as a finding rather than a silent contradiction.
-
-```bash
-uv run python scripts/lint_docs.py --check modeldata
-```
-
----
-
-## Phase 0 complete — 300 → 136
-
-| Check | Start | Now | |
-|---|---|---|---|
-| `links` | 122 | **0** | ✅ |
-| `images` | 52 | **0** | ✅ |
-| `fences` | 7 | **0** | ✅ |
-| `size` | 1 | **0** | ✅ |
-| `admonitions` | 0 | **0** | ✅ |
-| `modeldata` | — | **0** | ✅ new check |
-| `internal` | 0 | 2 | `deployment/keras.md` — Step 2.1 decision |
-| `stubs` | 33 | 31 | → Steps 2.2–2.4 |
-| `orphans` | 4 | 13 | → Step 2.1 (9 newly ported pages await nav) |
-| `frontmatter` | 81 | 90 | → Step 3.1 |
-
-**Everything remaining is scheduled work, not defects.** The three checks that still have
-findings are all waiting on Phase 2 (nav, content) or Phase 3 (frontmatter schema).
-
-### Open decision for Step 2.1
-
-Whether `docs/models/` should mirror the `get-started/capabilities/` pattern with
-`core/`, `variants/`, `research/` subdirectories.
-
-**Recommendation: no.** Two of the four nav groups are time-dependent — when Gemma 5 ships,
-Gemma 4 moves from *Gemma 4* to *Previous generations*, and every Gemma 4 URL would break at
-the worst possible moment. Classification is a presentation concern; identity is `gemma-4`.
-The nav already groups them (`zensical.toml` lines 40, 49, 88) at zero URL cost, and
-`models/index.md` is the grouped hub — it just needs wiring up in Step 2.1.
-
-If done anyway: land it *during* Step 2.1 (nav is being rewritten regardless) and *before*
-Step 3.1 adds `source_url` frontmatter, as one reviewable pure-move commit with redirects.
-Cost is ~195 path updates (39 files, 90 outbound links, 61 inbound, 44 asset refs).
-
-- [ ] Create `data/models.yml`: every Gemma 4 size with parameters, modalities in/out, context
-      window, memory at each quantization, MTP support, target platform.
-- [ ] Resolve the modality contradiction (`quickstart.md` vs `models/index.md:14` vs
-      `models/gemma-4/index.md:45`) — confirm 26B A4B and 31B are text+image only.
-- [ ] Generate the model tables in `quickstart.md`, `models/index.md`, and
-      `models/gemma-4/index.md` from this file via the macros plugin.
-- **Verify:** change one value in `data/models.yml`, rebuild, confirm all three tables update.
-- **Done when:** no hand-maintained model spec table remains anywhere.
-
----
-
-## Phase 1 — Design system
-
-### Step 1.1 — Token layer ✅
-
-- [x] `docs/stylesheets/gemma.css` — palette, spacing (1–7), radius (sm/md/lg/pill),
-      shadow (sm/md/lg), chat-token categories, all with dark-scheme variants.
-- [x] Material's `--md-*` primary/accent/link variables bound to Gemma tokens, so component
-      sheets never name a brand colour.
-- [x] `content.css`, `tokens.css`, and the landing-page stylesheet consume tokens.
-- [x] `cookbook.css` folded into `components.css` as a generalised card grid
-      (`.cookbook-*` kept as aliases — converter output and existing pages emit them).
-- [x] New **`css`** lint check: raw hex only in `gemma.css`, every sheet registered in
-      `extra_css`, none over 250 lines.
-- **Verify:** `uv run python scripts/lint_docs.py --check css` → zero.
-- **Done when:** ✅ zero hex outside `gemma.css`; every sheet under 250 lines.
-
-**Contrast drove a real decision.** Measured against the palette:
-
-| Pairing | Ratio | |
-|---|---|---|
-| `#3186FF` on white | 3.51:1 | **fails AA for body text** |
-| `#346BF1` on white | 4.65:1 | passes |
-| `#4FA0FF` on ink | 5.98:1 | passes |
-
-So `--gemma-blue` is a **fill** colour — buttons, bars, icons, step markers — and never body
-text. Text uses `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in
-dark. The header uses `blue-deep` for the same reason.
-
-**Stylesheet layout** (8 files, each under 250 lines):
+**Stylesheets** — 9 files, each under 250 lines, each opening with what it owns and what it must
+not touch. Adding a ninth is the right move when a new concern appears; growing one into a
+catch-all is not.
 
 | File | Owns |
 |---|---|
@@ -626,401 +200,206 @@ dark. The header uses `blue-deep` for the same reason.
 | `nav.css` | Header, tabs, sidebars, table of contents |
 | `content.css` | Tables, admonitions, code, content tabs |
 | `components.css` | Cards, badges, figures, steps, embeds |
+| `page-context.css` | The applicability bar above every H1 |
 | `tokens.css` | Chat-token badges |
-| `home.css` | Landing page: chrome, backdrop, hero |
+| `home.css` | Landing page |
 | `motion.css` | Transitions + reduced-motion opt-out |
 
-The homepage is deliberately one concern again: obsolete styles for an earlier DOM were removed,
-while the live template styles moved into the registered, linted `home.css`.
+Enforced by `lint_docs.py --check css`: raw hex only in `gemma.css`, every sheet registered in
+`extra_css`, none over 250 lines, no `<style>` blocks in templates.
 
-### Step 1.2 — Component library ✅
+Live catalogue with copy-paste markup: `docs/contributing/components.md`.
+
+---
 
-- [x] Card grid, model badges (`--accent`, `--muted`), framed figures with light/dark image
-      swap, numbered steps with a connecting rail, video embeds, model-card header.
-- [x] `docs/contributing/components.md` — every component with a **live example** plus the
-      markup to copy, the colour-token table with contrast guidance, and the stylesheet map.
-      Added to nav under *Contributing*.
-- **Verify:** the components page renders every component in both themes.
+## Frontmatter schema
 
-`.badge--muted` exists for the Step 4.2 switcher: a capability the selected model lacks is
-struck through rather than hidden, so the reader learns the limitation instead of it silently
-vanishing.
+Defined in `scripts/frontmatter_schema.py` — the single source of truth, imported by both the
+linter and the backfill script so they cannot disagree. Validated by
+`lint_docs.py --check frontmatter`.
 
-### Step 1.3 — Motion ✅
+**Required on every page:**
 
-- [x] `motion.css` owns shared transitions and the site-wide reduced-motion opt-out. The isolated
-      homepage text animation checks the same preference before starting.
-- [x] Blanket reduced-motion rule; anything added later inherits it.
-- [x] Focus rings never animate — keyboard users need them immediately.
-- **Verify:** enable *Reduce motion* in OS settings, reload, confirm hover effects are instant.
+| Key | Type | Notes |
+|---|---|---|
+| `title` | string | See *title is sometimes content* below |
+| `description` | string | One sentence, 40–200 chars. Feeds llms.txt, search, page cards. |
 
-Motion is feedback (something is interactive, something changed), never decoration.
+**Optional, validated when present:**
 
----
+| Key | Type | Notes |
+|---|---|---|
+| `models` | list | Models the page is about. **Omit when it is about none.** |
+| `requires` | string | One input modality, narrowing `models` to the sizes that support it |
+| `frameworks` | list | Engines/libraries the page shows code for |
+| `prerequisites` | list | docs-relative paths; must resolve to real pages |
+| `name` | string | Stable kebab-case id for agents to reference |
+| `source_url` | string | Upstream page, from `scripts/sources.toml` |
 
-### Step 1.1 — Token layer (original spec)
+**Reserved** for the Step 3.2 build hook and rejected if hand-written: `estimated_tokens`,
+`code_languages`, `last_updated`. Theme keys (`template`, `hide`, `cards`, …) pass through.
 
-**Stylesheets stay modular — many small files, not one monolith.** Each file owns one concern,
-opens with a comment saying what it owns and what it must not touch, and stays small enough for
-an agent to read end-to-end before editing. Adding a seventh or eighth stylesheet is the right
-move whenever a new concern appears; growing `custom.css` into a 900-line catch-all is not.
+### Authored vs computed
 
-Target layout (`docs/stylesheets/`):
+A key is either **authored** (only a person knows it) or **computed** (derivable from the file),
+never both. Hand-writing a token count guarantees it is wrong the next time anyone edits the
+page, so authoring a computed key is a lint *error*, not a style preference.
 
-| File | Owns |
-|---|---|
-| `gemma.css` | Palette + spacing/radius/shadow scale. **The only file with raw hex values.** |
-| `layout.css` | Page grid, sidebars, content width |
-| `components.css` | Cards, callouts, badges, figures, walkthroughs |
-| `code.css` | Code blocks, copy buttons, tabs |
-| `tokens.css` | Chat-token badges (`<start_of_turn>` etc.) |
-| `home.css` | Landing page only |
-| `motion.css` | Animations + `prefers-reduced-motion` |
-
-- [ ] Create `gemma.css` with the palette above as CSS custom properties, light and dark
-      variants, and the spacing/radius/shadow scale.
-- [ ] Rebind Zensical's `--md-*` primary/accent variables to the Gemma tokens.
-- [ ] Refactor the existing sheets to consume tokens — remove hardcoded hex values.
-- [ ] Resolve `custom.css` (currently not in `extra_css`): fold into `layout.css` or delete.
-- [ ] Keep `extra_css` in `zensical.toml` in the same order as the table above — token layer first.
-- **Verify:** visual pass on 3 pages in both light and dark mode.
-- **Done when:** `grep -E '#[0-9a-fA-F]{6}' docs/stylesheets/*.css` only matches `gemma.css`,
-  and no stylesheet exceeds ~250 lines.
-
-### Step 1.2 — Standard component library
-- [ ] Card grid (generalize the existing `cookbook-card`).
-- [ ] Callout/box variants: note, tip, warning, "requires GPU", "Gemma 4 only".
-- [ ] Model badge chips (size, modality, quantization) driven by `data/models.yml`.
-- [ ] Framed figure component with caption and light/dark image swap.
-- [ ] Step-by-step / numbered walkthrough component.
-- [ ] Document every component in `docs/contributing/components.md` with live examples.
-- **Verify:** the components page renders all variants correctly in both themes.
-- **Done when:** new pages can be built from components without writing new CSS.
-
-### Step 1.3 — Motion
-- [ ] Standardized entrance/hover animations, `prefers-reduced-motion` respected throughout.
-- [ ] Apply consistently to cards, callouts, and nav.
-- **Verify:** enable reduced-motion in the OS; confirm animations disable.
+### Vocabularies
 
----
+Both are read from data files, so adding an entry there is all that is needed:
 
-## Phase 2 — Navigation & content completion
+- **`models`** — `family` or `family/size`. Families come from the `docs/models/` directories;
+  Gemma 4 sizes come from `data/models.yml`. A bare `12b` is rejected: Gemma 3 and Gemma 4 both
+  ship a 12B, so the family is required. Only Gemma 4 has per-size data today; other families
+  are addressable at family level until someone adds sizes for them.
 
-### Step 2.1 — Restructure nav ✅
+    ```yaml
+    models: [gemma-4]                                  # the whole family
+    models: [gemma-4/e2b, gemma-4/e4b, gemma-4/12b]    # the audio-capable sizes
+    models: [embeddinggemma]                           # a variant
+    ```
 
-- [x] Nav rewritten to the 8-tab target: **Home · Get started · Models · Run · Fine-tune ·
-      Learn · Demos · Ecosystem**.
-- [x] **All 13 orphans resolved.** `orphans` 13 → **0**.
-- [x] Cookbook folded into *Learn*; the old Tools tab folded into *Demos* as
-      "Interactive tools"; `contributing/components.md` placed under *Ecosystem → Contribute*.
-- **Verify:** `uv run python scripts/lint_docs.py --check orphans` → zero.
-- **Done when:** ✅ zero orphans, zero broken nav entries, 8 top-level tabs confirmed in the
-      rendered header.
+    **Omitting `models` means the page is not about a model** — community, tooling and site
+    pages. There is no "applies to everything" wildcard: that and "applies to nothing" were
+    being written identically, which made the key useless for the page context header.
 
-How each orphan was resolved:
+    The audio page's list is generated from the `modalities` field rather than typed out, so it
+    cannot drift from the model data.
 
-| Orphan | Resolution |
-|---|---|
-| `get-started/index.md` | **Deleted** — a strict subset of `quickstart.md` with zero inbound links |
-| `capabilities/vision.md` | **Ported** from upstream (was a stub); now the parent of Image/Video |
-| `deployment/keras.md` | **Ported** — clears the last 2 `internal` findings and fills *Run → Local* |
-| `models/index.md`, `tools/index.md` | placed as section landing pages |
-| the 9 pages from Step 0.5b | placed in Run / Fine-tune / Learn / Models |
+- **`frameworks`** — ids from `data/frameworks.yml`, which also carries display names,
+  categories and the docs page for each. A controlled vocabulary because the Step 4.2 switcher
+  cannot offer a free-text value, and `transformers` / `huggingface` / `hf` spread across 89
+  pages would make the feature unbuildable.
 
-**Directory question settled: `models/` stays flat.** Grouping is nav-only, exactly so a
-model's URL survives reclassification — when Gemma 5 ships and Gemma 4 moves to *Previous
-generations*, no URL changes. The rationale is recorded in the nav file itself.
+### `title` is sometimes content
 
-**One more converter fix:** consecutive `{ .no-copy .output }` blocks separated only by blank
-lines are now merged. Upstream splits a single stream of terminal output across several fences
-(Keras `.summary()` prints a title then a table), which rendered as two separate result boxes.
-Only *output* blocks are merged — two adjacent code blocks may legitimately be separate cells.
+Zensical synthesises an H1 from `title` (`<h1 id="__skip">`) **only when the page has no H1 of
+its own**; it never replaces an existing heading. That gives the key two roles:
 
-### Phase 2 progress: 136 → 120
+- **Page has an H1** — `title` reaches only the browser tab and llms.txt, and may be *more*
+  specific than the heading. This is why nine pages headed "Model card" carry titles like
+  `EmbeddingGemma model card`: the heading on screen is untouched, the tab and the
+  machine-readable index become useful.
+- **Page has no H1** — `title` *is* the visible heading and must say what the page already said.
+  Five pages are in this state: `deployment/index.md`, `deployment/google-cloud.md`,
+  `models/gemma-4/model-card.md`, `models/diffusiongemma/inference.md`, `models/releases.md`.
+  The linter requires `title` on any page lacking an H1.
 
-| Check | Now | |
-|---|---|---|
-| `links` `images` `internal` `fences` `admonitions` `size` `modeldata` `css` `orphans` | **0** | ✅ all clear |
-| `stubs` | 30 | → Steps 2.2–2.4 |
-| `frontmatter` | 90 | → Step 3.1 |
-
-**Only two checks still have findings**, and both are scheduled work. Build warnings: 5 → **3**.
-
-### Step 2.1 — Restructure nav (original spec)
-- [ ] Rewrite the `nav` block in `zensical.toml` to the target structure above.
-- [ ] Resolve the 4 orphans: fold `get-started/index.md` into `quickstart.md`,
-      make `capabilities/vision.md` a real parent page, add `models/index.md` and
-      `tools/index.md` to nav.
-- **Verify:** `uv run python scripts/lint_docs.py --check orphans`
-- **Done when:** zero orphans, zero broken nav entries, 8 top-level tabs.
-
-### Step 2.2 — Local engine pages (the differentiator)
-
-These are *written*, not ported — upstream's versions are three paragraphs each. One step per
-engine so they can be tested and handed over independently.
-
-**These are quick references, not manuals** (decided 2026-08-03). Users want working code
-fast, and a short page is a page that stays correct. Roughly 100 lines each.
-
-Template — in this order, nothing else:
-
-1. **Install** — the one command per platform
-2. **Run** — the one command that works
-3. **Model tags / repositories** — a table, every Gemma 4 size
-4. **Multimodal** — only where it differs from plain text, with a note when a size lacks a modality
-5. **Server** — the OpenAI-compatible snippet
-
-Deliberately **not** included: context-length deep dives, troubleshooting tables, importing
-your own fine-tunes, "next steps" link lists. Each is either engine documentation that we would
-have to keep in sync, or a link the sidebar already provides. A single `!!! tip` is allowed
-where a default will actively surprise someone (Ollama's 4K context, for instance).
-
-- [x] 2.2a Ollama ✅ *(written)*
-- [x] 2.2b llama.cpp ✅ *(written)*
-- [x] 2.2c LM Studio ✅ *(written)*
-- [x] 2.2d MLX ✅ *(written)*
-- [x] 2.2e Transformers ✅ *(ported — upstream `core/huggingface_inference` is already complete)*
-- [x] 2.2f vLLM ✅ *(written)*
-- [x] 2.2g SGLang ✅ *(written)*
-- [x] 2.2h LiteRT / mobile / WebGPU ✅ *(ported — upstream pages are already concise link hubs)*
-- [x] 2.2i Google Cloud ✅ *(ported)*
-- [x] Also filled: Keras, LangChain, Quantization & QAT. **The Run section has zero stubs.**
-
-**Nav labels are short, page titles are not** (2026-08-03). The sidebar shows "Hugging Face
-Transformers", "LangChain", "Keras"; the page H1s stay as ported ("Run Gemma with Hugging Face
-Transformers"). Labels live in `zensical.toml`, so no ported prose is touched.
-
-**`mobile-actions.md` moved `deployment/` → `tuning/`.** Its H1 is "Fine-tune FunctionGemma 270M
-for Mobile Actions" — a fine-tuning guide that was sitting under Run.
-- [ ] 2.2c LM Studio
-- [ ] 2.2d MLX
-- [ ] 2.2e Transformers
-- [ ] 2.2f vLLM
-- [ ] 2.2g SGLang
-- [ ] 2.2h LiteRT / mobile / WebGPU
-- [ ] 2.2i Google Cloud (GKE, Cloud Run)
-- **Verify per page:** every command runs as written on a clean machine.
-
-**Sourcing rule (2026-08-03).** Every command, flag and model identifier on these pages is
-taken from a primary source — the engine's own repository docs, its published model registry,
-or the upstream Gemma docs. Nothing is written from memory. Where a claim can't be verified it
-is omitted rather than guessed.
-
-Verified sources used so far:
-
-| Page | Sources |
-|---|---|
-| Ollama | `ollama.com/library/gemma4/tags` (tags, sizes, context) · `ollama/ollama` `docs/api`, `docs/api/openai-compatibility.mdx`, `docs/capabilities/vision.mdx`, `docs/context-length.mdx` · upstream `/integrations/ollama` |
-| llama.cpp | `ggml-org/llama.cpp` `docs/install.md`, `docs/multimodal.md` · `huggingface.co/ggml-org` (5 GGUF repos) · `huggingface.co/google` (5 QAT GGUF repos) · upstream `/integrations/llamacpp` |
-
-**Throughput numbers are deliberately absent.** Measured tok/s can't be sourced from
-documentation, and inventing it would be worse than omitting it. The pages give the verifiable
-performance levers instead — GPU-offload checks, context defaults, quantization choice. Add real
-figures when someone can measure them.
-
-**Unresolved discrepancy — Ollama download sizes.** `ollama.com/library/gemma4/tags` states
-E2B 7.2 GB, E4B 9.6 GB, **12B 7.6 GB**, 26B 18 GB, 31B 20 GB. That makes E4B *larger* than 12B,
-which contradicts the validated Q4_0 column in `models/gemma-4/index.md` (E4B 4.5 GB,
-12B 6.7 GB — monotonic, as expected). Verified verbatim from the tags page twice.
-
-Most likely the default tag for each size uses a different quantization, but that is a guess and
-**no explanation appears on the page**. An earlier draft rationalised it as a Per-Layer
-Embeddings effect; that was wrong and was removed. If someone can confirm the per-tag quant
-levels, add them to the table — otherwise leave the figures unexplained.
-
-Two corrections to upstream found while writing:
-
-- Upstream's Ollama page lists "four sizes: E2B, E4B, 26B and 31B" and **omits 12B**, which the
-  Ollama library does publish.
-- **Ollama exposes Gemma 4 as text + image only** — no audio — while llama.cpp supports image,
-  audio *and* video. Neither page upstream mentions this, and it's the kind of thing that costs
-  someone an afternoon.
-
-
-- **Done when:** the page is genuinely more useful than the upstream equivalent.
-
-### Step 2.3 — Fill remaining stubs (in progress)
-
-- [x] **Fine-tune section ported** — all six stubs filled from upstream: `tune`,
-      `huggingface_text_finetune_qlora`, `huggingface_text_full_finetune`,
-      `huggingface_vision_finetune_qlora`, `lora_tuning`, `distributed_tuning`.
-      These are validated tutorials, so porting beats authoring.
-
-**`slugify` bug fixed.** `rewrite_links.py` stripped parenthesised text, so
-"Google Kubernetes Engine (GKE)" produced `google-kubernetes-engine` while Python-Markdown
-emits `google-kubernetes-engine-gke`. Anchors computed from it silently never matched. It now
-mirrors the real algorithm: reduce markdown to visible text, then drop non-word characters —
-parentheses contribute their contents, they are not removed.
-
-### Step 2.3 — Fill remaining stubs (original spec)
-- [ ] Fine-tune pages (QLoRA, full FT, vision QLoRA, Keras LoRA, distributed).
-- [ ] Quantization & QAT page — move the QAT content currently buried in
-      `models/gemma-4/index.md` into its own page under Run.
-- [ ] Multi-Token Prediction page — port `mtp/overview` + `mtp/mtp`. **Biggest content gap:**
-      MTP is a flagship Gemma 4 feature with zero documentation today.
-- [ ] Ecosystem pages — replace the `example.com` placeholders in `gemmaverse.md` with real
-      data-driven cards.
-- **Verify:** `uv run python scripts/lint_docs.py --check stubs` → zero.
-
-### Step 2.4 — Port remaining upstream pages
-- [x] ~~CodeGemma~~ — **not being ported** (decision: 2026-08-03). Existing references are
-      delinked, keeping the text. Do not add it back without a new decision.
-- [ ] `setup`, `run`, `gemma_chat`, `gemma_library`, `keras_inference`, `pytorch_gemma`,
-      `huggingface_inference`, `lora_tuning`, `prompt-structure`.
-- [ ] Application tutorials → Learn: `business-email-assistant`, `personal-code-assistant`,
-      `meme-generator`.
-- [ ] Decide case-by-case on: `gemma-aps`, `spoken-language/task-specific-tuning`,
-      `conversions/hf-to-mediapipe-task`, `mobile-actions`.
+Giving those five a real H1 would be a content change, so it is Maarten's call, not the port's.
 
 ---
 
-## Page context header (decided 2026-08-03, build after Step 3.1)
+## Page context header — **done 2026-08-05**
 
 A Stripe-style bar at the top of **every page** — see <https://docs.stripe.com/billing/scripts>.
 Not silence-by-default: the slot is always present, so readers learn where to look.
 
 It carries three things, in this order of arrival:
 
-1. **Applicability** — which model(s) the page is about. Three states, resolved from frontmatter
-   against `data/models.yml`:
+1. **Applicability** — ✅ built.
+2. **LLM tools** (Step 3.4) — Copy as Markdown, Open in Claude/ChatGPT, View raw.
+3. **Model + engine selector** (Step 4.2) — page-scoped context belongs here, not site chrome.
 
-    | State | Frontmatter | Shows |
-    |---|---|---|
-    | Universal | *(omitted)* | "All Gemma models" |
-    | Version-scoped | `models: gemma-3` | `Gemma 3` |
-    | Capability-scoped | `models: [e2b, e4b, 12b]` | `E2B · E4B · 12B` |
+- [x] `overrides/main.html` renders the bar on every page, inside `.md-content__inner` so it
+      shares one left edge with the breadcrumb, H1 and body.
+- [x] Applicability badges from `models`, display names from `data/models.yml`.
+- [x] Empty state holds its height, so the breadcrumb→H1 distance never changes between pages.
+- **Verified:** audio shows `Gemma 4 · E2B E4B 12B`, discord shows nothing, both 28px tall with
+  a 16px gap to the H1; all four left edges land on 304px; contrast passes AA in both schemes
+  (light 4.68/6.73/9.00, dark 6.64/9.25/12.12).
+- **Done when:** ✅ adding a size to `data/models.yml` changed the audio page without the page
+  being touched — confirmed with a throwaway `7b-probe` size, then reverted.
 
-2. **LLM tools** (Step 3.4) — Copy as Markdown, Open in Claude/ChatGPT, View raw.
-3. **Model + engine selector** (Step 4.2) — page-scoped context belongs here, not in site chrome.
-
-All three read `data/models.yml`, so the header, the tables, the VRAM calculator and the
-switcher cannot drift apart.
-
-**Open interaction question.** With 31B selected, on the audio page (E2B/E4B/12B only):
-passive (badges show 31B struck through, via `.badge--muted`), active (an inline note naming
-the substitution), or silent swap. Leaning passive, escalating to active only where the code
-samples would otherwise be wrong. Silent swap is rejected — the reader ends up with code for a
-model they did not choose.
-
-**Sequencing:** Step 3.1 (frontmatter schema, `models:` key) → static header with applicability
-→ 3.4 tools into the same bar → 4.2 selector into the same bar.
-
-### Step 1.4 — Stripe-inspired refinement ✅
-
-Measured against `docs.stripe.com` rather than eyeballed. What actually differed:
-
-| | Stripe | Was | Now |
-|---|---|---|---|
-| Content width | 800px | **1600px** | **880px** |
-| Body size | 14px | 15.5px | **14px** |
-| Line height | 1.43 | 1.7 | 1.65 |
-| Neutral ramp | 13 steps | none | **12 steps**, blue-tinted |
-| Brand use | accent only | **full header** | accent only |
-
-- [x] **Layout reshaped.** Sidebar sits flush left; the *content* is centred in the space that
-      remains, rather than stretching to fill it. That is the single biggest change — at 1600px
-      lines ran to ~180 characters.
-- [x] Pages that hide the table of contents can use the freed rail without changing the regular
-      article's left edge (the 7-column Gemma 4 table fits at the regular 880px measure).
-- [x] **Header is a near-white tint** (`--gemma-n50`), not a slab of brand blue. Blue appears
-      once: the active tab, as colour + a 2px rule.
-- [x] New `nav.css` (chrome) and `content.css` (tables, admonitions, collapsibles, inline code,
-      content tabs) — Stripe-inspired, tinted surfaces over heavy borders.
-
-**Four bugs found only because the result was measured in a browser:**
-
-1. **Body text was 18px, not 14px.** Material ships `html { font-size: 125% }`, so every `rem`
-   in the file was 25% larger than it read. Anchored to 100%.
-2. **The active-tab rule never matched.** Zensical marks the active tab on the `<li>`
-   (`md-tabs__item--active`), not the `<a>`.
-3. **Material's palette outranks a plain scheme selector.** It scopes at
-   `[data-md-color-scheme=X][data-md-color-primary=Y]` (0,2,0); ours was (0,1,0) and lost
-   silently regardless of load order. Dark-mode links and inline code stayed light.
-4. **Admonition tints outranked the base rule** at `.md-typeset .admonition.note` (0,3,1).
-
-**Layout shape** (verified at 1366px and 2000px):
+### Applicability is declared two ways
+
+| Frontmatter | Shows |
+|---|---|
+| *(no `models`)* | nothing — the slot stays empty |
+| `models: [gemma-3]` | `Gemma 3` |
+| `models: [gemma-4/e2b, gemma-4/e4b]` | `Gemma 4 · E2B E4B` |
+| `models: [gemma-4]` + `requires: audio` | `Gemma 4 · E2B E4B 12B`, resolved at build time |
+
+**Prefer `requires` whenever the set *is* "the models that can do X".** A listed set is a copy
+of the model data and goes stale the day a new size ships; a derived one cannot. `requires`
+takes one input modality, validated against `data/models.yml`, and narrows the family named in
+`models` — so it needs `models` to be present.
+
+This was the gap that testing the "done when" exposed: the audio page's three sizes had been
+written into its frontmatter by the Step 3.1 backfill, which is generation at *authoring* time,
+not build time. It looked data-driven and wasn't.
+
+### Design
+
+Measured off `docs.stripe.com`, not approximated. Their page toolbar is `display:flex`,
+`gap:16px`, `align-items:baseline`, `padding-bottom:12px`, a 1px hairline rule beneath, and
+between items a `<span role="separator">` **1px wide with `align-self: stretch`** in a tone
+deliberately darker than that rule. Nothing sits above the H1 at all.
 
 ```
-sidebar 17rem │  ←auto→  │ content 56rem │ toc 17rem │  ←auto→
-   flush left            └── centred as a pair, TOC follows the text ──┘
+Models  Gemma 4  │  Uses  Ollama  │  Examples  Gemma 4 12B ⌄
+──────────────────────────────────────────────────────────────
 ```
 
-The sidebar stays flush left at every width. Content and the table of contents are centred
-*as a pair* via auto margins on the pair's outer edges — capping and centring all three
-columns instead pushes the sidebar inward, which a docs rail should not do.
+Stripe's toolbar items are peer actions at one weight. Ours are label→value pairs, a structure
+they never have to solve — so two rules do the work their flat treatment cannot:
 
-**`hide: toc` pages get their own rule.** Material still renders the sidebar element, just
-`hidden`, so the pair's `margin-right: auto` lands on a zero-width box and every pixel of
-slack piles up on the left, shunting the column right. Those pages centre on their own and
-widen to 73rem — hiding the table of contents is usually a request for a wider page.
+1. **Spacing is hierarchical.** 6px inside a pair, 16px between pairs. An 8px/16px ratio was
+   too weak and the whole line read as one run of grey.
+2. **Every pair gets the same label/value contrast.** Emphasising the model value but not the
+   framework values made `Frameworks` and `Sentence Transformers` indistinguishable.
 
-Padding lives on `.md-content`, not `.md-content__inner`: the breadcrumb is a **sibling** of
-`__inner`, so padding there left the two misaligned by 77px. Breadcrumb, `h1` and body text
-now share one left edge.
+Model names link to their local canonical page. Tool names link to the project home. Both stay
+neutral-coloured; the selector is the only control in the strip.
 
-**Contrast verified in the live page, both schemes — everything passes AA:**
-
-| | Light | Dark |
-|---|---|---|
-| Body text | 21.0 | 15.4 |
-| Links | 4.65 | 7.27 |
-| Active tab | 4.92 | — |
-| Table header / muted | 6.38 | 8.39 |
+### Subject vs. selection
 
-Two tokens exist purely to hold that line: `--gemma-text-muted` uses `n600` (n500 measured
-4.46 on the tint), and `--gemma-accent-on-tint` (`#2F63E6`) carries small brand-coloured text
-on tinted surfaces, where plain `blue-deep` measures 4.41.
+The strip carries two different kinds of thing, and conflating them was the original design
+error:
 
-## Phase 2 status — not finished
+| Item | Source | Answers |
+|---|---|---|
+| `Models` | `models:` frontmatter | what the page is **about** |
+| `Uses` | `frameworks:` frontmatter | what it uses |
+| `Examples` | the page's marked code blocks | what the **reader** wants the commands to target |
 
-| Step | State |
-|---|---|
-| 2.1 Restructure nav | ✅ 8 tabs, zero orphans |
-| 2.2 Local engine pages | ✅ Run section has zero stubs |
-| 2.3 Fill remaining stubs | **7 left** — see below |
-| 2.4 Port remaining upstream pages | **6 left** — see below |
+Driving the selector from `models:` looked reasonable and was wrong: the Ollama page is about
+Gemma 4, but its commands could target anything Ollama publishes, and Quick Start ended up
+claiming `E2B` on a page that covers all five sizes. The selector is now built in JavaScript
+from the identifiers actually present in the page's `.model-swap` blocks:
 
-**Remaining stubs (7):**
+- a model must have a published id for **every** engine on the page, so a switch cannot rewrite
+  some snippets and leave others behind;
+- `requires:` narrows it further, so an audio page can never offer a model without ears;
+- with fewer than two candidates there is no control at all.
 
-| Page | Blocked on |
-|---|---|
-| `community/gemmaverse.md` | real project data (still ships `example.com` cards) |
-| `community/discord.md` | port `/discord` |
-| `community/gemma-skills.md` | content — no upstream source |
-| `cookbook.md` | content decision |
-| `demos/concurrent.md` | content — the app lives in the cookbook repo |
-| `tools/index.md`, `tools/model-selector.md` | **Phase 4** — these are the interactive tools |
+This also gives the extension point for later generations: add Gemma 3's Ollama tags to
+`data/models.yml` and it appears in the Run pages' selector with no template change.
 
-**Upstream pages not ported (6):** `/core/gemma_library`, `/core/pytorch_gemma`,
-`/core/gemma_on_gemini_api`, `/core/deploy_to_cloud_run_from_ai_studio`, `/gemma_chat`,
-`/meme-generator`. Each needs a keep-or-drop decision rather than a straight port —
-`/meme-generator` in particular is a demo app, not reference material.
+### How the data reaches the template
 
-Deliberately not ported and **not** gaps: `/integrations/{ollama,llamacpp,lmstudio,mlx}`
-(written as quick references instead), `/core/gke` (folded into Google Cloud),
-`/codegemma/*` (dropped by decision).
+Zensical renders with **minijinja**, exposes `page.meta` to templates but *not* the macros
+plugin's YAML, and has no hook mechanism to inject any. So `data/models.yml` is compiled into
+`overrides/partials/model-names.html` by `scripts/generate_model_names.py`, and
+`lint_docs.py --check generated` fails if the two drift apart. Committed build output, verified
+in CI — the same arrangement as `.lintbaseline.json`.
 
-**Provenance gap.** Pages ported before `sources.toml` existed — `get-started/capabilities/*`,
-`prompt-formatting.md`, `quickstart.md` — have no `source` line. They are protected by absence
-(nothing can re-convert them), but Step 3.1's `source_url` frontmatter will need those URLs
-adding to the manifest with `frozen = true`.
+Writing template logic here: minijinja has no `.get()` and no Python string methods. Use
+`dict[key] | default(...)`, the `split` filter, and the `startingwith` test.
 
-## Phase 3 — Agent-first infrastructure
+**Interaction decision.** The picker is local to pages with swappable examples, but its full
+model choice is remembered globally and mirrored in `?model=` for shareable URLs. Code blocks
+opt in with `.model-swap`; there are no competing per-block selectors. If the remembered model
+is unavailable, the authored page default remains active and the menu names the incompatibility.
+Silent substitution is rejected.
 
-*The headline requirement from the original plan, currently ~11% built.*
+---
 
-### Step 3.1 — Frontmatter schema
-- [ ] Define and document the schema: `name`, `title`, `description` (richer than llms.txt),
-      `estimated_tokens`, `prerequisites`, `frameworks`, `code_languages`, `models`, `last_updated`,
-      plus `source_url` and `ported_at` on every ported page (see *Content fidelity*) so any page
-      can be diffed against upstream later.
-- [ ] Backfill all 80 pages.
-- [ ] Enforce in `scripts/lint_docs.py`.
-- **Verify:** linter passes with the frontmatter check enabled.
+## Phase 3 — Agent-first infrastructure (3.1 ✅)
 
 ### Step 3.2 — Computed metadata
 - [ ] Build hook computing `estimated_tokens`, `code_languages`, and `last_updated`
-      automatically so they can't drift.
+      automatically so they cannot drift.
 - **Verify:** add a code block to a page, rebuild, confirm metadata updates.
 
 ### Step 3.3 — Machine-readable surfaces
@@ -1028,14 +407,18 @@ adding to the manifest with `frozen = true`.
 - [ ] Serve raw markdown at `<page>.md` for every page.
 - **Verify:** `curl` the built site for `llms.txt` and one `.md` endpoint.
 
+Compose entry labels from `title` + `models` rather than adding a second title key —
+`models: [gemma-4]` plus `title: Model card` gives "Gemma 4 — Model card" without touching the
+page. Every input this needs already exists.
+
 ### Step 3.4 — Copy-for-agent affordances
-- [ ] "Copy page as Markdown" button in the page header.
+- [ ] "Copy page as Markdown" button, in the page context header.
 - [ ] "Open in Gemini / Claude / ChatGPT" links.
 - **Verify:** click the button, paste, confirm clean markdown with frontmatter.
 
 ### Step 3.5 — Docs as a skill
-- [ ] Package the docs as an installable skill (`npx skills add ...`) and/or MCP server so
-      agents can query them directly.
+- [ ] Package the docs as an installable skill and/or MCP server so agents can query them
+      directly.
 
 ---
 
@@ -1048,26 +431,34 @@ Replace the static table in `docs/tools/vram-calculator.md` with a real calculat
       (4090, 5090, A100, H100, M-series unified memory, …).
 - [ ] Outputs: fits / doesn't fit with headroom, estimated tok/s, recommended engine,
       **and the exact copy-paste command for that engine**.
-- [ ] URL-encoded state (`?model=12b&quant=q4_0&gpu=rtx4090&ctx=32k`) so results are shareable.
+- [ ] URL-encoded state (`?model=gemma-4/12b&quant=q4_0&gpu=rtx4090&ctx=32k`) so results are
+      shareable.
 - [ ] Reads from `data/models.yml`.
 - **Verify:** results match the memory table in `models/gemma-4/index.md` at matching settings.
 - **Done when:** a shared URL reproduces the exact same state.
 
-### Step 4.2 — Global engine + model switcher
+### Step 4.2 — Global model switcher, then engine preference
 
-The highest-leverage UX win: pick "MLX" once and the entire site shows MLX; pick "E4B" and
-every snippet on every page rewrites its model ID.
+The model half is implemented: pick "E4B" and marked examples across the site rewrite their model
+IDs. A global engine preference remains a separate follow-up; pages currently show the runtimes
+their tutorial actually covers.
 
-- [ ] Header control for engine and model, persisted in `localStorage`.
+- [x] Lean control in the page context header, persisted in `localStorage` and the URL.
+- [ ] Add a global engine preference once pages have equivalent, verified examples for each engine.
 - [ ] Extend Zensical's `content.tabs.link` to sync site-wide.
-- [ ] **Capability-aware gating, driven by `data/models.yml`:** audio examples must not offer
-      26B A4B or 31B (text+image only). When the selected model can't do what the page shows,
-      show an inline explanation and the nearest valid model — never a broken snippet, and
-      never silently swap the user's choice.
-- [ ] Graceful fallback when a model/engine pair has no published weights.
-- **Verify:** select 31B, navigate to `capabilities/audio.md`, confirm the gating message
-      appears and no invalid command is shown.
-- **Done when:** no combination of switcher state produces a command that would fail.
+- [x] **Capability-aware gating, driven by `data/models.yml`:** audio examples must not offer
+      26B A4B or 31B. When the selected model can't do what the page shows, show an inline
+      explanation and the nearest valid model — never a broken snippet, never a silent swap.
+- [x] Graceful fallback when a model/engine or identifier-flavour pair has no published weights.
+- [x] Opt-in `.model-swap` blocks across the current Gemma 4 quickstart, deployment, capability,
+      QAT, and QLoRA pages; base, instruction, QAT, GGUF, Ollama, and MLX IDs stay semantically
+      correct.
+- **Verify:** select 31B, navigate to `capabilities/audio.md`, confirm the gating message appears
+      and no invalid command is shown.
+- **Model selector verified:** 31B → audio shows the gating message and retains the authored E2B
+      example; multi-engine pages only offer models published for every marked block.
+- **Done when:** engine selection and linked tabs are implemented and no combination of model and
+      engine state produces a command that would fail.
 
 ### Step 4.3 — "Choose your model" decision flow
 - [ ] Four questions → recommendation + copy-paste command for the user's engine.
@@ -1076,8 +467,7 @@ every snippet on every page rewrites its model ID.
 ### Step 4.4 — Tokenizer & chat-template playground
 - [ ] Paste a conversation → exact `<start_of_turn>` token stream, token count.
 - [ ] Image token budget for a given resolution and aspect ratio.
-- [ ] Reuses the existing `tokens.css` badge styling.
-- Pairs directly with `get-started/prompt-formatting.md`.
+- [ ] Reuses the existing `tokens.css` badge styling. Pairs with `get-started/prompt-formatting.md`.
 
 ---
 
@@ -1087,7 +477,6 @@ every snippet on every page rewrites its model ID.
 - [ ] Embed <https://huggingface.co/spaces/google/gemma4_vision_token_budget> on a Demos page.
 - [ ] Lazy-load (Spaces are heavy) and design a fallback for sleeping Spaces — a dead iframe
       looks worse than no iframe.
-- [ ] Evaluate: sizing, theme match, mobile behavior, load time.
 - **Verify:** view on mobile and desktop, light and dark. Test with the Space asleep.
 - **Done when:** we know whether to standardize on iframe or `<gradio-app>`, documented here.
 
@@ -1099,19 +488,14 @@ every snippet on every page rewrites its model ID.
 
 ## Phase 6 — Learn
 
-### Step 6.1 — Visual guides, fully ported
-Not outbound links — complete pages hosted here. Maarten provides raw SVG for all images.
+### Step 6.1 — Visual guides: vendor the images
+The four guides are imported and hand-edited. Their images still point at the Substack CDN.
 
-- [ ] 6.1a A Visual Guide to Gemma 4
-- [ ] 6.1b A Visual Guide to Gemma 4 12B
-- [ ] 6.1c A Visual Guide to MTP for Gemma 4
-- [ ] 6.1d A Visual Guide to DiffusionGemma
-- [ ] Establish the reading experience: wide layout, framed figures, dark-mode SVG handling,
-      canonical link back to the newsletter.
-- [ ] **Contextual cross-linking** — link the MoE guide from the 26B A4B section, the
-      Quantization guide from QAT, the Reasoning LLMs guide from `capabilities/thinking.md`.
-      Related guides worth linking (not porting): MoE, Quantization, Reasoning LLMs, LLM Agents.
-- **Verify:** SVGs render crisply at all widths, legible in both themes.
+- [ ] Replace remote images with local assets (Maarten provides raw SVG).
+- [ ] Empty `PENDING_IMAGE_HOSTS` in `lint_docs.py` when done — it exists only for this.
+- [ ] **Contextual cross-linking** — link the MoE guide from the 26B A4B section, Quantization
+      from QAT, Reasoning LLMs from `capabilities/thinking.md`.
+- **Verify:** SVGs render crisply at all widths, legible in both themes; `--check images` → 0.
 
 ### Step 6.2 — Notebooks as source of truth
 - [ ] Keep `.ipynb` canonical, render to pages at build time.
@@ -1119,8 +503,8 @@ Not outbound links — complete pages hosted here. Maarten provides raw SVG for
 - [ ] Execute notebooks in CI — a "snippets are tested" badge is a real credibility marker.
 
 ### Step 6.3 — Task-first entry points
-- [ ] "I want to…" layer: build a RAG app · extract structured JSON · caption images ·
-      classify content · build a tool-using agent. Each is a curated path across existing pages.
+- [ ] "I want to…" layer: build a RAG app · extract structured JSON · caption images · classify
+      content · build a tool-using agent. Each is a curated path across existing pages.
 
 ---
 
@@ -1129,184 +513,97 @@ Not outbound links — complete pages hosted here. Maarten provides raw SVG for
 Only after Phases 0–2 are clean.
 
 - [ ] **In-browser playground** — E2B q4 via WebGPU/transformers.js, running in the docs page.
-      ~1.5–2 GB download, so explicit opt-in and clear progress UI. "Try Gemma 4 with zero
-      install, right here" is unmatched for an open-weights model.
+      ~1.5–2 GB download, so explicit opt-in and clear progress UI.
 - [ ] **Ask Gemma about Gemma** — docs chatbot on transformers.js or an HF Space endpoint.
-      Doubles as a live demo and a search upgrade.
-- [ ] **Interactive Gemma 4 architecture explainer** — clickable/scrollytelling coverage of PLE
-      (why E2B's memory exceeds its effective params), MoE routing on 26B A4B, MTP draft heads,
-      and the 12B's encoder-free linear projections. These four concepts are exactly what
-      confuses people, and prose isn't fixing it.
-- [ ] **MTP animated explainer** — draft-vs-verify animation with an acceptance-rate slider
-      showing the resulting speedup.
+- [ ] **Interactive Gemma 4 architecture explainer** — PLE, MoE routing on 26B A4B, MTP draft
+      heads, the 12B's encoder-free linear projections. These four confuse people and prose
+      isn't fixing it.
+- [ ] **MTP animated explainer** — draft-vs-verify with an acceptance-rate slider.
 - [ ] **DiffusionGemma denoising animation** — text unmasking step by step.
 - [ ] **Community throughput leaderboard** — measured tok/s per (model × quant × engine ×
-      hardware), PR-based submissions. Exists nowhere authoritative; high maintenance, high payoff.
+      hardware), PR-based. Exists nowhere authoritative; high maintenance, high payoff.
 - [ ] **Cost calculator** — GPU $/hr → $/1M tokens, self-host vs. API.
 
 ---
 
 ## Phase 8 — Maintainer skill & handover
 
-*Finalized once the docs are done, but **drafted incrementally** — every phase that establishes
-a convention adds its section immediately, while the reasoning is fresh. A skill written from
-scratch at the end will miss the things that were obvious in the moment.*
+*Finalized once the docs are done, but **drafted incrementally** — every phase that establishes a
+convention adds its section immediately, while the reasoning is fresh.*
 
 ### Step 8.1 — `gemma-docs` authoring skill
-A skill that lets any agent update these docs correctly without rediscovering the conventions.
 Must cover:
 
-- [ ] **Content fidelity rules** — the non-negotiable section above, stated first and plainly.
-- [ ] **How to port a page** — fetch `.md.txt`, run `scripts/convert_notebook.py`, what the
-      converter handles vs. what needs manual repair, required frontmatter including `source_url`.
-- [ ] **How to add a page** — where it goes in nav, frontmatter schema, which components to use.
-- [ ] **The design system** — which stylesheet owns what, the token layer, the rule that raw hex
-      only lives in `gemma.css`, when to add a new stylesheet instead of growing one.
+- [ ] **Content fidelity rules** — stated first and plainly.
+- [ ] **How to port a page** — fetch `.md.txt`, run the pipeline, what the converter handles vs.
+      what needs manual repair, and freezing immediately.
+- [ ] **How to add a page** — nav placement, frontmatter schema, which components to use.
+- [ ] **The design system** — which stylesheet owns what, the token layer, when to add a sheet.
 - [ ] **Component catalog** — every card/callout/badge/figure with copy-paste examples.
-- [ ] **`data/models.yml`** — that it's the single source of truth, what reads from it
-      (tables, VRAM calculator, switcher gating), and why hand-editing a model table is a bug.
-- [ ] **Verification** — `scripts/lint_docs.py` checks, how to run the build, what CI enforces.
-- [ ] **Gotchas** — Zensical version quirks, features in `zensical.toml` that aren't implemented,
-      converter edge cases, anything accumulated in *Notes* below.
+- [ ] **The data files** — what reads `data/models.yml` and `data/frameworks.yml`, and why
+      hand-editing a model table is a bug.
+- [ ] **Verification** — the lint checks, how to run the build, what CI enforces.
+- [ ] **Gotchas** — everything in the archive's *Notes*.
 - [ ] **What not to do** — don't reword ported prose, don't hotlink images, don't add hex outside
-      the token file, don't commit or push (that's Maarten's).
+      the token file, don't commit or push.
 - **Verify:** hand a fresh agent the skill and a page to port. It should succeed with no
   additional context and pass the linter first try.
-- **Done when:** that test passes.
 
 ### Step 8.2 — Contributor docs
-- [ ] `docs/contributing/` covering the same ground for humans, linking the components page
-      from Step 1.2.
+- [ ] `docs/contributing/` covering the same ground for humans.
 
 ---
 
-## Open decisions
+## Tooling lifecycle
 
-- Whether visual-guide diagrams can also appear inline in the reference docs (with attribution),
-  which would be a much stronger integration than standalone guide pages alone.
-- Whether the throughput leaderboard is worth its ongoing maintenance cost.
+Scripts are **migration tooling or permanent infrastructure**, and the two have opposite
+lifespans. Every entry below has a stated end date so the directory doesn't quietly become an
+archive of one-shot scripts nobody dares delete.
 
----
+| Script | Lines | Fate |
+|---|---|---|
+| `lint_docs.py` | 804 | **Permanent.** CI gate. |
+| `frontmatter_schema.py` | 233 | **Permanent.** Imported by the linter. |
+| `generate_model_names.py` | 86 | **Permanent.** Compiles `data/models.yml` into the template partial the page context header reads; CI verifies it is current. |
+| `convert_notebook.py` + `test_convert_notebook.py` | 896 | **Delete when Phase 2.4 closes** — i.e. once the 6 keep-or-drop decisions are made and nothing more will be imported from upstream. |
+| `port_docs.py` | 135 | **Delete with the converter.** All 63 manifest pages are frozen, so today it can only skip. |
+| `check_fidelity.py` | 223 | **Delete with the converter.** Same reason: it reports `0 faithful, 63 frozen` and has nothing left to check. |
+| `rewrite_links.py` | 253 | **Delete with the converter.** One-shot link migration; `links` and `internal` have been 0 for weeks. |
+| `sources.toml` + `link_map.toml` | 577 | **Delete with the converter.** ⚠️ `source` is now duplicated in every page's `source_url` frontmatter — the manifest's only unique data is `frozen`, `assets` and `importer`. |
+| `backfill_frontmatter.py` | 373 | **Delete when Phase 3 closes.** One-shot; frontmatter is complete and hand-maintained from here. New pages get theirs from the Phase 8 skill. |
+| `import_newsletter.py` | 259 | **Keep while more visual guides may arrive**; revisit at Phase 6.1. |
+| `vendor_images.py` | 136 | **Keep until Phase 6.1** — the Substack images still need vendoring. |
+
+Roughly **2,450 of 3,877 lines are scheduled for deletion**, most of it gated on one decision:
+whether anything else gets ported from upstream. Making that call early shrinks the surface a
+maintainer has to understand.
 
-### Step 0.7 — Rendering fixes from review ✅
-
-All fixed **in the converter**, not per-page, so re-porting preserves them.
-
-- [x] **Devsite card grids → plain bullets.** Upstream's own `.md.txt` export mangles its card
-      HTML into an empty bullet + `####` heading + paragraph. Now collapsed to
-      `- **Title** — description`. Affected shieldgemma, recurrentgemma, datagemma,
-      gemma-scope, paligemma, gemma-aps (14 cards).
-- [x] **Videos embedded, not linked.** A standalone `[Video](youtube…)` becomes a responsive
-      16:9 `youtube-nocookie` iframe (lazy-loaded). 8 pages.
-- [x] **Model-card header centred.** Upstream wraps the banner/links/license block in
-      `<p align="center">`, which the export drops. Restored as `.model-card-header`.
-- [x] **Code inside list items stays inside.** Fences were emitted at column 0, breaking out of
-      the list so everything after started a new one — `embeddinggemma/inference.md`'s RAG
-      section. Blocks are now dedented by their own indent and the fence indented to its item.
-- [x] **Nested lists tightened.** A blank line between a bullet and its own sub-list made
-      Markdown render it "loose", wrapping items in `<p>` inconsistently.
-- [x] **`> NOTE:` blockquotes → admonitions.** 4 in functiongemma pages. The `**Note:**` lines in
-      `capabilities/thinking.md` are inside model-output blocks and were correctly left verbatim.
-- [x] **Gemma 1/2/3 model cards reference themselves.** Upstream's "Model Page: Gemma → /core"
-      resolved to Gemma 4 here, so the Gemma 2 card advertised Gemma 4 as its model page.
-      New `[page_overrides]` in `link_map.toml` handles per-page mappings.
-- [x] `docs/stylesheets/components.css` added (video embeds, model-card header) — the first
-      piece of the Phase 1 modular stylesheet layout.
-
-**The fidelity gate caught these transformations and blocked 8 pages** until
-`check_fidelity.py` was taught that video URLs and empty `[](url)` duplicates are markup, not
-prose. Working as designed — a transformation must be declared before it can land.
+---
 
 ## Pipeline order
 
-Steps that rewrite pages must run in this order, because each stage can undo the previous one:
+Steps that rewrite pages must run in this order, because each stage can undo the previous one
+(conversion restores upstream image URLs; vendoring replaces them with local paths):
 
 ```
-port_docs.py  ->  vendor_images.py  ->  lint_docs.py --baseline
+port_docs.py  ->  vendor_images.py  ->  rewrite_links.py  ->  lint_docs.py --baseline
 ```
 
 `check_fidelity.py --manifest` can run at any point; it ignores image targets by design.
 
-## Notes
-
-*Agents: record surprises, deviations, and anything that changes the plan here.*
-
-- Zensical is pinned exactly via `pyproject.toml` and `uv.lock` (`zensical==0.0.51`). Some Material features listed in
-  `zensical.toml` may not be implemented yet — verify before relying on them.
-
-**2026-08-03 (Step 0.1)**
-
-- ⚠️ **Two workflows both deploy to GitHub Pages on push to `main`** — `docs.yml` and
-  `deploy.yml`. They will race; `deploy.yml` has a `concurrency: pages` group, `docs.yml`
-  doesn't. They also install zensical differently (`docs.yml` → plain PyPI, `deploy.yml` → uv +
-  `pypi.zensical.org`). **Needs a decision from Maarten** — `deploy.yml` looks like the intended
-  one. Left both in place rather than deleting unilaterally.
-- **Correction to the original audit:** devsite callouts are *not* an issue. The converter
-  already converts them (36 admonitions present, 0 unconverted). The earlier "~16" figure came
-  from a combined grep pattern that also matched `.md.txt` and `colab.research` strings.
-  Step 0.2's admonition work is therefore already done — verify with a fixture rather than rewrite.
-- `check_images` and `check_internal` scan **outside code blocks only**. Markdown-like syntax
-  inside snippets is code, not a reference (e.g. an f-string `{render_inline(image, resize=(64,64))}`
-  in `paligemma/fine-tuning.md:515` was a false positive).
-- `check_links` scans **everywhere**, including code blocks — 5 upstream links live inside
-  snippets. Some may be intentional (e.g. downloading a sample asset); review case by case in Step 0.5.
-- `Finding.key()` deliberately excludes line numbers so edits above a known finding don't
-  resurrect it as "new".
-- The 34 base64 images are all in `paligemma/fine-tuning.md`. Fixing that one file clears the
-  `size` check and 34 of the 52 `images` findings at once — good first target for Step 0.3/0.4.
-
-**2026-08-03 (Step 0.2)**
-
-- ⚠️ **Pages have been hand-edited since their port.** There is currently no way to tell a
-  straight port from a hand-edited page automatically — hence `--divergence` in Step 0.3 and
-  `source_url` frontmatter in Step 3.1. Until one of those lands, **assume any page may contain
-  hand-work and never re-convert in bulk.**
-- **Get Started is reviewed and approved by Maarten.** Repair defects in place; do not re-convert.
-- Only 6 files have converter-class defects. The other 294 backlog findings are links, images,
-  frontmatter, and nav — all outside the converter's remit.
-- The converter's `text` fallback matters more than it looks: an early version of the Python
-  detector matched `\bfor\b` inside "for formatting the answer", which is exactly how prose got
-  classified as code. Keep the detector on *syntax* signals, never bare English words.
-
-**2026-08-03 (Step 0.3)**
-
-- **`scripts/sources.toml` is the authority on what may be re-converted.** Absent from the
-  manifest = hand-authored or hand-edited = never re-convert. Add a page to it only after
-  confirming it is a straight port.
-- Fidelity checking must compare against *source minus designed drops*, not raw source.
-  A naive comparison flags every Colab/Kaggle badge as lost prose — that produced 10 false
-  failures before `strip_designed_drops` reused the converter's own `is_badge_line`.
-- `check_fidelity.py` and `port_docs.py` **share `cache_name()`**. They must: keying the source
-  cache on the bare filename collided every `index.md` across all model directories onto one
-  entry, making the two tools disagree (0 failures vs 26).
-- `--dry-run` extracts images to a temp directory. Without that it wrote 34 files to
-  `docs/assets/` while claiming to change nothing.
-- Two blank lines mid-cell is common in notebook exports — worth checking for whenever a new
-  converter bug is suspected.
-
-**2026-08-03 (Step 0.4)**
-
-- **Conversion and vendoring fight each other.** Converting restores upstream image URLs;
-  vendoring replaces them with local paths. Always port first, then vendor. See *Pipeline order*.
-- Any check that gates writes (as `port_docs.py` does on fidelity) must be **precise about what
-  is allowed to change**, or it locks pages out of future edits. Vendoring tripped the fidelity
-  check on 6 pages until image targets were excluded from the word comparison.
-- `docs/assets/` is 8.2 MB, most of it `capabilities/` (7 notebook-output PNGs, 5.5 MB). If repo
-  size becomes a concern, those are the candidates for recompression — not the diagrams.
-- The site's `404.html` uses root-absolute `/assets/` paths. That is correct (a 404 can be served
-  from any depth) and will look like a broken link to any checker resolving paths relative to the
-  file. Don't "fix" it.
-- ⚠️ **Any new workflow must be zizmor-clean before it is pushed.** Pin every action to a SHA,
-  set an explicit `permissions:` block, and scope write permissions to the job that needs them.
-  Check locally with `uvx zizmor --persona=regular .github/workflows/` — it catches everything
-  CI does and takes seconds. The org fails on Medium/High, which is broader than the published
-  mandatory-check list.
-- **Why zizmor appeared only now, on this branch.** The scanner is "GitHub Actions Scan"
-  (`.github/workflows/github_actions_scan.yml`), injected org-wide by `google-gemma` — it is not
-  in this repo. It triggers on **`pull_request_target`**, and every commit before this branch went
-  *straight to main via push*, so it had never run. `foundation` is the repo's first pull request.
-  The pre-existing `docs.yml` and `deploy.yml` did not pass the policy; they were never scanned.
-- **The scan is diff-scoped.** It flagged only `lint.yml` while `deploy.yml` sat unmodified on the
-  same branch with four unpinned actions. Pre-existing workflow debt therefore stays invisible
-  until a PR touches that file — worth auditing a workflow *before* you need to edit it under
-  time pressure.
+---
+
+## Open decisions
+
+- **Will anything else be ported from upstream?** Gates the deletion of ~2,450 lines of tooling
+  (see above) and closes Phase 2.4.
+- The 6 unported upstream pages — keep or drop, individually.
+- Whether visual-guide diagrams can also appear inline in the reference docs (with attribution),
+  which would be a stronger integration than standalone guide pages alone.
+- Whether the throughput leaderboard is worth its ongoing maintenance cost.
+- The five pages with no H1 — leave as-is, or add real headings.
+- **Unresolved: Ollama download sizes.** `ollama.com/library/gemma4/tags` states E4B 9.6 GB and
+  12B 7.6 GB, making E4B *larger* than 12B, which contradicts the validated Q4_0 column in
+  `models/gemma-4/index.md`. Verified verbatim twice. Most likely the default tag per size uses a
+  different quantization, but no explanation appears on the page — so the figures stay
+  unexplained rather than guessed at.
diff --git a/data/frameworks.yml b/data/frameworks.yml
new file mode 100644
index 0000000..43397ed
--- /dev/null
+++ b/data/frameworks.yml
@@ -0,0 +1,114 @@
+# Engines, libraries and runtimes the docs show code for -- the single source of truth.
+#
+# Consumed by:
+#   - Frontmatter validation  (scripts/frontmatter_schema.py, the `frameworks` key)
+#   - The page context strip  (overrides/partials/model-names.html, generated)
+#
+# A controlled vocabulary on purpose: "transformers" vs "huggingface" vs "hf" spread across 89
+# pages would be unusable. Add an entry here before using it in a page -- the linter rejects
+# anything not listed, which is the point.
+#
+# This list holds only what is in use. It is not a catalogue of everything that can run Gemma.
+#
+#   id    what frontmatter uses. Lowercase, kebab-case.
+#   name  how it is written in prose and in the UI.
+#   url   the project's own home. Where one was already linked from a docs page, that URL is
+#         reused verbatim; the four marked `unverified` were not found anywhere in docs/ and
+#         should be confirmed before anyone relies on them.
+#   page  our page for it, where we have one.
+#
+# There is deliberately no `category`. The previous version had one and it was incoherent:
+# `local`/`serve` classify where something runs while `library`/`tuning`/`orchestration`
+# classify what kind of software it is, so `transformers` and `llamacpp` -- both libraries,
+# both usually local -- landed in different groups for no reason a reader could follow.
+
+frameworks:
+  - id: ollama
+    name: Ollama
+    url: https://ollama.com
+    page: deployment/ollama.md
+
+  - id: llamacpp
+    name: llama.cpp
+    url: https://github.com/ggml-org/llama.cpp
+    page: deployment/llamacpp.md
+
+  - id: lmstudio
+    name: LM Studio
+    url: https://lmstudio.ai
+    page: deployment/lmstudio.md
+
+  - id: mlx
+    name: MLX
+    url: https://github.com/ml-explore/mlx
+    page: deployment/mlx.md
+
+  - id: litert
+    name: LiteRT
+    url: https://ai.google.dev/edge/litert
+    page: deployment/litert.md
+
+  - id: webgpu
+    name: WebGPU
+    url: https://www.w3.org/TR/webgpu/     # unverified
+    page: deployment/web.md
+
+  - id: vllm
+    name: vLLM
+    url: https://docs.vllm.ai
+    page: deployment/vllm.md
+
+  - id: sglang
+    name: SGLang
+    url: https://docs.sglang.io
+    page: deployment/sglang.md
+
+  - id: google-cloud
+    name: Google Cloud
+    url: https://cloud.google.com          # unverified
+    page: deployment/google-cloud.md
+
+  - id: transformers
+    name: Hugging Face Transformers
+    url: https://huggingface.co/docs/transformers
+    page: deployment/huggingface.md
+
+  - id: keras
+    name: Keras
+    url: https://keras.io
+    page: deployment/keras.md
+
+  - id: jax
+    name: JAX
+    url: https://jax.readthedocs.io
+
+  - id: pytorch
+    name: PyTorch
+    url: https://pytorch.org               # unverified
+
+  - id: sentence-transformers
+    name: Sentence Transformers
+    url: https://sbert.net
+
+  - id: peft
+    name: PEFT
+    url: https://huggingface.co/docs/peft
+
+  - id: trl
+    name: TRL
+    url: https://github.com/huggingface/trl
+
+  - id: langchain
+    name: LangChain
+    url: https://python.langchain.com
+    page: deployment/langchain.md
+
+  - id: android
+    name: Android
+    url: https://developer.android.com
+    page: deployment/mobile.md
+
+  - id: ios
+    name: iOS
+    url: https://developer.apple.com/ios/  # unverified
+    page: deployment/mobile.md
diff --git a/data/models.yml b/data/models.yml
index 413a3c1..337e0c9 100644
--- a/data/models.yml
+++ b/data/models.yml
@@ -2,6 +2,8 @@
 #
 # Consumed by:
 #   - Generated tables in quickstart.md and models/index.md (macros plugin, `models.*`)
+#   - Frontmatter validation       (scripts/frontmatter_schema.py, the `models` key)
+#   - The page context header      (overrides/partials/model-names.html, generated)
 #   - The VRAM calculator          (PLAN.md Step 4.1)
 #   - Global model/engine switcher (PLAN.md Step 4.2) -- `modalities` drives capability gating,
 #     so an audio example can never offer a model that cannot hear
@@ -12,6 +14,77 @@
 # straight port (scripts/sources.toml) and re-porting would overwrite any template. The figures
 # below are copied from it; keep them in sync if upstream revises them.
 
+# ---------------------------------------------------------------------------------------------
+# Families
+#
+# `id` matches a directory under docs/models/, which is what the `models` frontmatter key and
+# the page context header address. `name` is how the family is written in prose and in the UI;
+# `page` is the canonical local destination for that family in the context header.
+#
+# Order is display order: current generation, variants, previous generations, research.
+# ---------------------------------------------------------------------------------------------
+
+families:
+  - id: gemma-4
+    name: Gemma 4
+    page: models/gemma-4/model-card.md
+    group: current
+
+  - id: gemma-3n
+    name: Gemma 3n
+    page: models/gemma-3n/model-card.md
+    group: variant
+  - id: diffusiongemma
+    name: DiffusionGemma
+    page: models/diffusiongemma/model-card.md
+    group: variant
+  - id: functiongemma
+    name: FunctionGemma
+    page: models/functiongemma/model-card.md
+    group: variant
+  - id: embeddinggemma
+    name: EmbeddingGemma
+    page: models/embeddinggemma/model-card.md
+    group: variant
+  - id: paligemma
+    name: PaliGemma
+    page: models/paligemma/model-card-v2.md
+    group: variant
+  - id: shieldgemma
+    name: ShieldGemma
+    page: models/shieldgemma/model-card-v2.md
+    group: variant
+
+  - id: gemma-3
+    name: Gemma 3
+    page: models/gemma-3/index.md
+    group: previous
+  - id: gemma-2
+    name: Gemma 2
+    page: models/gemma-2/index.md
+    group: previous
+  - id: gemma-1
+    name: Gemma
+    page: models/gemma-1/index.md
+    group: previous
+
+  - id: recurrentgemma
+    name: RecurrentGemma
+    page: models/recurrentgemma/model-card.md
+    group: research
+  - id: datagemma
+    name: DataGemma
+    page: models/datagemma/index.md
+    group: research
+  - id: gemma-scope
+    name: Gemma Scope
+    page: models/gemma-scope/index.md
+    group: research
+  - id: gemma-aps
+    name: Gemma APS
+    page: models/gemma-aps/index.md
+    group: research
+
 gemma4:
   context_windows:
     small: 128K      # E2B, E4B
@@ -32,6 +105,14 @@ gemma4:
       modalities:
         input: [text, image, audio, video]
         output: [text]
+      ids:                             # verified repo/tag strings; see PLAN.md
+        hf: google/gemma-4-E2B-it
+        hf_base: google/gemma-4-E2B
+        hf_qat: google/gemma-4-E2B-it-qat-w4a16-ct
+        gguf: ggml-org/gemma-4-E2B-it-GGUF
+        gguf_qat: google/gemma-4-E2B-it-qat-q4_0-gguf
+        ollama: gemma4:e2b
+        mlx: mlx-community/gemma-4-E2B-it-qat-4bit
       memory:                          # GB, static weights + ~20% overhead
         bf16: 11.4
         sfp8: 5.7
@@ -50,6 +131,14 @@ gemma4:
       modalities:
         input: [text, image, audio, video]
         output: [text]
+      ids:                             # verified repo/tag strings; see PLAN.md
+        hf: google/gemma-4-E4B-it
+        hf_base: google/gemma-4-E4B
+        hf_qat: google/gemma-4-E4B-it-qat-w4a16-ct
+        gguf: ggml-org/gemma-4-E4B-it-GGUF
+        gguf_qat: google/gemma-4-E4B-it-qat-q4_0-gguf
+        ollama: gemma4:e4b
+        mlx: mlx-community/gemma-4-E4B-it-qat-4bit
       memory:
         bf16: 17.9
         sfp8: 8.9
@@ -68,6 +157,14 @@ gemma4:
       modalities:
         input: [text, image, audio, video]
         output: [text]
+      ids:                             # verified repo/tag strings; see PLAN.md
+        hf: google/gemma-4-12B-it
+        hf_base: google/gemma-4-12B
+        hf_qat: google/gemma-4-12B-it-qat-w4a16-ct
+        gguf: ggml-org/gemma-4-12B-it-GGUF
+        gguf_qat: google/gemma-4-12B-it-qat-q4_0-gguf
+        ollama: gemma4:12b
+        mlx: mlx-community/gemma-4-12B-it-qat-4bit
       memory:
         bf16: 26.7
         sfp8: 13.4
@@ -85,6 +182,13 @@ gemma4:
         # No audio. Video is supported as individual frames, i.e. as images.
         input: [text, image, video]
         output: [text]
+      ids:                             # verified repo/tag strings; see PLAN.md
+        hf: google/gemma-4-26B-A4B-it
+        hf_base: google/gemma-4-26B-A4B
+        gguf: ggml-org/gemma-4-26B-A4B-it-GGUF
+        gguf_qat: google/gemma-4-26B-A4B-it-qat-q4_0-gguf
+        ollama: gemma4:26b
+        mlx: mlx-community/gemma-4-26B-A4B-it-qat-4bit
       memory:
         bf16: 57.7
         sfp8: 28.8
@@ -101,6 +205,14 @@ gemma4:
       modalities:
         input: [text, image, video]
         output: [text]
+      ids:                             # verified repo/tag strings; see PLAN.md
+        hf: google/gemma-4-31B-it
+        hf_base: google/gemma-4-31B
+        hf_qat: google/gemma-4-31B-it-qat-w4a16-ct
+        gguf: ggml-org/gemma-4-31B-it-GGUF
+        gguf_qat: google/gemma-4-31B-it-qat-q4_0-gguf
+        ollama: gemma4:31b
+        mlx: mlx-community/gemma-4-31B-it-qat-4bit
       memory:
         bf16: 69.9
         sfp8: 34.9
diff --git a/docs/community/discord.md b/docs/community/discord.md
index cbad263..d209d73 100644
--- a/docs/community/discord.md
+++ b/docs/community/discord.md
@@ -1,3 +1,7 @@
+---
+title: Discord
+description: Join the Gemma developer community on Discord.
+---
 # Discord
 
 Join the Gemma developer community on Discord.
diff --git a/docs/community/gemma-skills.md b/docs/community/gemma-skills.md
index 6253346..de38563 100644
--- a/docs/community/gemma-skills.md
+++ b/docs/community/gemma-skills.md
@@ -1,3 +1,7 @@
+---
+title: Gemma Skills
+description: Curated AI agent skills, system prompts, and tool definitions.
+---
 # Gemma Skills
 
 Curated AI agent skills, system prompts, and tool definitions.
diff --git a/docs/community/gemmaverse.md b/docs/community/gemmaverse.md
index b304edc..a2b0fba 100644
--- a/docs/community/gemmaverse.md
+++ b/docs/community/gemmaverse.md
@@ -1,3 +1,7 @@
+---
+title: Gemmaverse
+description: Explore community projects and showcases.
+---
 # Gemmaverse
 
 Explore community projects and showcases.
diff --git a/docs/contributing/components.md b/docs/contributing/components.md
index 1df3e96..b7d3431 100644
--- a/docs/contributing/components.md
+++ b/docs/contributing/components.md
@@ -1,5 +1,7 @@
 ---
 title: Components
+models: [gemma-4]
+frameworks: [ollama]
 description: The reusable building blocks these docs are made from, with live examples and the markup to copy.
 ---
 
@@ -75,6 +77,71 @@ disagree with the tables.
 `--muted` is how the model switcher (Step 4.2) will show a capability the selected model does
 not have — struck through rather than hidden, so the reader learns the limitation.
 
+## Page context strip
+
+The quiet band under every H1. You never write this markup; it is rendered from frontmatter by
+`overrides/partials/page-context.html`. The strip on *this* page is the live example.
+
+```yaml
+models: [gemma-4]        # what the page is about
+frameworks: [ollama]     # primary tools the page teaches, linked to their home pages
+requires: audio          # narrows the selector to models that accept audio
+```
+
+Two kinds of item sit on the line, and keeping them apart is the design:
+
+| Item | Comes from | Meaning |
+|---|---|---|
+| `Models` | `models` frontmatter | What the page is **about**. Links to the local model page. |
+| `Uses` | `frameworks` frontmatter | Primary tools the page teaches. Links out to each project. |
+| `Examples` | the page's own code | What **you** want the commands to target. A preference. |
+
+Pairs are separated by a 1px rule, values all share one weight, and labels are muted — 16px of
+space alone was not enough to stop `Frameworks Sentence Transformers` reading as a single run
+of grey text.
+
+Keep `frameworks` editorial rather than exhaustive. Name the user-facing stack needed to follow
+the page, not every imported or transitive dependency. For example, a Transformers tutorial that
+imports PyTorch tensors should say `frameworks: [transformers]`; add `pytorch` only when the page
+actually teaches its APIs or workflow.
+
+`Examples` is deliberately *not* derived from `models:`. Those answer different questions:
+the Ollama page is about Gemma 4, but its commands could target anything Ollama publishes. So
+the selector is built from what the page's marked blocks can actually be rewritten to — which
+means it disappears on pages with nothing switchable rather than claiming a size the page never
+mentioned.
+
+### Swappable code blocks
+
+Add `{ .model-swap }` to a fence and the selector rewrites its model identifiers:
+
+````markdown
+```bash { .model-swap }
+ollama pull gemma4:12b
+```
+````
+
+Substitution is **opt-in per block** because a page's code was validated as written. Unmarked
+blocks are never touched — which is what keeps the tag table on `deployment/ollama.md` listing
+every tag while the `pull` command above it follows the selector.
+
+A model is only offered if it has a published id for **every** engine appearing in the page's
+marked blocks, so switching can never rewrite some snippets and silently leave others behind.
+If an intentionally separate example lacks one size—such as a QAT checkpoint—mark it
+`{ .model-fixed }` and explain the limitation beside it instead of unnecessarily narrowing the
+page's primary examples.
+
+Identifier variants live under each size's `ids` mapping in `data/models.yml`. Use a distinct
+flavour when syntax has different semantics—for example `hf_base` for training, `hf_qat` for a
+quantization-aware Transformers checkpoint, and `gguf_qat` for its llama.cpp equivalent. The
+generator exposes those variants to the switcher; do not encode size substitutions by hand in
+JavaScript.
+
+The choice is remembered across the site and reflected in the page URL. If a remembered model
+is unavailable, the examples remain at their authored default and the picker explains why;
+there is no silent substitution. Use `{ .model-fixed }` to acknowledge an intentionally static
+block when the same page has swappable examples for that engine.
+
 ## Steps
 
 A numbered walkthrough. Built on an ordered list, so it still reads correctly without CSS.
@@ -184,6 +251,7 @@ reload; hover effects should become instant.
 | `nav.css` | Header, tabs, sidebars, table of contents |
 | `content.css` | Tables, admonitions, code, content tabs |
 | `components.css` | Everything on this page |
+| `page-context.css` | The applicability bar above every H1 |
 | `tokens.css` | Chat-token badges (`<start_of_turn>` etc.) |
 | `home.css` | Landing page: chrome, backdrop, hero |
 | `motion.css` | Transitions and the reduced-motion opt-out |
diff --git a/docs/cookbook.md b/docs/cookbook.md
index 60d53af..09d74e8 100644
--- a/docs/cookbook.md
+++ b/docs/cookbook.md
@@ -1,44 +1,37 @@
 ---
 title: Cookbook
-hide:
-  - navigation
-  - toc
+description: Interactive demos, tools, notebooks, and community resources for the Gemma model family.
 cards:
   - title: "Vision Token Budget"
     desc: "Visualize how Gemma 4 allocates image tokens across resolutions and aspect ratios."
     url: "https://huggingface.co/spaces/google/gemma4_vision_token_budget"
-
   - title: "Gemma Skills"
     desc: "A collection of task-specific skills and prompts for Gemma models, from code generation to analysis."
     url: "https://github.com/google-gemma/gemma-skills"
-
   - title: "Gemma Cookbook"
     desc: "Official Jupyter notebooks and guides covering inference, fine-tuning, RAG, function calling, and more."
     url: "https://github.com/google-gemma/gemma-cookbook"
-
   - title: "Google AI Studio"
     desc: "Experiment with Gemma models in the browser. Prototype prompts, test multimodal inputs, and export code."
     url: "https://aistudio.google.com/"
-
   - title: "Hugging Face Collection"
     desc: "Browse all Gemma 4 model weights, quantized variants, and community fine-tunes."
     url: "https://huggingface.co/collections/google/gemma-4-67ab89fa1f8b53e3d1da28d9"
-
   - title: "Kaggle Models"
     desc: "Download Gemma model weights from Kaggle with starter notebooks and benchmarks."
     url: "https://www.kaggle.com/models/google/gemma-4"
-
   - title: "Ollama Library"
     desc: "Run Gemma 4 locally with a single command. Pre-quantized weights for CPU and GPU inference."
     url: "https://ollama.com/library/gemma4"
-
   - title: "LM Studio"
     desc: "Run Gemma models with a native desktop GUI. Chat, completions, and a local OpenAI-compatible server."
     url: "https://lmstudio.ai/"
-
   - title: "Gemma GitHub"
     desc: "The official google-gemma GitHub organization with all repositories, tools, and community projects."
     url: "https://github.com/google-gemma"
+hide:
+  - navigation
+  - toc
 ---
 
 # Cookbook
diff --git a/docs/demos/concurrent.md b/docs/demos/concurrent.md
index 05e0f94..f71b65f 100644
--- a/docs/demos/concurrent.md
+++ b/docs/demos/concurrent.md
@@ -1,3 +1,7 @@
+---
+title: Concurrent
+description: A multi-model concurrent inference demo application.
+---
 # Concurrent
 
 A multi-model concurrent inference demo application.
diff --git a/docs/deployment/google-cloud.md b/docs/deployment/google-cloud.md
index d7a0601..4b4914a 100644
--- a/docs/deployment/google-cloud.md
+++ b/docs/deployment/google-cloud.md
@@ -1,3 +1,10 @@
+---
+title: Google Cloud
+description: "Deploy, serve, and fine-tune Gemma 4 on Google Cloud: Cloud Run, GKE, the Agent Development Kit, vLLM on TPUs, MaxText, and Sovereign Cloud."
+models: [gemma-4]
+frameworks: [google-cloud, vllm]
+source_url: "https://ai.google.dev/gemma/docs/integrations/google-cloud.md.txt"
+---
 The Google Cloud platform provides many options for deploying, serving,
 and fine-tuning Gemma 4 open models, including the following:
 
diff --git a/docs/deployment/huggingface.md b/docs/deployment/huggingface.md
index 89a650a..06f4db8 100644
--- a/docs/deployment/huggingface.md
+++ b/docs/deployment/huggingface.md
@@ -1,3 +1,10 @@
+---
+title: Run Gemma with Hugging Face Transformers
+description: Generating text, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models.
+models: [gemma-4, gemma-3, gemma-3n]
+frameworks: [transformers]
+source_url: "https://ai.google.dev/gemma/docs/core/huggingface_inference.md.txt"
+---
 # Run Gemma with Hugging Face Transformers
 
 
@@ -27,7 +34,7 @@ Prompting a Gemma model with text to get a text response is the simplest way to
 
 Use the `torch` and `transformers` libraries to create an instance of a model execution `pipeline` class with Gemma. When using a model for generating output or following directions, select an instruction tuned (IT) model, which typically has `it` in the model ID string. Using the `pipeline` object, you specify the Gemma variant you want to use, the type of task you want to perform, specifically `"any-to-any"` for multimodal generation, as shown in the following code example:
 
-```python
+```python { .model-swap }
 from transformers import pipeline
 
 MODEL_ID = "google/gemma-4-E2B-it"
diff --git a/docs/deployment/index.md b/docs/deployment/index.md
index 88c3c1f..e5a686b 100644
--- a/docs/deployment/index.md
+++ b/docs/deployment/index.md
@@ -1,3 +1,9 @@
+---
+title: Overview
+description: Choose an execution framework and a Gemma variant for your hardware, then run your first generation request.
+models: [gemma-4]
+source_url: "https://ai.google.dev/gemma/docs/run.md.txt"
+---
 There are two key decisions to make when you want to run a Gemma model:
 1) what Gemma variant you want to run, and 2) what AI execution framework you
 are going to use to run it? A key issue in making both these decisions has to do
diff --git a/docs/deployment/keras.md b/docs/deployment/keras.md
index 606fdcf..432319b 100644
--- a/docs/deployment/keras.md
+++ b/docs/deployment/keras.md
@@ -1,3 +1,10 @@
+---
+title: Run Gemma with Keras
+description: Generating content, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models.
+models: [gemma-4]
+frameworks: [keras]
+source_url: "https://ai.google.dev/gemma/docs/core/keras_inference.md.txt"
+---
 # Run Gemma with Keras
 
 
diff --git a/docs/deployment/langchain.md b/docs/deployment/langchain.md
index 61d9633..6165907 100644
--- a/docs/deployment/langchain.md
+++ b/docs/deployment/langchain.md
@@ -1,3 +1,10 @@
+---
+title: Get started with Gemma and LangChain
+description: This tutorial shows you how to get started with Gemma and LangChain, running in Google Cloud or in your Colab environment.
+models: [gemma-1]
+frameworks: [langchain, google-cloud, keras, jax, transformers]
+source_url: "https://ai.google.dev/gemma/docs/integrations/langchain.md.txt"
+---
 # Get started with Gemma and LangChain
 
 
diff --git a/docs/deployment/litert.md b/docs/deployment/litert.md
index 486c009..57da9d0 100644
--- a/docs/deployment/litert.md
+++ b/docs/deployment/litert.md
@@ -1,3 +1,10 @@
+---
+title: Convert Hugging Face Safetensors to MediaPipe Task
+description: This guide provides instructions for converting Gemma models in Hugging Face Safetensors format (.safetensors) into the MediaPipe Task file format (.task).
+models: [gemma-3]
+frameworks: [litert]
+source_url: "https://ai.google.dev/gemma/docs/conversions/hf-to-mediapipe-task.md.txt"
+---
 # Convert Hugging Face Safetensors to MediaPipe Task
 
 This guide provides instructions for converting Gemma models in Hugging Face
diff --git a/docs/deployment/llamacpp.md b/docs/deployment/llamacpp.md
index 423d171..9f6105e 100644
--- a/docs/deployment/llamacpp.md
+++ b/docs/deployment/llamacpp.md
@@ -1,6 +1,8 @@
 ---
 title: llama.cpp
 description: Run Gemma 4 with llama.cpp — GGUF repos for every size, image/audio/video input, and the OpenAI-compatible server.
+models: [gemma-4]
+frameworks: [llamacpp]
 ---
 
 # Run Gemma with llama.cpp
@@ -37,14 +39,14 @@ most capable local runtime for Gemma 4 — **image, audio and video input all wo
 
 ## Run
 
-```bash
+```bash { .model-swap }
 llama-cli -hf ggml-org/gemma-4-E2B-it-GGUF --prompt "Write a poem about the Kraken."
 ```
 
 `-hf` downloads the weights on first use, including the multimodal projector. With a system
 prompt:
 
-```bash
+```bash { .model-swap }
 llama-cli -hf ggml-org/gemma-4-E2B-it-GGUF -sys "You are Hong Gildong." -p "Who are you?"
 ```
 
@@ -61,7 +63,7 @@ llama-cli -hf ggml-org/gemma-4-E2B-it-GGUF -sys "You are Hong Gildong." -p "Who
 For 4-bit, prefer Google's [QAT](qat.md) weights — trained with quantization simulated rather
 than compressed afterwards:
 
-```bash
+```bash { .model-swap }
 llama-cli -hf google/gemma-4-12B-it-qat-q4_0-gguf -p "Explain PLE in one paragraph"
 ```
 
@@ -69,7 +71,7 @@ Available as `google/gemma-4-{E2B,E4B,12B,26B-A4B,31B}-it-qat-q4_0-gguf`.
 
 ## Multimodal
 
-```bash
+```bash { .model-swap }
 llama-mtmd-cli -hf ggml-org/gemma-4-E2B-it-GGUF
 ```
 
@@ -85,7 +87,7 @@ llama-server -m gemma-4-12B-it-Q4_K_M.gguf --mmproj mmproj-gemma-4-12B-it.gguf
 
 ## Server
 
-```bash
+```bash { .model-swap }
 llama-server -hf ggml-org/gemma-4-E2B-it-GGUF
 ```
 
diff --git a/docs/deployment/lmstudio.md b/docs/deployment/lmstudio.md
index 2d3325e..b7a052d 100644
--- a/docs/deployment/lmstudio.md
+++ b/docs/deployment/lmstudio.md
@@ -1,6 +1,8 @@
 ---
 title: LM Studio
 description: Run Gemma 4 with LM Studio — desktop app, lms CLI, and the OpenAI-compatible local server.
+models: [gemma-4]
+frameworks: [lmstudio]
 ---
 
 # Run Gemma with LM Studio
diff --git a/docs/deployment/mlx.md b/docs/deployment/mlx.md
index 40c739e..eac2f47 100644
--- a/docs/deployment/mlx.md
+++ b/docs/deployment/mlx.md
@@ -1,6 +1,8 @@
 ---
 title: Apple MLX
 description: Run Gemma 4 on Apple silicon with MLX — text and vision generation, model repos, and the OpenAI-compatible server.
+models: [gemma-4]
+frameworks: [mlx]
 ---
 
 # Run Gemma with MLX
@@ -19,14 +21,14 @@ pip install mlx mlx-lm mlx-vlm
 
 === "Text"
 
-    ```bash
-    mlx_lm.generate --model mlx-community/gemma-4-e2b-it-4bit --prompt "Who are you?"
+    ```bash { .model-swap }
+    mlx_lm.generate --model mlx-community/gemma-4-E2B-it-qat-4bit --prompt "Who are you?"
     ```
 
 === "Vision"
 
-    ```bash
-    mlx_vlm.generate --model mlx-community/gemma-4-e2b-it-4bit \
+    ```bash { .model-swap }
+    mlx_vlm.generate --model mlx-community/gemma-4-E2B-it-qat-4bit \
       --prompt "Describe this image." --image <path_to_image>
     ```
 
@@ -37,10 +39,11 @@ Community-converted weights live under
 
 | Size | Repository |
 |---|---|
-| E2B | `mlx-community/gemma-4-e2b-it-4bit` |
-| E4B | `mlx-community/gemma-4-e4b-it-qat-OptiQ-4bit` |
-| 12B | `mlx-community/gemma-4-12B-it-4bit`, `…-it-8bit` |
-| 26B A4B | `mlx-community/gemma-4-26b-a4b-it-4bit` |
+| E2B | `mlx-community/gemma-4-E2B-it-qat-4bit` |
+| E4B | `mlx-community/gemma-4-E4B-it-qat-4bit` |
+| 12B | `mlx-community/gemma-4-12B-it-qat-4bit` |
+| 26B A4B | `mlx-community/gemma-4-26B-A4B-it-qat-4bit` |
+| 31B | `mlx-community/gemma-4-31B-it-qat-4bit` |
 
 !!! note
     Coverage and naming vary — these are community conversions, not first-party releases.
@@ -50,19 +53,19 @@ Community-converted weights live under
 
 ## Server
 
-```bash
-mlx_vlm.server --model mlx-community/gemma-4-e2b-it-4bit --port 8080
+```bash { .model-swap }
+mlx_vlm.server --model mlx-community/gemma-4-E2B-it-qat-4bit --port 8080
 ```
 
 OpenAI-compatible API at `http://localhost:8080/v1`.
 
-```python
+```python { .model-swap }
 from openai import OpenAI
 
 client = OpenAI(base_url="http://localhost:8080/v1", api_key="not-needed")
 
 response = client.chat.completions.create(
-    model="mlx-community/gemma-4-e2b-it-4bit",
+    model="mlx-community/gemma-4-E2B-it-qat-4bit",
     messages=[{"role": "user", "content": "Say this is a test"}],
 )
 print(response.choices[0].message.content)
diff --git a/docs/deployment/mobile.md b/docs/deployment/mobile.md
index 3929f4a..071258d 100644
--- a/docs/deployment/mobile.md
+++ b/docs/deployment/mobile.md
@@ -1,3 +1,10 @@
+---
+title: Deploy Gemma on mobile devices
+description: This document outlines various methods and tools for deploying and running Gemma models on mobile devices, including using the Google AI Edge Gallery app and the MediaPipe LLM Inference API.
+models: [gemma-4]
+frameworks: [litert, android, ios]
+source_url: "https://ai.google.dev/gemma/docs/integrations/mobile.md.txt"
+---
 # Deploy Gemma on mobile devices
 
 This document outlines various methods and tools for deploying and running Gemma
diff --git a/docs/deployment/ollama.md b/docs/deployment/ollama.md
index eae93c1..385ce3b 100644
--- a/docs/deployment/ollama.md
+++ b/docs/deployment/ollama.md
@@ -1,6 +1,8 @@
 ---
 title: Ollama
 description: Run Gemma 4 locally with Ollama — model tags, image input, and the OpenAI-compatible server.
+models: [gemma-4]
+frameworks: [ollama]
 ---
 
 # Run Gemma with Ollama
@@ -18,13 +20,13 @@ ollama --version
 
 ## Run
 
-```bash
-ollama run gemma4 "Explain quantum computing simply"
+```bash { .model-swap }
+ollama run gemma4:12b "Explain quantum computing simply"
 ```
 
 Downloads the model on first use. To fetch without running:
 
-```bash
+```bash { .model-swap }
 ollama pull gemma4:12b
 ```
 
@@ -44,8 +46,8 @@ ollama pull gemma4:12b
 
 ## Images
 
-```bash
-ollama run gemma4 ./image.png "What is in this image?"
+```bash { .model-swap }
+ollama run gemma4:12b ./image.png "What is in this image?"
 ```
 
 !!! note
@@ -58,7 +60,7 @@ Ollama serves on port `11434`.
 
 === "OpenAI-compatible"
 
-    ```python
+    ```python { .model-swap }
     from openai import OpenAI
 
     client = OpenAI(
@@ -75,22 +77,22 @@ Ollama serves on port `11434`.
 
 === "Native API"
 
-    ```bash
+    ```bash { .model-swap }
     curl http://localhost:11434/api/generate -d '{
-      "model": "gemma4",
+      "model": "gemma4:12b",
       "prompt": "roses are red"
     }'
     ```
 
 === "Image via API"
 
-    ```bash
+    ```bash { .model-swap }
     IMG=$(base64 < test.jpg | tr -d '\n')
 
     curl -X POST http://localhost:11434/api/chat \
       -H "Content-Type: application/json" \
       -d '{
-        "model": "gemma4",
+        "model": "gemma4:12b",
         "messages": [{
           "role": "user",
           "content": "What is in this image?",
diff --git a/docs/deployment/qat.md b/docs/deployment/qat.md
index 558d195..3de2c14 100644
--- a/docs/deployment/qat.md
+++ b/docs/deployment/qat.md
@@ -1,6 +1,8 @@
 ---
 title: Quantization & QAT
 description: Which Gemma 4 quantized weights to download for each engine, and why QAT beats post-training quantization at 4-bit.
+models: [gemma-4]
+frameworks: [llamacpp, vllm]
 ---
 
 # Quantization and QAT
@@ -25,7 +27,7 @@ quality doing it.
 
 Names follow `google/gemma-4-{SIZE}-it{SUFFIX}`, for example:
 
-```bash
+```bash { .model-swap }
 # Local, via llama.cpp
 llama-cli -hf google/gemma-4-12B-it-qat-q4_0-gguf
 
diff --git a/docs/deployment/sglang.md b/docs/deployment/sglang.md
index 9efc389..bb67857 100644
--- a/docs/deployment/sglang.md
+++ b/docs/deployment/sglang.md
@@ -1,6 +1,8 @@
 ---
 title: SGLang
 description: Serve Gemma 4 with SGLang — install, launch_server, model repositories, and the OpenAI-compatible API.
+models: [gemma-4]
+frameworks: [sglang]
 ---
 
 # Serve Gemma with SGLang
@@ -17,7 +19,7 @@ uv pip install --prerelease=allow sglang
 
 ## Serve
 
-```bash
+```bash { .model-swap }
 python3 -m sglang.launch_server \
   --model-path google/gemma-4-12B-it \
   --host 0.0.0.0 \
@@ -39,7 +41,7 @@ For 4-bit serving, use the [QAT](qat.md) compressed-tensors builds,
 
 ## Call it
 
-```python
+```python { .model-swap }
 from openai import OpenAI
 
 client = OpenAI(api_key="EMPTY", base_url="http://localhost:30000/v1")
diff --git a/docs/deployment/vllm.md b/docs/deployment/vllm.md
index ba84000..6e656b7 100644
--- a/docs/deployment/vllm.md
+++ b/docs/deployment/vllm.md
@@ -1,6 +1,8 @@
 ---
 title: vLLM
 description: Serve Gemma 4 with vLLM — install, vllm serve, model repositories, and the OpenAI-compatible API.
+models: [gemma-4]
+frameworks: [vllm]
 ---
 
 # Serve Gemma with vLLM
@@ -17,7 +19,7 @@ uv pip install vllm --torch-backend=auto
 
 ## Serve
 
-```bash
+```bash { .model-swap }
 vllm serve google/gemma-4-12B-it
 ```
 
@@ -38,15 +40,17 @@ Drop `-it` for base weights, which you want only for further training.
 For 4-bit serving, use the [QAT](qat.md) compressed-tensors builds — 4-bit weights with 16-bit
 activations:
 
-```bash
+```bash { .model-fixed }
 vllm serve google/gemma-4-12B-it-qat-w4a16-ct
 ```
 
-Published as `google/gemma-4-{E2B,E4B,12B,31B}-it-qat-w4a16-ct`.
+Published as `google/gemma-4-{E2B,E4B,12B,31B}-it-qat-w4a16-ct`. This QAT example stays at
+12B because no 26B A4B compressed-tensors checkpoint is published; the page selector controls
+the regular serving and client examples above and below.
 
 ## Call it
 
-```python
+```python { .model-swap }
 from openai import OpenAI
 
 client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
diff --git a/docs/deployment/web.md b/docs/deployment/web.md
index 6872e36..43fc79e 100644
--- a/docs/deployment/web.md
+++ b/docs/deployment/web.md
@@ -1,3 +1,10 @@
+---
+title: Deploy Gemma in web browsers
+description: You can run Gemma models completely on-device with the MediaPipe LLM Inference API.
+models: [gemma-4]
+frameworks: [webgpu, transformers]
+source_url: "https://ai.google.dev/gemma/docs/integrations/web.md.txt"
+---
 # Deploy Gemma in web browsers
 
 You can run Gemma models completely on-device with the [MediaPipe LLM Inference
diff --git a/docs/get-started/capabilities/audio.md b/docs/get-started/capabilities/audio.md
index e9531df..d5d6344 100644
--- a/docs/get-started/capabilities/audio.md
+++ b/docs/get-started/capabilities/audio.md
@@ -1,3 +1,10 @@
+---
+title: Audio Understanding
+description: Starting with Gemma 3n, you can use audio directly into your prompts and workflows.
+models: [gemma-4, gemma-3n]
+requires: audio
+frameworks: [transformers]
+---
 
 # Audio Understanding
 
@@ -23,8 +30,8 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python
-MODEL_ID = "google/gemma-4-E2B-it" # @param ["google/gemma-4-E2B-it","google/gemma-4-E4B-it", "google/gemma-4-12B-it"]
+```python { .model-swap }
+MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import pipeline
 
diff --git a/docs/get-started/capabilities/function-calling.md b/docs/get-started/capabilities/function-calling.md
index 0594151..7603d5a 100644
--- a/docs/get-started/capabilities/function-calling.md
+++ b/docs/get-started/capabilities/function-calling.md
@@ -1,3 +1,9 @@
+---
+title: Tool calling
+description: When using a generative artificial intelligence (AI) model such as Gemma, you may want to use the model to operate programming interfaces in order to complete tasks or answer questions.
+models: [gemma-4]
+frameworks: [transformers]
+---
 <!-- TODO: Rename this to "tool calling"? -->
 # Tool calling
 
@@ -29,8 +35,8 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python
-MODEL_ID = "google/gemma-4-E2B-it" # @param ["google/gemma-4-E2B-it", "google/gemma-4-E4B-it", "google/gemma-4-12B-it", "google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it"]
+```python { .model-swap }
+MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import AutoProcessor, AutoModelForMultimodalLM
 
diff --git a/docs/get-started/capabilities/image.md b/docs/get-started/capabilities/image.md
index 8dcdcce..c00effe 100644
--- a/docs/get-started/capabilities/image.md
+++ b/docs/get-started/capabilities/image.md
@@ -1,3 +1,9 @@
+---
+title: Image Understanding
+description: You can use Gemma 3 and later models to analyze and understand the content of images.
+models: [gemma-4, gemma-3]
+frameworks: [transformers]
+---
 # Image Understanding
 
 You can use Gemma 3 and later models to analyze and understand the content of images. This capability includes tasks like describing image content, identifying objects, recognizing scenes, and even inferring the context from visual information.
@@ -21,8 +27,8 @@ pip install "transformers>=5.10.1"
 
 Use `transformers` library to load the pipeline
 
-```python
-MODEL_ID = "google/gemma-4-E2B-it" # @param ["google/gemma-4-E2B-it", "google/gemma-4-E4B-it", "google/gemma-4-12B-it", "google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it"]
+```python { .model-swap }
+MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import pipeline
 
diff --git a/docs/get-started/capabilities/mtp.md b/docs/get-started/capabilities/mtp.md
index ba79937..67353d0 100644
--- a/docs/get-started/capabilities/mtp.md
+++ b/docs/get-started/capabilities/mtp.md
@@ -1,3 +1,9 @@
+---
+title: Speed-up Gemma 4 with Multi-Token Prediction
+description: In Gemma 4, Multi-Token Prediction (MTP) is the specific architecture used to enable highly efficient Speculative Decoding.
+models: [gemma-4]
+source_url: "https://ai.google.dev/gemma/docs/mtp/overview.md.txt"
+---
 # Speed-up Gemma 4 with Multi-Token Prediction
 
 In Gemma 4, Multi-Token Prediction (MTP) is the specific architecture
diff --git a/docs/get-started/capabilities/text.md b/docs/get-started/capabilities/text.md
index 1c2bfe5..4e989d2 100644
--- a/docs/get-started/capabilities/text.md
+++ b/docs/get-started/capabilities/text.md
@@ -1,3 +1,9 @@
+---
+title: Inference
+description: "Basic text inference with Gemma 4 in Hugging Face Transformers: single-turn prompts, multi-turn conversations, and system instructions."
+models: [gemma-4]
+frameworks: [transformers]
+---
 # Inference
 
 Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the [Gemini](https://deepmind.google/technologies/gemini/#introduction) models. Gemma 4 is designed to be the world's most efficient open-weight model family.
@@ -26,8 +32,8 @@ pip install dialog
 
 Use `transformers` library to load the pipeline
 
-```python
-MODEL_ID = "google/gemma-4-E2B-it" # @param ["google/gemma-4-E2B-it", "google/gemma-4-E4B-it", "google/gemma-4-12B-it", "google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it"]
+```python { .model-swap }
+MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import pipeline
 
@@ -309,4 +315,4 @@ In this guide, you learned how to perform basic text inference with Gemma 4 usin
 
 - [Prompt and system instructions](../prompt-formatting.md)
 - [Function calling](function-calling.md)
-- [Run Gemma overview](../../deployment/index.md) -->
\ No newline at end of file
+- [Run Gemma overview](../../deployment/index.md) -->
diff --git a/docs/get-started/capabilities/thinking.md b/docs/get-started/capabilities/thinking.md
index 77f2c0a..cc20df2 100644
--- a/docs/get-started/capabilities/thinking.md
+++ b/docs/get-started/capabilities/thinking.md
@@ -1,3 +1,9 @@
+---
+title: Reasoning
+description: Use Gemma 4's thinking mode to reason before answering, for text and image prompts, and parse the reasoning apart from the answer.
+models: [gemma-4]
+frameworks: [transformers]
+---
 # Reasoning
 
 
@@ -22,8 +28,8 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python
-MODEL_ID = "google/gemma-4-E2B-it" # @param ["google/gemma-4-E2B-it", "google/gemma-4-E4B-it", "google/gemma-4-12B-it", "google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it"]
+```python { .model-swap }
+MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import AutoProcessor, AutoModelForMultimodalLM
 
diff --git a/docs/get-started/capabilities/video.md b/docs/get-started/capabilities/video.md
index 84b431f..f16ca9c 100644
--- a/docs/get-started/capabilities/video.md
+++ b/docs/get-started/capabilities/video.md
@@ -1,3 +1,9 @@
+---
+title: Video Understanding
+description: Video data is a rich source of information that can help you accomplish tasks and understand the world around you.
+models: [gemma-4]
+frameworks: [transformers]
+---
 # Video Understanding
 
 Video data is a rich source of information that can help you accomplish tasks and understand the world around you. Using Gemma with video data can help you understand spatial relationships, interpret human interactions, and assist with situational awareness. This tutorial shows you how to get started processing video data with Gemma using Hugging Face Transformers.
@@ -19,8 +25,8 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python
-MODEL_ID = "google/gemma-4-E2B-it" # @param ["google/gemma-4-E2B-it", "google/gemma-4-E4B-it", "google/gemma-4-12B-it", "google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it"]
+```python { .model-swap }
+MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import AutoProcessor, AutoModelForMultimodalLM
 
diff --git a/docs/get-started/capabilities/vision.md b/docs/get-started/capabilities/vision.md
index 63b08d8..9ba70d6 100644
--- a/docs/get-started/capabilities/vision.md
+++ b/docs/get-started/capabilities/vision.md
@@ -1,3 +1,9 @@
+---
+title: Vision understanding
+description: Prepare and pass visual data to Gemma 4 for object detection, OCR, visual question answering, captioning, and multi-image reasoning.
+models: [gemma-4, gemma-3, gemma-3n, paligemma]
+source_url: "https://ai.google.dev/gemma/docs/capabilities/vision.md.txt"
+---
 # Vision understanding
 
 Gemma 4, the latest model from the Gemma family, can perform a wide range of
diff --git a/docs/get-started/prompt-formatting.md b/docs/get-started/prompt-formatting.md
index a178104..b65e7a8 100644
--- a/docs/get-started/prompt-formatting.md
+++ b/docs/get-started/prompt-formatting.md
@@ -1,4 +1,7 @@
 ---
+title: Prompt Formatting
+description: The control tokens Gemma 4 uses to structure a prompt, and what each one is for.
+models: [gemma-4]
 hide:
   - toc
 ---
diff --git a/docs/get-started/prompt-structure.md b/docs/get-started/prompt-structure.md
index d13057b..a50bea0 100644
--- a/docs/get-started/prompt-structure.md
+++ b/docs/get-started/prompt-structure.md
@@ -1,3 +1,9 @@
+---
+title: Gemma formatting and system instructions
+description: Gemma instruction-tuned (IT) models are trained with a specific formatter that annotates all instruction tuning examples with extra information, both at training and inference time.
+models: [gemma-4]
+source_url: "https://ai.google.dev/gemma/docs/core/prompt-structure.md.txt"
+---
 # Gemma formatting and system instructions
 
 Gemma instruction-tuned (IT) models are trained with a specific *formatter* that
diff --git a/docs/get-started/quickstart.md b/docs/get-started/quickstart.md
index 36d0df0..2abef46 100644
--- a/docs/get-started/quickstart.md
+++ b/docs/get-started/quickstart.md
@@ -1,4 +1,8 @@
 ---
+title: Quick Start
+description: The Gemma family of open models includes a range of model sizes, capabilities, and task-specialized variations to help you build custom generative solutions.
+models: [gemma-4]
+frameworks: [transformers, ollama, mlx]
 hide:
   - toc
 ---
@@ -13,7 +17,7 @@ The Gemma family of open models includes a range of model sizes, capabilities, a
 
 | Size | Variant | Input ➔ Output | Context | Intended Platform |
 |---|---|---|---|---|
-{% for size in models.gemma4.sizes -%}
+{% for size in modeldata.gemma4.sizes -%}
 | **{{ size.name }}** | [Gemma 4 (core)](../models/gemma-4/index.md) | {{ size.modalities.input | map('capitalize') | join(', ') }} ➔ {{ size.modalities.output | map('capitalize') | join(', ') }} | {{ size.context }} | {{ size.platform }} |
 {% endfor %}
 
@@ -37,12 +41,12 @@ You can download Gemma models from:
 
     Then use:
 
-    ```python
+    ```python { .model-swap }
     from transformers import pipeline
 
     pipe = pipeline(
         "text-generation",
-        model="google/gemma-4-12b",
+        model="google/gemma-4-12B-it",
         device_map="auto"
     )
     output = pipe("Explain quantum computing simply:", max_new_tokens=150)
@@ -51,15 +55,15 @@ You can download Gemma models from:
 
 === "Ollama"
 
-    ```bash
+    ```bash { .model-swap }
     ollama run gemma4:12b "Explain quantum computing simply:"
     ```
 
 === "Apple MLX"
 
-    ```bash
+    ```bash { .model-swap }
     pip install mlx-lm
-    mlx_lm.generate --model mlx-community/gemma-4-12b-4bit \
+    mlx_lm.generate --model mlx-community/gemma-4-12B-it-qat-4bit \
         --prompt "Explain quantum computing simply:"
     ```
 
diff --git a/docs/index.md b/docs/index.md
index ea2544b..a1bce3b 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,12 +1,11 @@
 ---
-name: gemma-home
 title: Google Gemma 4 Open Models
 description: Official technical documentation portal for Google Gemma 4 open-weight AI models. Explore multimodal capabilities, interactive WebGPU playgrounds, and partner integrations.
-estimated_tokens: 200
+name: gemma-home
+frameworks: [pytorch, jax, keras, webgpu, llamacpp]
 prerequisites: []
-frameworks: [pytorch, jax, keras, webgpu, cpp]
-template: home.html
 hide:
   - navigation
   - toc
+template: home.html
 ---
diff --git a/docs/javascripts/model-data.js b/docs/javascripts/model-data.js
new file mode 100644
index 0000000..624b442
--- /dev/null
+++ b/docs/javascripts/model-data.js
@@ -0,0 +1,111 @@
+/* GENERATED FILE -- DO NOT EDIT.
+   Written by scripts/generate_model_names.py from data/models.yml.
+   Verified by: uv run python scripts/lint_docs.py --check generated
+
+   Consumed by docs/javascripts/model-switcher.js. A model with no id for an engine
+   has no published build there -- the switcher must leave such snippets alone
+   rather than invent a repository name. */
+window.GEMMA_MODELS = {
+  "gemma-4/e2b": {
+    "name": "E2B",
+    "fullName": "Gemma 4 E2B",
+    "context": "128K",
+    "platform": "Mobile devices",
+    "ids": {
+      "hf": "google/gemma-4-E2B-it",
+      "hf_base": "google/gemma-4-E2B",
+      "hf_qat": "google/gemma-4-E2B-it-qat-w4a16-ct",
+      "gguf": "ggml-org/gemma-4-E2B-it-GGUF",
+      "gguf_qat": "google/gemma-4-E2B-it-qat-q4_0-gguf",
+      "ollama": "gemma4:e2b",
+      "mlx": "mlx-community/gemma-4-E2B-it-qat-4bit"
+    },
+    "inputs": [
+      "text",
+      "image",
+      "audio",
+      "video"
+    ]
+  },
+  "gemma-4/e4b": {
+    "name": "E4B",
+    "fullName": "Gemma 4 E4B",
+    "context": "128K",
+    "platform": "Mobile and laptops",
+    "ids": {
+      "hf": "google/gemma-4-E4B-it",
+      "hf_base": "google/gemma-4-E4B",
+      "hf_qat": "google/gemma-4-E4B-it-qat-w4a16-ct",
+      "gguf": "ggml-org/gemma-4-E4B-it-GGUF",
+      "gguf_qat": "google/gemma-4-E4B-it-qat-q4_0-gguf",
+      "ollama": "gemma4:e4b",
+      "mlx": "mlx-community/gemma-4-E4B-it-qat-4bit"
+    },
+    "inputs": [
+      "text",
+      "image",
+      "audio",
+      "video"
+    ]
+  },
+  "gemma-4/12b": {
+    "name": "12B",
+    "fullName": "Gemma 4 12B",
+    "context": "256K",
+    "platform": "Laptops and desktops",
+    "ids": {
+      "hf": "google/gemma-4-12B-it",
+      "hf_base": "google/gemma-4-12B",
+      "hf_qat": "google/gemma-4-12B-it-qat-w4a16-ct",
+      "gguf": "ggml-org/gemma-4-12B-it-GGUF",
+      "gguf_qat": "google/gemma-4-12B-it-qat-q4_0-gguf",
+      "ollama": "gemma4:12b",
+      "mlx": "mlx-community/gemma-4-12B-it-qat-4bit"
+    },
+    "inputs": [
+      "text",
+      "image",
+      "audio",
+      "video"
+    ]
+  },
+  "gemma-4/26b-a4b": {
+    "name": "26B A4B",
+    "fullName": "Gemma 4 26B A4B",
+    "context": "256K",
+    "platform": "Desktops and servers",
+    "ids": {
+      "hf": "google/gemma-4-26B-A4B-it",
+      "hf_base": "google/gemma-4-26B-A4B",
+      "gguf": "ggml-org/gemma-4-26B-A4B-it-GGUF",
+      "gguf_qat": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf",
+      "ollama": "gemma4:26b",
+      "mlx": "mlx-community/gemma-4-26B-A4B-it-qat-4bit"
+    },
+    "inputs": [
+      "text",
+      "image",
+      "video"
+    ]
+  },
+  "gemma-4/31b": {
+    "name": "31B",
+    "fullName": "Gemma 4 31B",
+    "context": "256K",
+    "platform": "Servers",
+    "ids": {
+      "hf": "google/gemma-4-31B-it",
+      "hf_base": "google/gemma-4-31B",
+      "hf_qat": "google/gemma-4-31B-it-qat-w4a16-ct",
+      "gguf": "ggml-org/gemma-4-31B-it-GGUF",
+      "gguf_qat": "google/gemma-4-31B-it-qat-q4_0-gguf",
+      "ollama": "gemma4:31b",
+      "mlx": "mlx-community/gemma-4-31B-it-qat-4bit"
+    },
+    "inputs": [
+      "text",
+      "image",
+      "video"
+    ]
+  }
+};
diff --git a/docs/javascripts/model-switcher.js b/docs/javascripts/model-switcher.js
new file mode 100644
index 0000000..7aa03b6
--- /dev/null
+++ b/docs/javascripts/model-switcher.js
@@ -0,0 +1,241 @@
+/* Rewrite explicitly opted-in examples to a reader's preferred Gemma 4 model. */
+(() => {
+  "use strict";
+
+  const STORAGE_KEY = "gemma-model";
+  const QUERY_KEY = "model";
+  const models = window.GEMMA_MODELS || {};
+  const index = [];
+
+  for (const [id, model] of Object.entries(models)) {
+    for (const [engine, value] of Object.entries(model.ids || {})) {
+      index.push({ text: value, engine, model: id });
+    }
+  }
+  index.sort((a, b) => b.text.length - a.text.length);
+
+  const blocks = () => document.querySelectorAll(".model-swap code, code.model-swap");
+  const displayName = (id) => models[id]?.fullName || models[id]?.name || id;
+
+  const readStored = () => {
+    try {
+      return window.localStorage.getItem(STORAGE_KEY);
+    } catch {
+      return null;
+    }
+  };
+
+  const writeStored = (value) => {
+    try {
+      window.localStorage.setItem(STORAGE_KEY, value);
+    } catch {
+      // Storage may be disabled; the current page still works.
+    }
+  };
+
+  const readUrl = () => new URL(window.location.href).searchParams.get(QUERY_KEY);
+
+  const writeUrl = (value) => {
+    const url = new URL(window.location.href);
+    url.searchParams.set(QUERY_KEY, value);
+    window.history.replaceState(window.history.state, "", url);
+  };
+
+  const enginesInUse = () => {
+    const found = new Set();
+    for (const block of blocks()) {
+      for (const entry of index) {
+        if (block.textContent.includes(entry.text)) found.add(entry.engine);
+      }
+    }
+    return found;
+  };
+
+  const candidates = (requires) => {
+    const engines = [...enginesInUse()];
+    if (!engines.length) return [];
+    return Object.keys(models).filter((id) => {
+      const model = models[id];
+      if (requires && !(model.inputs || []).includes(requires)) return false;
+      return engines.every((engine) => (model.ids || {})[engine]);
+    });
+  };
+
+  const detect = () => {
+    for (const block of blocks()) {
+      const hit = index.find((entry) => block.textContent.includes(entry.text));
+      if (hit) return hit.model;
+    }
+    return null;
+  };
+
+  const rewriteNode = (node, target) => {
+    const original = node.__gemmaOriginal ?? node.nodeValue;
+    let next = original;
+    for (const entry of index) {
+      if (!next.includes(entry.text)) continue;
+      const replacement = models[target]?.ids?.[entry.engine];
+      if (replacement) next = next.split(entry.text).join(replacement);
+    }
+    if (next === node.nodeValue) return;
+    node.__gemmaOriginal = original;
+    node.nodeValue = next;
+  };
+
+  const apply = (target) => {
+    if (!models[target]) return;
+    for (const block of blocks()) {
+      const walker = document.createTreeWalker(block, NodeFilter.SHOW_TEXT);
+      let node;
+      while ((node = walker.nextNode())) rewriteNode(node, target);
+    }
+  };
+
+  const closePicker = (picker, returnFocus = false) => {
+    const trigger = picker.querySelector(".page-context__picker-trigger");
+    const menu = picker.querySelector(".page-context__picker-menu");
+    trigger.setAttribute("aria-expanded", "false");
+    menu.hidden = true;
+    if (returnFocus) trigger.focus();
+  };
+
+  const choose = (picker, target) => {
+    const trigger = picker.querySelector(".page-context__picker-trigger");
+    trigger.querySelector(".page-context__picker-name").textContent = displayName(target);
+    trigger.dataset.gemmaModel = target;
+    trigger.setAttribute("aria-label", `Model used by this page's examples: ${displayName(target)}`);
+    for (const option of picker.querySelectorAll("[role=option]")) {
+      option.setAttribute("aria-selected", String(option.dataset.value === target));
+    }
+  };
+
+  const buildControl = (strip, available, initial, unavailable) => {
+    const item = document.createElement("span");
+    item.className = "page-context__item";
+
+    const label = document.createElement("span");
+    label.className = "page-context__label";
+    label.textContent = "Examples";
+
+    const picker = document.createElement("span");
+    picker.className = "page-context__picker";
+
+    const menuId = `gemma-model-menu-${Math.random().toString(36).slice(2, 9)}`;
+    const trigger = document.createElement("button");
+    trigger.type = "button";
+    trigger.className = "page-context__picker-trigger";
+    trigger.dataset.gemmaModel = initial;
+    trigger.setAttribute("aria-haspopup", "listbox");
+    trigger.setAttribute("aria-expanded", "false");
+    trigger.setAttribute("aria-controls", menuId);
+    trigger.innerHTML = '<span class="page-context__picker-name"></span>';
+
+    const menu = document.createElement("span");
+    menu.className = "page-context__picker-menu";
+    menu.id = menuId;
+    menu.setAttribute("role", "listbox");
+    menu.setAttribute("aria-label", "Model used by this page's examples");
+    menu.hidden = true;
+
+    if (unavailable && models[unavailable]) {
+      const notice = document.createElement("span");
+      notice.className = "page-context__picker-notice";
+      notice.textContent = `${displayName(unavailable)} isn't available for this page.`;
+      menu.appendChild(notice);
+    }
+
+    for (const id of available) {
+      const option = document.createElement("button");
+      option.type = "button";
+      option.className = "page-context__picker-option";
+      option.dataset.value = id;
+      option.setAttribute("role", "option");
+      option.innerHTML =
+        `<span class="page-context__picker-option-name">${displayName(id)}</span>` +
+        `<span class="page-context__picker-option-meta">${models[id].platform} · ${models[id].context} context</span>`;
+      option.addEventListener("click", () => {
+        choose(picker, id);
+        writeStored(id);
+        writeUrl(id);
+        apply(id);
+        closePicker(picker, true);
+      });
+      option.addEventListener("keydown", (event) => {
+        const options = [...menu.querySelectorAll("[role=option]")];
+        const current = options.indexOf(option);
+        const destinations = {
+          ArrowDown: options[(current + 1) % options.length],
+          ArrowUp: options[(current - 1 + options.length) % options.length],
+          Home: options[0],
+          End: options.at(-1),
+        };
+        if (destinations[event.key]) {
+          event.preventDefault();
+          destinations[event.key].focus();
+        } else if (event.key === "Escape") {
+          event.preventDefault();
+          closePicker(picker, true);
+        }
+      });
+      menu.appendChild(option);
+    }
+
+    trigger.addEventListener("click", () => {
+      const opening = menu.hidden;
+      document.querySelectorAll(".page-context__picker").forEach((other) => closePicker(other));
+      menu.hidden = !opening;
+      trigger.setAttribute("aria-expanded", String(opening));
+    });
+    trigger.addEventListener("keydown", (event) => {
+      if (event.key === "Escape") {
+        event.preventDefault();
+        closePicker(picker, true);
+        return;
+      }
+      if (event.key !== "ArrowDown" && event.key !== "ArrowUp") return;
+      event.preventDefault();
+      menu.hidden = false;
+      trigger.setAttribute("aria-expanded", "true");
+      const options = [...menu.querySelectorAll("[role=option]")];
+      (event.key === "ArrowDown" ? options[0] : options.at(-1))?.focus();
+    });
+
+    picker.append(trigger, menu);
+    item.append(label, picker);
+    strip.appendChild(item);
+    choose(picker, initial);
+  };
+
+  const init = () => {
+    const strip = document.querySelector(".page-context");
+    const requested = readUrl() || readStored();
+
+    if (!strip || document.querySelector("[data-gemma-model]")) return;
+
+    const available = candidates(strip.dataset.requires || "");
+    if (available.length < 2) {
+      if (requested && available.includes(requested)) apply(requested);
+      return;
+    }
+
+    const detected = detect();
+    const initial = available.includes(requested)
+      ? requested
+      : available.includes(detected)
+        ? detected
+        : available[0];
+
+    buildControl(strip, available, initial, requested && !available.includes(requested) ? requested : null);
+    if (requested && available.includes(requested)) apply(requested);
+  };
+
+  document.addEventListener("click", (event) => {
+    for (const picker of document.querySelectorAll(".page-context__picker")) {
+      if (!picker.contains(event.target)) closePicker(picker);
+    }
+  });
+
+  if (document.readyState === "loading") document.addEventListener("DOMContentLoaded", init);
+  else init();
+  if (window.document$?.subscribe) window.document$.subscribe(init);
+})();
diff --git a/docs/learn/business-email-assistant.md b/docs/learn/business-email-assistant.md
index c1fa431..6ef1289 100644
--- a/docs/learn/business-email-assistant.md
+++ b/docs/learn/business-email-assistant.md
@@ -1,3 +1,10 @@
+---
+title: Build a business email AI assistant with Gemma
+description: Handling customer inquiries, including emails, is a necessary part of running many businesses, but it can quickly get overwhelming.
+models: [gemma-2]
+frameworks: [keras, jax]
+source_url: "https://ai.google.dev/gemma/docs/business-email-assistant.md.txt"
+---
 # Build a business email AI assistant with Gemma
 
 <div class="video-embed">
diff --git a/docs/learn/personal-code-assistant.md b/docs/learn/personal-code-assistant.md
index 393fec4..af43fff 100644
--- a/docs/learn/personal-code-assistant.md
+++ b/docs/learn/personal-code-assistant.md
@@ -1,3 +1,10 @@
+---
+title: Build a personal AI coding assistant with Gemma
+description: Run a Gemma coding assistant on your own hardware, keeping your codebase local when connectivity, cost, or data security rule out hosted models.
+models: [gemma-2]
+frameworks: [keras, jax]
+source_url: "https://ai.google.dev/gemma/docs/personal-code-assistant.md.txt"
+---
 # Build a personal AI coding assistant with Gemma
 
 <div class="video-embed">
diff --git a/docs/models/datagemma/index.md b/docs/models/datagemma/index.md
index ae8ca47..4c7746d 100644
--- a/docs/models/datagemma/index.md
+++ b/docs/models/datagemma/index.md
@@ -1,3 +1,9 @@
+---
+title: DataGemma
+description: DataGemma is a research tool that lets users ask questions in plain language and receive answers based on publicly available statistical data in the Data Commons repository.
+models: [datagemma]
+source_url: "https://ai.google.dev/gemma/docs/datagemma.md.txt"
+---
 # DataGemma
 
 DataGemma is a research tool that lets users ask questions in plain language and receive
diff --git a/docs/models/diffusiongemma/explained.md b/docs/models/diffusiongemma/explained.md
index 41acf79..9288fe8 100644
--- a/docs/models/diffusiongemma/explained.md
+++ b/docs/models/diffusiongemma/explained.md
@@ -1,3 +1,9 @@
+---
+title: Diffusion in Text Generation Explained
+description: To understand DiffusionGemma, it helps to examine the core limitations of standard language models and how text-based diffusion differs.
+models: [diffusiongemma]
+source_url: "https://ai.google.dev/gemma/docs/diffusiongemma/explained.md.txt"
+---
 # Diffusion in Text Generation Explained
 
 To understand DiffusionGemma, it helps to examine the core limitations of
diff --git a/docs/models/diffusiongemma/index.md b/docs/models/diffusiongemma/index.md
index b7c581b..051e17c 100644
--- a/docs/models/diffusiongemma/index.md
+++ b/docs/models/diffusiongemma/index.md
@@ -1,3 +1,9 @@
+---
+title: DiffusionGemma model overview
+description: DiffusionGemma is an experimental open model that explores text diffusion, an exceptionally fast approach to text generation.
+models: [diffusiongemma]
+source_url: "https://ai.google.dev/gemma/docs/diffusiongemma.md.txt"
+---
 # DiffusionGemma model overview
 
 DiffusionGemma is an experimental open model that explores text diffusion, an
diff --git a/docs/models/diffusiongemma/inference.md b/docs/models/diffusiongemma/inference.md
index 9e7b9dc..54086fd 100644
--- a/docs/models/diffusiongemma/inference.md
+++ b/docs/models/diffusiongemma/inference.md
@@ -1,3 +1,10 @@
+---
+title: Generate output
+description: Generate text with DiffusionGemma using Hugging Face Transformers, including the GPU memory the model requires.
+models: [diffusiongemma]
+frameworks: [transformers]
+source_url: "https://ai.google.dev/gemma/docs/diffusiongemma/inference-diffusiongemma-with-hf.md.txt"
+---
 
 DiffusionGemma is an experimental open model that explores text diffusion, an exceptionally fast approach to text generation. Based on the 26B (4B active) Mixture-of-Experts (MoE) Gemma 4 architecture, DiffusionGemma generates tokens using discrete diffusion.
 
diff --git a/docs/models/diffusiongemma/model-card.md b/docs/models/diffusiongemma/model-card.md
index b407d19..c565484 100644
--- a/docs/models/diffusiongemma/model-card.md
+++ b/docs/models/diffusiongemma/model-card.md
@@ -1,3 +1,9 @@
+---
+title: DiffusionGemma model card
+description: DiffusionGemma is a generative model built by Google DeepMind.
+models: [diffusiongemma]
+source_url: "https://ai.google.dev/gemma/docs/diffusiongemma/model_card.md.txt"
+---
 # DiffusionGemma model card
 
 [Hugging Face](https://huggingface.co/google/diffusiongemma-26B-A4B-it) \|
diff --git a/docs/models/embeddinggemma/fine-tuning.md b/docs/models/embeddinggemma/fine-tuning.md
index 3cc6a95..a4f8727 100644
--- a/docs/models/embeddinggemma/fine-tuning.md
+++ b/docs/models/embeddinggemma/fine-tuning.md
@@ -1,3 +1,10 @@
+---
+title: Fine-tune EmbeddingGemma
+description: Fine-tuning helps close the gap between a model's general-purpose understanding and the specialized, high-performance accuracy that your application requires.
+models: [embeddinggemma]
+frameworks: [sentence-transformers, transformers]
+source_url: "https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers.md.txt"
+---
 # Fine-tune EmbeddingGemma
 
 
diff --git a/docs/models/embeddinggemma/index.md b/docs/models/embeddinggemma/index.md
index b0afee3..f03edce 100644
--- a/docs/models/embeddinggemma/index.md
+++ b/docs/models/embeddinggemma/index.md
@@ -1,3 +1,9 @@
+---
+title: EmbeddingGemma model overview
+description: EmbeddingGemma is a 308M parameter multilingual text embedding model based on Gemma 3.
+models: [embeddinggemma]
+source_url: "https://ai.google.dev/gemma/docs/embeddinggemma.md.txt"
+---
 # EmbeddingGemma model overview {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
diff --git a/docs/models/embeddinggemma/inference.md b/docs/models/embeddinggemma/inference.md
index 7f780a0..abab022 100644
--- a/docs/models/embeddinggemma/inference.md
+++ b/docs/models/embeddinggemma/inference.md
@@ -1,3 +1,10 @@
+---
+title: Generate Embeddings with Sentence Transformers
+description: EmbeddingGemma is a lightweight, open embedding model designed for fast, high-quality retrieval on everyday devices like mobile phones.
+models: [embeddinggemma]
+frameworks: [sentence-transformers, transformers]
+source_url: "https://ai.google.dev/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.md.txt"
+---
 # Generate Embeddings with Sentence Transformers
 
 
diff --git a/docs/models/embeddinggemma/model-card.md b/docs/models/embeddinggemma/model-card.md
index 64cf9eb..62755b6 100644
--- a/docs/models/embeddinggemma/model-card.md
+++ b/docs/models/embeddinggemma/model-card.md
@@ -1,3 +1,9 @@
+---
+title: EmbeddingGemma model card
+description: "Model card for EmbeddingGemma: embedding inputs and outputs, training data, evaluation results, and intended use."
+models: [embeddinggemma]
+source_url: "https://ai.google.dev/gemma/docs/embeddinggemma/model_card.md.txt"
+---
 # Model card
 
 **Model Page** : [EmbeddingGemma](index.md)
diff --git a/docs/models/functiongemma/fine-tuning.md b/docs/models/functiongemma/fine-tuning.md
index 66a1aa7..1124ec1 100644
--- a/docs/models/functiongemma/fine-tuning.md
+++ b/docs/models/functiongemma/fine-tuning.md
@@ -1,3 +1,10 @@
+---
+title: Fine-tuning with FunctionGemma
+description: This guide demonstrates how to fine-tune FunctionGemma for tool calling.
+models: [functiongemma]
+frameworks: [transformers, peft, trl]
+source_url: "https://ai.google.dev/gemma/docs/functiongemma/finetuning-with-functiongemma.md.txt"
+---
 # Fine-tuning with FunctionGemma
 
 
diff --git a/docs/models/functiongemma/formatting.md b/docs/models/functiongemma/formatting.md
index 0185874..5d0c94d 100644
--- a/docs/models/functiongemma/formatting.md
+++ b/docs/models/functiongemma/formatting.md
@@ -1,3 +1,9 @@
+---
+title: FunctionGemma formatting and best practices
+description: FunctionGemma is a specialized version of the Gemma 3 270M model, trained specifically for function calling (i.e., tool use).
+models: [functiongemma]
+source_url: "https://ai.google.dev/gemma/docs/functiongemma/formatting-and-best-practices.md.txt"
+---
 # FunctionGemma formatting and best practices
 
 FunctionGemma is a specialized version of the Gemma 3 270M model,
diff --git a/docs/models/functiongemma/full-sequence.md b/docs/models/functiongemma/full-sequence.md
index 94bea56..8b72a84 100644
--- a/docs/models/functiongemma/full-sequence.md
+++ b/docs/models/functiongemma/full-sequence.md
@@ -1,3 +1,10 @@
+---
+title: Full function calling sequence with FunctionGemma
+description: "The complete FunctionGemma tool-calling cycle: the model's call, the developer's execution, and the final response built from the tool's output."
+models: [functiongemma]
+frameworks: [transformers]
+source_url: "https://ai.google.dev/gemma/docs/functiongemma/full-function-calling-sequence-with-functiongemma.md.txt"
+---
 # Full function calling sequence with FunctionGemma
 
 
diff --git a/docs/models/functiongemma/index.md b/docs/models/functiongemma/index.md
index 832ba56..aa29448 100644
--- a/docs/models/functiongemma/index.md
+++ b/docs/models/functiongemma/index.md
@@ -1,3 +1,9 @@
+---
+title: FunctionGemma model overview
+description: FunctionGemma is a specialized version of our Gemma 3 270M model tuned for function calling.
+models: [functiongemma]
+source_url: "https://ai.google.dev/gemma/docs/functiongemma.md.txt"
+---
 # FunctionGemma model overview {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
diff --git a/docs/models/functiongemma/inference.md b/docs/models/functiongemma/inference.md
index 525a026..a265a96 100644
--- a/docs/models/functiongemma/inference.md
+++ b/docs/models/functiongemma/inference.md
@@ -1,3 +1,10 @@
+---
+title: Function calling with Hugging Face Transformers
+description: Call functions with FunctionGemma in Hugging Face Transformers, passing tools as manual JSON schemas or as raw Python functions.
+models: [functiongemma]
+frameworks: [transformers]
+source_url: "https://ai.google.dev/gemma/docs/functiongemma/function-calling-with-hf.md.txt"
+---
 # Function calling with Hugging Face Transformers
 
 
diff --git a/docs/models/functiongemma/model-card.md b/docs/models/functiongemma/model-card.md
index 66c3015..7ca4a0b 100644
--- a/docs/models/functiongemma/model-card.md
+++ b/docs/models/functiongemma/model-card.md
@@ -1,3 +1,9 @@
+---
+title: FunctionGemma model card
+description: "Model card for FunctionGemma: function-calling inputs and outputs, training data, evaluation results, and intended use."
+models: [functiongemma]
+source_url: "https://ai.google.dev/gemma/docs/functiongemma/model_card.md.txt"
+---
 # Model card
 
 **Model Page** : [FunctionGemma](index.md)
diff --git a/docs/models/gemma-1/index.md b/docs/models/gemma-1/index.md
index 04867db..4702222 100644
--- a/docs/models/gemma-1/index.md
+++ b/docs/models/gemma-1/index.md
@@ -1,3 +1,9 @@
+---
+title: Gemma model card
+description: "Model card for the first-generation Gemma text-to-text models: description, inputs and outputs, training data, and evaluation."
+models: [gemma-1]
+source_url: "https://ai.google.dev/gemma/docs/core/model_card.md.txt"
+---
 # Model card
 
 **Model Page** : [Gemma](index.md)
diff --git a/docs/models/gemma-2/index.md b/docs/models/gemma-2/index.md
index 3e1ec98..181b11e 100644
--- a/docs/models/gemma-2/index.md
+++ b/docs/models/gemma-2/index.md
@@ -1,3 +1,9 @@
+---
+title: Gemma 2 model card
+description: "Model card for Gemma 2 text-to-text models: description, inputs and outputs, training data, and evaluation."
+models: [gemma-2]
+source_url: "https://ai.google.dev/gemma/docs/core/model_card_2.md.txt"
+---
 # Model card
 
 **Model Page** : [Gemma](index.md)
diff --git a/docs/models/gemma-3/index.md b/docs/models/gemma-3/index.md
index ebafa79..386f71e 100644
--- a/docs/models/gemma-3/index.md
+++ b/docs/models/gemma-3/index.md
@@ -1,3 +1,9 @@
+---
+title: Gemma 3 model card
+description: "Model card for the multimodal Gemma 3 models: 128K context, 140+ languages, inputs and outputs, training data, and evaluation."
+models: [gemma-3]
+source_url: "https://ai.google.dev/gemma/docs/core/model_card_3.md.txt"
+---
 # Model card
 
 **Model Page** : [Gemma](index.md)
diff --git a/docs/models/gemma-3n/index.md b/docs/models/gemma-3n/index.md
index 7cb7945..324fbc6 100644
--- a/docs/models/gemma-3n/index.md
+++ b/docs/models/gemma-3n/index.md
@@ -1,3 +1,9 @@
+---
+title: Gemma 3n model overview
+description: Gemma 3n is a generative AI model optimized for use in everyday devices, such as phones, laptops, and tablets.
+models: [gemma-3n]
+source_url: "https://ai.google.dev/gemma/docs/gemma-3n.md.txt"
+---
 # Gemma 3n model overview {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
diff --git a/docs/models/gemma-3n/model-card.md b/docs/models/gemma-3n/model-card.md
index c4dfaf2..de1d0bc 100644
--- a/docs/models/gemma-3n/model-card.md
+++ b/docs/models/gemma-3n/model-card.md
@@ -1,3 +1,9 @@
+---
+title: Gemma 3n model card
+description: "Model card for Gemma 3n: on-device inputs and outputs, training data, evaluation results, and intended use."
+models: [gemma-3n]
+source_url: "https://ai.google.dev/gemma/docs/gemma-3n/model_card.md.txt"
+---
 # Model card
 
 **Model Page** : [Gemma 3n](index.md)
diff --git a/docs/models/gemma-4/index.md b/docs/models/gemma-4/index.md
index 7ac00d4..c2abefe 100644
--- a/docs/models/gemma-4/index.md
+++ b/docs/models/gemma-4/index.md
@@ -1,3 +1,9 @@
+---
+title: Gemma 4
+description: Gemma is a family of generative artificial intelligence models and you can use them in a wide variety of generation tasks, including question answering, summarization, and reasoning.
+models: [gemma-4]
+source_url: "https://ai.google.dev/gemma/docs/core.md.txt"
+---
 # Gemma 4 {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
diff --git a/docs/models/gemma-4/model-card.md b/docs/models/gemma-4/model-card.md
index a107427..be6c868 100644
--- a/docs/models/gemma-4/model-card.md
+++ b/docs/models/gemma-4/model-card.md
@@ -1,3 +1,9 @@
+---
+title: Model card
+description: Gemma is a family of open models built by Google DeepMind.
+models: [gemma-4]
+source_url: "https://ai.google.dev/gemma/docs/core/model_card_4.md.txt"
+---
 <div class="model-card-header" markdown>
 
 ![Gemma 4 Banner](../../assets/gemma-4/gemma4_banner.png)
diff --git a/docs/models/gemma-aps/index.md b/docs/models/gemma-aps/index.md
index 4e0214c..f6024fc 100644
--- a/docs/models/gemma-aps/index.md
+++ b/docs/models/gemma-aps/index.md
@@ -1,3 +1,9 @@
+---
+title: Gemma for abstractive proposition segmentation (APS)
+description: Abstractive proposition segmentation (APS) is an analysis technique that breaks down text information into component parts.
+models: [gemma-aps]
+source_url: "https://ai.google.dev/gemma/docs/gemma-aps.md.txt"
+---
 # Gemma for abstractive proposition segmentation (APS)
 
 ## Gemma-APS
diff --git a/docs/models/gemma-scope/index.md b/docs/models/gemma-scope/index.md
index c71a27a..73274de 100644
--- a/docs/models/gemma-scope/index.md
+++ b/docs/models/gemma-scope/index.md
@@ -1,3 +1,9 @@
+---
+title: Gemma Scope
+description: Gemma Scope 2 is a comprehensive, open suite of interpretability tools designed for the Gemma 3 model collection.
+models: [gemma-scope]
+source_url: "https://ai.google.dev/gemma/docs/gemma_scope.md.txt"
+---
 # Gemma Scope
 
 ## Analyze Gemma 3 with Gemma Scope 2
diff --git a/docs/models/index.md b/docs/models/index.md
index a69b61f..5382c78 100644
--- a/docs/models/index.md
+++ b/docs/models/index.md
@@ -1,3 +1,7 @@
+---
+title: Gemma Model Family
+description: The Gemma family of open models spans multiple generations and specialized variants.
+---
 # Gemma Model Family
 
 The Gemma family of open models spans multiple generations and specialized variants.
@@ -10,7 +14,7 @@ Gemma 4 spans four distinct architectures:
 
 | Variant | Parameters | Input | Context | Intended Platform |
 |---|---|---|---|---|
-{% for size in models.gemma4.sizes -%}
+{% for size in modeldata.gemma4.sizes -%}
 | **{{ size.name }}** | {{ size.parameters }} | {{ size.modalities.input | map('capitalize') | join(', ') }} | {{ size.context }} | {{ size.platform_short }} |
 {% endfor %}
 !!! note "Audio and video support"
diff --git a/docs/models/paligemma/fine-tuning.md b/docs/models/paligemma/fine-tuning.md
index c41f0dd..1f81259 100644
--- a/docs/models/paligemma/fine-tuning.md
+++ b/docs/models/paligemma/fine-tuning.md
@@ -1,3 +1,10 @@
+---
+title: Fine-tune PaliGemma with JAX and Flax
+description: This notebook shows how to fine-tune PaliGemma on a vision-language task with JAX.
+models: [paligemma]
+frameworks: [jax]
+source_url: "https://ai.google.dev/gemma/docs/paligemma/fine-tuning-paligemma.md.txt"
+---
 # Fine-tune PaliGemma with JAX and Flax
 
 
diff --git a/docs/models/paligemma/index.md b/docs/models/paligemma/index.md
index a9b2a2a..98ea6fe 100644
--- a/docs/models/paligemma/index.md
+++ b/docs/models/paligemma/index.md
@@ -1,3 +1,9 @@
+---
+title: PaliGemma
+description: PaliGemma 2 and PaliGemma are lightweight open vision-language models (VLM) inspired by PaLI-3, and based on open components like the SigLIP vision model and the Gemma language model.
+models: [paligemma]
+source_url: "https://ai.google.dev/gemma/docs/paligemma.md.txt"
+---
 # PaliGemma
 
 PaliGemma 2 and PaliGemma are lightweight open *vision-language models* (VLM)
diff --git a/docs/models/paligemma/inference.md b/docs/models/paligemma/inference.md
index 064e038..9d2d55d 100644
--- a/docs/models/paligemma/inference.md
+++ b/docs/models/paligemma/inference.md
@@ -1,3 +1,10 @@
+---
+title: Generate PaliGemma output with Keras
+description: PaliGemma models have multimodal capabilities, allowing you to generate output using both text and image input data.
+models: [paligemma]
+frameworks: [keras]
+source_url: "https://ai.google.dev/gemma/docs/paligemma/inference-with-keras.md.txt"
+---
 # Generate PaliGemma output with Keras
 
 
diff --git a/docs/models/paligemma/model-card-v1.md b/docs/models/paligemma/model-card-v1.md
index 45a9e19..0fae71f 100644
--- a/docs/models/paligemma/model-card-v1.md
+++ b/docs/models/paligemma/model-card-v1.md
@@ -1,3 +1,9 @@
+---
+title: PaliGemma model card
+description: PaliGemma is a versatile and lightweight vision-language model (VLM) inspired by PaLI-3 and based on open components such as the SigLIP vision model and the Gemma language model.
+models: [paligemma]
+source_url: "https://ai.google.dev/gemma/docs/paligemma/model-card.md.txt"
+---
 # Model card
 
 **Model page:** [PaliGemma](index.md)
diff --git a/docs/models/paligemma/model-card-v2.md b/docs/models/paligemma/model-card-v2.md
index fbfa02a..832c409 100644
--- a/docs/models/paligemma/model-card-v2.md
+++ b/docs/models/paligemma/model-card-v2.md
@@ -1,3 +1,9 @@
+---
+title: PaliGemma 2 model card
+description: PaliGemma 2 is an update of the PaliGemma vision-language model (VLM) which incorporates the capabilities of the Gemma 2 models.
+models: [paligemma]
+source_url: "https://ai.google.dev/gemma/docs/paligemma/model-card-2.md.txt"
+---
 # Model card
 
 **Model page:** [PaliGemma](index.md)
diff --git a/docs/models/paligemma/prompting.md b/docs/models/paligemma/prompting.md
index 660ad99..3752bf4 100644
--- a/docs/models/paligemma/prompting.md
+++ b/docs/models/paligemma/prompting.md
@@ -1,3 +1,9 @@
+---
+title: PaliGemma prompt and system instructions
+description: This page describes prompt formatting and system instructions for PaliGemma models.
+models: [paligemma]
+source_url: "https://ai.google.dev/gemma/docs/paligemma/prompt-system-instructions.md.txt"
+---
 # PaliGemma prompt and system instructions
 
 This page describes prompt formatting and system instructions for PaliGemma
diff --git a/docs/models/recurrentgemma/fine-tuning.md b/docs/models/recurrentgemma/fine-tuning.md
index 371bcf1..f09acd6 100644
--- a/docs/models/recurrentgemma/fine-tuning.md
+++ b/docs/models/recurrentgemma/fine-tuning.md
@@ -1,3 +1,10 @@
+---
+title: Fine-tuning using JAX and Flax
+description: Fine-tune RecurrentGemma 2B Instruct for English-French translation using JAX, Flax, and the MTNT noisy-text dataset.
+models: [recurrentgemma]
+frameworks: [jax]
+source_url: "https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_finetune.md.txt"
+---
 # Fine-tuning using JAX and Flax
 
 This tutorial demonstrates how to fine-tune the [RecurrentGemma](index.md) 2B Instruct model for an English-French translation task using [Google DeepMind's `recurrentgemma` library](https://github.com/google-deepmind/recurrentgemma), [JAX](https://jax.readthedocs.io) (a high-performance numerical computing library), [Flax](https://flax.readthedocs.io) (the JAX-based neural network library), [Chex](https://chex.readthedocs.io/en/latest/) (a library of utilities for writing reliable JAX code), [Optax](https://optax.readthedocs.io/en/latest/) (the JAX-based gradient processing and optimization library), and the [MTNT (Machine Translation of Noisy Text) dataset](https://arxiv.org/abs/1809.00388). Although Flax is not used directly in this notebook, Flax was used to create Gemma.
diff --git a/docs/models/recurrentgemma/index.md b/docs/models/recurrentgemma/index.md
index a9255aa..49acfe6 100644
--- a/docs/models/recurrentgemma/index.md
+++ b/docs/models/recurrentgemma/index.md
@@ -1,3 +1,9 @@
+---
+title: RecurrentGemma
+description: RecurrentGemma is an open model based on Griffin, a hybrid model architecture that mixes gated linear recurrences with local sliding window attention.
+models: [recurrentgemma]
+source_url: "https://ai.google.dev/gemma/docs/recurrentgemma.md.txt"
+---
 # RecurrentGemma
 
 RecurrentGemma is an open model based on
diff --git a/docs/models/recurrentgemma/inference.md b/docs/models/recurrentgemma/inference.md
index 0313669..42d5176 100644
--- a/docs/models/recurrentgemma/inference.md
+++ b/docs/models/recurrentgemma/inference.md
@@ -1,3 +1,10 @@
+---
+title: Inference with RecurrentGemma using JAX and Flax
+description: Run sampling and inference with RecurrentGemma 2B Instruct using Google DeepMind's recurrentgemma library and JAX.
+models: [recurrentgemma]
+frameworks: [jax]
+source_url: "https://ai.google.dev/gemma/docs/recurrentgemma/recurrentgemma_jax_inference.md.txt"
+---
 # Inference with RecurrentGemma using JAX and Flax
 
 
diff --git a/docs/models/recurrentgemma/model-card.md b/docs/models/recurrentgemma/model-card.md
index 4d427c1..dea3298 100644
--- a/docs/models/recurrentgemma/model-card.md
+++ b/docs/models/recurrentgemma/model-card.md
@@ -1,3 +1,9 @@
+---
+title: RecurrentGemma model card
+description: RecurrentGemma is a family of open language models built on a novel recurrent architecture developed at Google.
+models: [recurrentgemma]
+source_url: "https://ai.google.dev/gemma/docs/recurrentgemma/model_card.md.txt"
+---
 # Model card
 
 **Model page:** [RecurrentGemma](index.md)
diff --git a/docs/models/releases.md b/docs/models/releases.md
index b330c57..957039a 100644
--- a/docs/models/releases.md
+++ b/docs/models/releases.md
@@ -1,3 +1,8 @@
+---
+title: Releases
+description: Dated release history for every Gemma model, from Gemma 1 through Gemma 4 and the specialized variants.
+source_url: "https://ai.google.dev/gemma/docs/releases.md.txt"
+---
 This page documents releases for the Gemma family of models.
 
 ## June 3, 2026
diff --git a/docs/models/shieldgemma/index.md b/docs/models/shieldgemma/index.md
index 9234193..689d8a7 100644
--- a/docs/models/shieldgemma/index.md
+++ b/docs/models/shieldgemma/index.md
@@ -1,3 +1,9 @@
+---
+title: ShieldGemma
+description: ShieldGemma is a set of instruction tuned models for evaluating the safety of text and images against a set of defined safety policies.
+models: [shieldgemma]
+source_url: "https://ai.google.dev/gemma/docs/shieldgemma.md.txt"
+---
 # ShieldGemma {: #hidden-title style="display: none;" }
 
 <div class="video-embed">
diff --git a/docs/models/shieldgemma/model-card-v1.md b/docs/models/shieldgemma/model-card-v1.md
index 7c6b9cd..22b3086 100644
--- a/docs/models/shieldgemma/model-card-v1.md
+++ b/docs/models/shieldgemma/model-card-v1.md
@@ -1,3 +1,9 @@
+---
+title: ShieldGemma model card
+description: "Model card for ShieldGemma: safety-classification inputs and outputs, training data, evaluation results, and intended use."
+models: [shieldgemma]
+source_url: "https://ai.google.dev/gemma/docs/shieldgemma/model_card.md.txt"
+---
 # ShieldGemma model card
 
 **Model Page** : [ShieldGemma](index.md)
diff --git a/docs/models/shieldgemma/model-card-v2.md b/docs/models/shieldgemma/model-card-v2.md
index 0996807..b0d07d4 100644
--- a/docs/models/shieldgemma/model-card-v2.md
+++ b/docs/models/shieldgemma/model-card-v2.md
@@ -1,3 +1,9 @@
+---
+title: ShieldGemma 2 model card
+description: ShieldGemma 2 is a model trained on Gemma 3's 4B IT checkpoint for image safety classification across key categories that takes in images and outputs safety labels per policy.
+models: [shieldgemma]
+source_url: "https://ai.google.dev/gemma/docs/shieldgemma/model_card_2.md.txt"
+---
 # ShieldGemma model card
 
 **Model Page** : [ShieldGemma](index.md)
diff --git a/docs/stylesheets/gemma.css b/docs/stylesheets/gemma.css
index 3b893ce..e2db731 100644
--- a/docs/stylesheets/gemma.css
+++ b/docs/stylesheets/gemma.css
@@ -72,6 +72,9 @@
   --gemma-surface: var(--gemma-n0);
   --gemma-surface-sunken: var(--gemma-n50);
   --gemma-border: var(--gemma-n150);
+  /* One step darker than the hairline. Separators inside a row have to out-read the rule
+     that closes the row, or the row looks like a grid. Stripe uses the same relationship. */
+  --gemma-border-strong: var(--gemma-n200);
   /* n500 measures 4.46:1 on the n50 tint -- just under AA for the 12-13px text this is
      used for (table headers, nav, tabs). n600 clears it at 6.38:1. */
   --gemma-text-muted: var(--gemma-n600);
@@ -163,6 +166,7 @@
   --gemma-surface: #1c1d21;
   --gemma-surface-sunken: #17181c;
   --gemma-border: #303239;
+  --gemma-border-strong: #454852;
   --gemma-text-muted: var(--gemma-n300);
   --gemma-text-secondary: var(--gemma-n200);
 
diff --git a/docs/stylesheets/page-context.css b/docs/stylesheets/page-context.css
new file mode 100644
index 0000000..3e032ea
--- /dev/null
+++ b/docs/stylesheets/page-context.css
@@ -0,0 +1,175 @@
+/* Page facts and the optional example-model picker beneath the H1. */
+.page-context {
+  display: flex;
+  flex-wrap: wrap;
+  align-items: center;
+  gap: var(--gemma-space-4);
+  padding-bottom: var(--gemma-space-3);
+  margin-bottom: var(--gemma-space-5);
+  font-size: 0.75rem;
+  line-height: 1.4;
+  border-bottom: 1px solid var(--gemma-border);
+}
+
+.page-context__item {
+  position: relative;
+  display: inline-flex;
+  align-items: center;
+  gap: var(--gemma-space-2);
+}
+
+.page-context__item:not(:first-child)::before {
+  content: "";
+  width: 1px;
+  align-self: stretch;
+  margin-right: 10px;
+  background-color: var(--gemma-border-strong);
+}
+
+.page-context__label {
+  color: var(--gemma-text-muted);
+}
+
+.page-context__value {
+  font-weight: 600;
+  color: var(--gemma-text-secondary);
+}
+
+.page-context__value:not(:last-child)::after {
+  content: ",";
+  font-weight: 400;
+  color: var(--gemma-text-muted);
+}
+
+.md-typeset .page-context__value--link {
+  color: var(--gemma-text-secondary);
+  text-decoration: none;
+}
+
+.md-typeset .page-context__value--link:hover,
+.md-typeset .page-context__value--link:focus-visible {
+  color: var(--gemma-accent);
+  text-decoration: underline;
+}
+
+.page-context__picker {
+  position: relative;
+  display: inline-flex;
+}
+
+.page-context__picker-trigger {
+  position: relative;
+  display: inline-flex;
+  align-items: center;
+  padding: 0 1.25rem 0 var(--gemma-space-1);
+  font: inherit;
+  font-weight: 600;
+  color: var(--gemma-text-secondary);
+  background: transparent;
+  border: 0;
+  border-radius: var(--gemma-radius-sm);
+  box-shadow: inset 0 0 0 1px var(--gemma-border);
+  cursor: pointer;
+}
+
+/* Keep a comfortable pointer target without increasing the metadata row's line box. */
+.page-context__picker-trigger::before {
+  content: "";
+  position: absolute;
+  inset: -0.25rem;
+}
+
+.page-context__picker-trigger::after {
+  content: "";
+  position: absolute;
+  top: 50%;
+  right: 0.45rem;
+  width: 0.34em;
+  height: 0.34em;
+  border-right: 1px solid currentColor;
+  border-bottom: 1px solid currentColor;
+  transform: translateY(-70%) rotate(45deg);
+}
+
+.page-context__picker-trigger:hover,
+.page-context__picker-trigger[aria-expanded="true"] {
+  color: var(--gemma-text-primary);
+  background: var(--gemma-surface-sunken);
+  box-shadow: inset 0 0 0 1px var(--gemma-border-strong);
+}
+
+.page-context__picker-trigger:focus-visible,
+.page-context__picker-option:focus-visible {
+  outline: 2px solid var(--gemma-accent);
+  outline-offset: 2px;
+}
+
+.page-context__picker-menu {
+  position: absolute;
+  top: calc(100% + var(--gemma-space-2));
+  left: 0;
+  z-index: 20;
+  display: grid;
+  width: max-content;
+  min-width: 17rem;
+  max-width: min(22rem, calc(100vw - 2rem));
+  padding: var(--gemma-space-1);
+  background: var(--gemma-surface);
+  border: 1px solid var(--gemma-border-strong);
+  border-radius: var(--gemma-radius-lg);
+  box-shadow: var(--gemma-shadow-md);
+}
+
+.page-context__picker-menu[hidden] {
+  display: none;
+}
+
+.page-context__picker-option {
+  display: grid;
+  gap: 0.125rem;
+  width: 100%;
+  padding: var(--gemma-space-2) var(--gemma-space-3);
+  font: inherit;
+  color: var(--gemma-text-secondary);
+  text-align: left;
+  background: transparent;
+  border: 0;
+  border-radius: var(--gemma-radius-md);
+  cursor: pointer;
+}
+
+.page-context__picker-option:hover,
+.page-context__picker-option:focus-visible,
+.page-context__picker-option[aria-selected="true"] {
+  background: color-mix(in srgb, var(--gemma-accent) 10%, transparent);
+}
+
+.page-context__picker-option-name {
+  font-weight: 600;
+}
+
+.page-context__picker-option-meta,
+.page-context__picker-notice {
+  color: var(--gemma-text-muted);
+  font-size: 0.6875rem;
+}
+
+.page-context__picker-notice {
+  padding: var(--gemma-space-2) var(--gemma-space-3) var(--gemma-space-3);
+  margin-bottom: var(--gemma-space-1);
+  border-bottom: 1px solid var(--gemma-border);
+}
+
+@media screen and (max-width: 44.98em) {
+  .page-context {
+    gap: var(--gemma-space-3);
+  }
+
+  .page-context__item:not(:first-child)::before {
+    display: none;
+  }
+
+  .page-context__picker-menu {
+    min-width: min(17rem, calc(100vw - 2rem));
+  }
+}
diff --git a/docs/tools/index.md b/docs/tools/index.md
index 2f65f61..e2cc8c4 100644
--- a/docs/tools/index.md
+++ b/docs/tools/index.md
@@ -1,3 +1,7 @@
+---
+title: Tools
+description: Interactive tools for planning your Gemma deployment.
+---
 # Tools
 
 Interactive tools for planning your Gemma deployment.
diff --git a/docs/tools/model-selector.md b/docs/tools/model-selector.md
index 2407122..f8a312c 100644
--- a/docs/tools/model-selector.md
+++ b/docs/tools/model-selector.md
@@ -1,3 +1,7 @@
+---
+title: Model Selector
+description: Find the right Gemma model for your hardware and use case.
+---
 # Model Selector
 
 Find the right Gemma model for your hardware and use case.
diff --git a/docs/tools/vram-calculator.md b/docs/tools/vram-calculator.md
index 0202f4f..1f9fce7 100644
--- a/docs/tools/vram-calculator.md
+++ b/docs/tools/vram-calculator.md
@@ -1,3 +1,7 @@
+---
+title: (V)RAM Calculator
+description: Estimate memory requirements for running Gemma models.
+---
 # (V)RAM Calculator
 
 Estimate memory requirements for running Gemma models.
diff --git a/docs/tuning/distributed.md b/docs/tuning/distributed.md
index 7ca90a0..8e6fd07 100644
--- a/docs/tuning/distributed.md
+++ b/docs/tuning/distributed.md
@@ -1,3 +1,10 @@
+---
+title: Distributed tuning with Gemma using Keras
+description: Gemma is a family of lightweight, state-of-the-art open models built from research and technology used to create Google Gemini models.
+models: [gemma-1]
+frameworks: [jax, keras]
+source_url: "https://ai.google.dev/gemma/docs/core/distributed_tuning.md.txt"
+---
 # Distributed tuning with Gemma using Keras
 
 
diff --git a/docs/tuning/full-finetune.md b/docs/tuning/full-finetune.md
index 660b4c5..90929d1 100644
--- a/docs/tuning/full-finetune.md
+++ b/docs/tuning/full-finetune.md
@@ -1,3 +1,10 @@
+---
+title: Full Model Fine-Tune using Hugging Face Transformers
+description: This guide walks you through how to fine-tune Gemma on a mobile game NPC dataset using Hugging Face Transformers and TRL.
+models: [gemma-3]
+frameworks: [transformers, trl]
+source_url: "https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetune.md.txt"
+---
 # Full Model Fine-Tune using Hugging Face Transformers
 
 
diff --git a/docs/tuning/keras-lora.md b/docs/tuning/keras-lora.md
index e119ab9..c980b10 100644
--- a/docs/tuning/keras-lora.md
+++ b/docs/tuning/keras-lora.md
@@ -1,3 +1,10 @@
+---
+title: Fine-tune Gemma in Keras using LoRA
+description: Generative artificial intelligent (AI) models like Gemma are effective at a variety of tasks.
+models: [gemma-3]
+frameworks: [keras]
+source_url: "https://ai.google.dev/gemma/docs/core/lora_tuning.md.txt"
+---
 # Fine-tune Gemma in Keras using LoRA
 
 
diff --git a/docs/tuning/mobile-actions.md b/docs/tuning/mobile-actions.md
index b2136f7..27bbe19 100644
--- a/docs/tuning/mobile-actions.md
+++ b/docs/tuning/mobile-actions.md
@@ -1,3 +1,10 @@
+---
+title: Fine-tune FunctionGemma 270M for Mobile Actions
+description: Mobile Actions is a demo app where users can trigger actions on their device from voice or text input.
+models: [functiongemma]
+frameworks: [transformers, litert, android]
+source_url: "https://ai.google.dev/gemma/docs/mobile-actions.md.txt"
+---
 # Fine-tune FunctionGemma 270M for Mobile Actions
 
 Mobile Actions is a demo app where users can trigger actions on their device
diff --git a/docs/tuning/overview.md b/docs/tuning/overview.md
index 9e7c752..6dc7b1a 100644
--- a/docs/tuning/overview.md
+++ b/docs/tuning/overview.md
@@ -1,3 +1,9 @@
+---
+title: Gemma model fine-tuning
+description: Fine-tuning a generative artificial intelligence (AI) model such as Gemma modifies the behavior of the model.
+models: [gemma-4]
+source_url: "https://ai.google.dev/gemma/docs/tune.md.txt"
+---
 # Gemma model fine-tuning
 
 Fine-tuning a generative artificial intelligence (AI) model such as
diff --git a/docs/tuning/qlora.md b/docs/tuning/qlora.md
index fb91bdf..793ca92 100644
--- a/docs/tuning/qlora.md
+++ b/docs/tuning/qlora.md
@@ -1,3 +1,10 @@
+---
+title: Fine-Tune Gemma using Hugging Face Transformers and QloRA
+description: This guide walks you through how to fine-tune Gemma on a custom text-to-sql dataset using Hugging Face Transformers and TRL.
+models: [gemma-4]
+frameworks: [transformers, peft, trl]
+source_url: "https://ai.google.dev/gemma/docs/core/huggingface_text_finetune_qlora.md.txt"
+---
 # Fine-Tune Gemma using Hugging Face Transformers and QloRA
 
 
@@ -134,13 +141,13 @@ You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https:/
 
 The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration.
 
-```python
+```python { .model-swap }
 import torch
 from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig
 from peft import prepare_model_for_kbit_training
 
 # Hugging Face model id
-model_id = "google/gemma-4-E2B" # @param ["google/gemma-4-E2B","google/gemma-4-E4B","google/gemma-4-12B","google/gemma-4-31B","google/gemma-4-26B-A4B"] {"allow-input":true}
+model_id = "google/gemma-4-E2B"
 
 # Check if GPU supports bfloat16
 if torch.cuda.is_bf16_supported():
@@ -358,7 +365,7 @@ When using QLoRA, you only train adapters and not the full model. This means whe
     It requires more than 30GB of CPU Memory when you want to merge the adapter into the model. You can skip this and continue with Test Model Inference.
 
 
-```python
+```python { .model-swap }
 from transformers import AutoModelForMultimodalLM, AutoProcessor
 from peft import PeftModel
 
diff --git a/docs/tuning/spoken-language.md b/docs/tuning/spoken-language.md
index 1ca226d..a8ef5ba 100644
--- a/docs/tuning/spoken-language.md
+++ b/docs/tuning/spoken-language.md
@@ -1,3 +1,10 @@
+---
+title: Tasks in spoken languages with Gemma
+description: Using artificial intelligence (AI) technology in a specific spoken language is a critical need for many businesses to be able to use it effectively.
+models: [gemma-2]
+frameworks: [keras]
+source_url: "https://ai.google.dev/gemma/docs/spoken-language/task-specific-tuning.md.txt"
+---
 # Tasks in spoken languages with Gemma
 
 <div class="video-embed">
diff --git a/docs/tuning/vision-qlora.md b/docs/tuning/vision-qlora.md
index ebec298..ba40a45 100644
--- a/docs/tuning/vision-qlora.md
+++ b/docs/tuning/vision-qlora.md
@@ -1,3 +1,10 @@
+---
+title: Fine-Tune Gemma for Vision Tasks using Hugging Face Transformers and QLoRA
+description: This guide walks you through how to fine-tune Gemma on a custom image and text dataset for a vision task (generating product descriptions) using Hugging Face Transformers and TRL.
+models: [gemma-4]
+frameworks: [transformers, peft, trl]
+source_url: "https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora.md.txt"
+---
 # Fine-Tune Gemma for Vision Tasks using Hugging Face Transformers and QLoRA
 
 
@@ -165,13 +172,13 @@ You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https:/
 
 The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration.
 
-```python
+```python { .model-swap }
 import torch
 from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig
 from peft import prepare_model_for_kbit_training
 
 # Hugging Face model id
-model_id = "google/gemma-4-E2B" # @param ["google/gemma-4-E2B","google/gemma-4-E4B","google/gemma-4-12B","google/gemma-4-31B","google/gemma-4-26B-A4B"] {"allow-input":true}
+model_id = "google/gemma-4-E2B"
 
 # Check if GPU supports bfloat16
 if not torch.cuda.is_bf16_supported():
diff --git a/docs/tutorials/visual-guides/diffusiongemma.md b/docs/tutorials/visual-guides/diffusiongemma.md
index 079ddef..e0a20e8 100644
--- a/docs/tutorials/visual-guides/diffusiongemma.md
+++ b/docs/tutorials/visual-guides/diffusiongemma.md
@@ -1,6 +1,7 @@
 ---
 title: A Visual Guide to DiffusionGemma
 description: A Visual Guide to DiffusionGemma — a visual walkthrough.
+models: [diffusiongemma]
 source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-diffusiongemma
 ---
 
diff --git a/docs/tutorials/visual-guides/gemma-4-12b.md b/docs/tutorials/visual-guides/gemma-4-12b.md
index d46b218..cb7426e 100644
--- a/docs/tutorials/visual-guides/gemma-4-12b.md
+++ b/docs/tutorials/visual-guides/gemma-4-12b.md
@@ -1,6 +1,7 @@
 ---
 title: A Visual Guide to Gemma 4 12B
 description: A Visual Guide to Gemma 4 12B — a visual walkthrough.
+models: [gemma-4/12b]
 source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4-12b
 ---
 
diff --git a/docs/tutorials/visual-guides/gemma-4.md b/docs/tutorials/visual-guides/gemma-4.md
index 0bd3a26..fabd3e5 100644
--- a/docs/tutorials/visual-guides/gemma-4.md
+++ b/docs/tutorials/visual-guides/gemma-4.md
@@ -1,6 +1,7 @@
 ---
 title: A Visual Guide to Gemma 4
 description: A Visual Guide to Gemma 4 — a visual walkthrough.
+models: [gemma-4]
 source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4
 ---
 
diff --git a/docs/tutorials/visual-guides/mtp.md b/docs/tutorials/visual-guides/mtp.md
index 5c7e0fc..21f4001 100644
--- a/docs/tutorials/visual-guides/mtp.md
+++ b/docs/tutorials/visual-guides/mtp.md
@@ -1,6 +1,7 @@
 ---
 title: A Visual Guide to MTP
 description: How multi-token prediction and speculative decoding make Gemma 4 faster — drafter models, shared KV cache, and the clustered LM head.
+models: [gemma-4]
 source_url: https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-gemma-4
 ---
 
diff --git a/overrides/partials/content.html b/overrides/partials/content.html
new file mode 100644
index 0000000..8ccae70
--- /dev/null
+++ b/overrides/partials/content.html
@@ -0,0 +1,27 @@
+{#-
+  Overrides the theme's content partial for one reason: the page context strip belongs
+  *below* the H1, the way docs.stripe.com places its page-level controls (measured: their
+  control row sits ~60px under the title, with nothing at all between breadcrumb and title).
+
+  The H1 comes out of rendered markdown, so the only way to get beneath it is to split the
+  content on the first `</h1>`. Autoescaping is off in this theme, so the two halves emit as
+  raw HTML -- verified before relying on it.
+
+  Everything else is the theme's own content.html, kept in step with it.
+-#}
+{% include "partials/actions.html" %}
+{% if "<h1" not in page.content %}
+  {#- No heading of its own: Zensical synthesises one from `title`. -#}
+  <h1 id="__skip">{{ page.title | d(config.site_name, true) }}</h1>
+  {% include "partials/page-context.html" %}
+  {{ page.content }}
+{% else %}
+  {% set parts = page.content | split("</h1>", 1) | list %}
+  {{ parts[0] }}</h1>
+  {% include "partials/page-context.html" %}
+  {{ parts[1] }}
+{% endif %}
+{% include "partials/tags.html" %}
+{% include "partials/source-file.html" %}
+{% include "partials/feedback.html" %}
+{% include "partials/comments.html" %}
diff --git a/overrides/partials/model-names.html b/overrides/partials/model-names.html
new file mode 100644
index 0000000..01f867c
--- /dev/null
+++ b/overrides/partials/model-names.html
@@ -0,0 +1,92 @@
+{#-
+  GENERATED FILE -- DO NOT EDIT.
+
+  Written by scripts/generate_model_names.py from data/models.yml.
+  Verified by: uv run python scripts/lint_docs.py --check generated
+
+  `gemma_model_names` maps every family, size and framework id to its display name.
+  `gemma_model_urls` maps a family id to its canonical local documentation page.
+  `gemma_framework_urls` maps a tool id to its project home.
+
+  No size lists here: the strip shows the page's subject, and the size selector is built in
+  JavaScript from what the page's marked code blocks can actually be rewritten to.
+-#}
+{% set gemma_model_names = {
+  "gemma-4"              : "Gemma 4",
+  "gemma-3n"             : "Gemma 3n",
+  "diffusiongemma"       : "DiffusionGemma",
+  "functiongemma"        : "FunctionGemma",
+  "embeddinggemma"       : "EmbeddingGemma",
+  "paligemma"            : "PaliGemma",
+  "shieldgemma"          : "ShieldGemma",
+  "gemma-3"              : "Gemma 3",
+  "gemma-2"              : "Gemma 2",
+  "gemma-1"              : "Gemma",
+  "recurrentgemma"       : "RecurrentGemma",
+  "datagemma"            : "DataGemma",
+  "gemma-scope"          : "Gemma Scope",
+  "gemma-aps"            : "Gemma APS",
+  "gemma-4/e2b"          : "E2B",
+  "gemma-4/e4b"          : "E4B",
+  "gemma-4/12b"          : "12B",
+  "gemma-4/26b-a4b"      : "26B A4B",
+  "gemma-4/31b"          : "31B",
+  "ollama"               : "Ollama",
+  "llamacpp"             : "llama.cpp",
+  "lmstudio"             : "LM Studio",
+  "mlx"                  : "MLX",
+  "litert"               : "LiteRT",
+  "webgpu"               : "WebGPU",
+  "vllm"                 : "vLLM",
+  "sglang"               : "SGLang",
+  "google-cloud"         : "Google Cloud",
+  "transformers"         : "Hugging Face Transformers",
+  "keras"                : "Keras",
+  "jax"                  : "JAX",
+  "pytorch"              : "PyTorch",
+  "sentence-transformers": "Sentence Transformers",
+  "peft"                 : "PEFT",
+  "trl"                  : "TRL",
+  "langchain"            : "LangChain",
+  "android"              : "Android",
+  "ios"                  : "iOS",
+} %}
+
+{% set gemma_model_urls = {
+  "gemma-4"       : "/models/gemma-4/model-card/",
+  "gemma-3n"      : "/models/gemma-3n/model-card/",
+  "diffusiongemma": "/models/diffusiongemma/model-card/",
+  "functiongemma" : "/models/functiongemma/model-card/",
+  "embeddinggemma": "/models/embeddinggemma/model-card/",
+  "paligemma"     : "/models/paligemma/model-card-v2/",
+  "shieldgemma"   : "/models/shieldgemma/model-card-v2/",
+  "gemma-3"       : "/models/gemma-3/",
+  "gemma-2"       : "/models/gemma-2/",
+  "gemma-1"       : "/models/gemma-1/",
+  "recurrentgemma": "/models/recurrentgemma/model-card/",
+  "datagemma"     : "/models/datagemma/",
+  "gemma-scope"   : "/models/gemma-scope/",
+  "gemma-aps"     : "/models/gemma-aps/",
+} %}
+
+{% set gemma_framework_urls = {
+  "ollama"               : "https://ollama.com",
+  "llamacpp"             : "https://github.com/ggml-org/llama.cpp",
+  "lmstudio"             : "https://lmstudio.ai",
+  "mlx"                  : "https://github.com/ml-explore/mlx",
+  "litert"               : "https://ai.google.dev/edge/litert",
+  "webgpu"               : "https://www.w3.org/TR/webgpu/",
+  "vllm"                 : "https://docs.vllm.ai",
+  "sglang"               : "https://docs.sglang.io",
+  "google-cloud"         : "https://cloud.google.com",
+  "transformers"         : "https://huggingface.co/docs/transformers",
+  "keras"                : "https://keras.io",
+  "jax"                  : "https://jax.readthedocs.io",
+  "pytorch"              : "https://pytorch.org",
+  "sentence-transformers": "https://sbert.net",
+  "peft"                 : "https://huggingface.co/docs/peft",
+  "trl"                  : "https://github.com/huggingface/trl",
+  "langchain"            : "https://python.langchain.com",
+  "android"              : "https://developer.android.com",
+  "ios"                  : "https://developer.apple.com/ios/",
+} %}
diff --git a/overrides/partials/page-context.html b/overrides/partials/page-context.html
new file mode 100644
index 0000000..0ee74c6
--- /dev/null
+++ b/overrides/partials/page-context.html
@@ -0,0 +1,43 @@
+{#- Page facts. model-switcher.js adds the Examples picker only when marked code can change.
+    Minijinja has no `.get()`; use `dict[key] | default(...)`. -#}
+{% include "partials/model-names.html" %}
+{%- set models = page.meta.models if page.meta and page.meta.models else [] -%}
+{%- set requires = page.meta.requires if page.meta and page.meta.requires else "" -%}
+{%- set frameworks = page.meta.frameworks if page.meta and page.meta.frameworks else [] -%}
+{%- set ns = namespace(families=[]) -%}
+{%- for id in models -%}
+  {%- set family = id | split("/") | first -%}
+  {%- if family not in ns.families -%}
+    {%- set ns.families = ns.families + [family] -%}
+  {%- endif -%}
+{%- endfor -%}
+{%- if models or frameworks %}
+<div class="page-context" data-requires="{{ requires }}">
+  {%- if ns.families %}
+  <span class="page-context__item">
+    <span class="page-context__label">Models</span>
+    {%- for family in ns.families %}
+    {%- set url = gemma_model_urls[family] | default("") %}
+    {%- if url %}
+    <a class="page-context__value page-context__value--link" href="{{ url }}">{{ gemma_model_names[family] | default(family) }}</a>
+    {%- else %}
+    <span class="page-context__value">{{ gemma_model_names[family] | default(family) }}</span>
+    {%- endif %}
+    {%- endfor %}
+  </span>
+  {%- endif %}
+  {%- if frameworks %}
+  <span class="page-context__item">
+    <span class="page-context__label">Uses</span>
+    {%- for id in frameworks %}
+    {%- set url = gemma_framework_urls[id] | default("") %}
+    {%- if url %}
+    <a class="page-context__value page-context__value--link" href="{{ url }}">{{ gemma_model_names[id] | default(id) }}</a>
+    {%- else %}
+    <span class="page-context__value">{{ gemma_model_names[id] | default(id) }}</span>
+    {%- endif %}
+    {%- endfor %}
+  </span>
+  {%- endif %}
+</div>
+{%- endif %}
diff --git a/scripts/backfill_frontmatter.py b/scripts/backfill_frontmatter.py
new file mode 100644
index 0000000..cd3c1f4
--- /dev/null
+++ b/scripts/backfill_frontmatter.py
@@ -0,0 +1,373 @@
+"""Fill in missing frontmatter across docs/ without touching a single line of body text.
+
+Every page is hand-maintained (PLAN.md, "Import once, then hand-maintain"), so this script
+is deliberately conservative:
+
+  * It never rewrites a key that already exists -- yours wins, always.
+  * It never touches the body. The block is rebuilt and the rest of the file is copied
+    through verbatim, which `--check` verifies.
+  * Existing keys keep their original raw lines, so nested structures (`cards:`, `hide:`)
+    and any comments survive exactly as written. Only the key *order* is normalised.
+
+Descriptions are derived from the page's opening paragraph. That is a starting point for
+review, not an authoring system -- run with `--report` to see which ones were guessed.
+
+    uv run python scripts/backfill_frontmatter.py --check     # report, write nothing
+    uv run python scripts/backfill_frontmatter.py             # write
+"""
+
+from __future__ import annotations
+
+import argparse
+import re
+import sys
+import tomllib
+from pathlib import Path
+
+import yaml
+
+from frontmatter_schema import (
+    DESCRIPTION_MAX,
+    DESCRIPTION_MIN,
+    DOCS_DIR,
+    REPO_ROOT,
+    SCHEMA,
+    THEME_KEYS,
+    models_with_modality,
+    ported_sources,
+)
+
+# Emission order: required keys first, then optional authored keys, then theme keys.
+KEY_ORDER = [f.name for f in SCHEMA] + sorted(THEME_KEYS)
+
+FENCE = re.compile(r"^\s*(```|~~~)")
+HEADING = re.compile(r"^#\s+(.*)$")
+ATTR_LIST = re.compile(r"\s*\{[^}]*\}\s*$")
+MD_LINK = re.compile(r"\[([^\]]*)\]\([^)]*\)")
+MD_EMPHASIS = re.compile(r"[*_`]+")
+HTML_TAG = re.compile(r"<[^>]+>")
+SENTENCE_END = re.compile(r"(?<=[.!?])\s+")
+
+# Lines that open a paragraph but are not prose.
+NON_PROSE_PREFIX = ("!!!", "???", "|", ">", "-", "*", "+", "<", "![", "=== ", ":", "{")
+
+
+# --- Frontmatter block surgery ----------------------------------------------------------------
+
+
+def split_block(text: str) -> tuple[list[str], str]:
+    """Return (frontmatter lines, rest-of-file). Rest is byte-identical to the input tail."""
+    if not text.startswith("---"):
+        return [], text
+    lines = text.split("\n")
+    for idx in range(1, len(lines)):
+        if lines[idx].strip() == "---":
+            return lines[1:idx], "\n".join(lines[idx + 1 :])
+    return [], text  # unterminated: treat it all as body
+
+
+def segment(block: list[str]) -> dict[str, list[str]]:
+    """Group a frontmatter block into {top-level key: its raw lines}.
+
+    Continuation lines (indented, blank) and preceding comments stay with their key, so a
+    nested `cards:` list round-trips exactly.
+    """
+    segments: dict[str, list[str]] = {}
+    pending: list[str] = []
+    current: str | None = None
+    for raw in block:
+        stripped = raw.strip()
+        is_top_level = raw[:1] not in (" ", "\t") and ":" in raw and not stripped.startswith("#")
+        if is_top_level:
+            current = raw.partition(":")[0].strip()
+            segments[current] = [*pending, raw]
+            pending = []
+        elif current is None or not stripped:
+            pending.append(raw)
+        else:
+            segments[current].append(raw)
+    return segments
+
+
+def parse(block: list[str]) -> dict:
+    try:
+        return yaml.safe_load("\n".join(block)) or {}
+    except yaml.YAMLError:
+        return {}
+
+
+def emit(key: str, value) -> list[str]:
+    if isinstance(value, list):
+        inline = ", ".join(str(v) for v in value)
+        return [f"{key}: [{inline}]"]
+    text = str(value)
+    if any(ch in text for ch in ':#"') or text != text.strip():
+        return [f'{key}: "{text}"']
+    return [f"{key}: {text}"]
+
+
+def render(segments: dict[str, list[str]], additions: dict[str, object]) -> str:
+    """Rebuild the block: known keys in canonical order, anything unrecognised last."""
+    lines: list[str] = []
+    ordered = [k for k in KEY_ORDER if k in segments or k in additions]
+    trailing = [k for k in segments if k not in KEY_ORDER]
+    for key in ordered + trailing:
+        lines.extend(segments[key] if key in segments else emit(key, additions[key]))
+    return "---\n" + "\n".join(lines) + "\n---\n"
+
+
+# --- Inference --------------------------------------------------------------------------------
+
+
+def strip_fences(lines: list[str]) -> list[str]:
+    """Blank out fenced blocks. Shell comments (`# Install PyTorch`) are not headings, and
+    reading them as one is how `diffusiongemma/inference.md` acquired the wrong title."""
+    out, in_fence = [], False
+    for line in lines:
+        if FENCE.match(line):
+            in_fence = not in_fence
+            out.append("")
+        else:
+            out.append("" if in_fence else line)
+    return out
+
+
+def infer_title(body: str, rel: str, nav_labels: dict[str, str]) -> str:
+    for line in strip_fences(body.split("\n")):
+        match = HEADING.match(line)
+        if match:
+            text = ATTR_LIST.sub("", match.group(1))
+            text = MD_EMPHASIS.sub("", MD_LINK.sub(r"\1", text)).strip()
+            if text:
+                return text
+    # No H1 on the page. The nav label is hand-curated, so it beats a filename guess.
+    return nav_labels.get(rel) or Path(rel).stem.replace("-", " ").capitalize()
+
+
+def lead_paragraphs(body: str, limit: int = 3) -> list[str]:
+    """The opening prose paragraphs, skipping fences, HTML embeds, images, tables, callouts.
+
+    Non-prose openers skip to the next blank line rather than advancing one line: an
+    `<iframe` spans several lines, and only its first one starts with `<`.
+    """
+    lines = strip_fences(body.split("\n"))
+    found: list[str] = []
+    idx = 0
+    while idx < len(lines) and len(found) < limit:
+        stripped = lines[idx].strip()
+        if not stripped or stripped.startswith("#"):
+            idx += 1
+            continue
+        if stripped.startswith(NON_PROSE_PREFIX):
+            while idx < len(lines) and lines[idx].strip():
+                idx += 1
+            continue
+        collected = []
+        while idx < len(lines) and lines[idx].strip():
+            collected.append(lines[idx].strip())
+            idx += 1
+        paragraph = " ".join(collected)
+        # Link rows ("Hugging Face | GitHub | Docs") and stray tag attributes are not prose.
+        if '="' not in paragraph and paragraph.count("|") < 2:
+            found.append(paragraph)
+    return found
+
+
+# Upstream pages open with a boilerplate "Gemma is a family of..." paragraph and only then
+# say what the page covers. That second paragraph is the description we actually want.
+SCOPE_OPENER = re.compile(r"^This (document|guide|page|tutorial|notebook|section)\b", re.I)
+
+
+def lead_paragraph(body: str) -> str:
+    paragraphs = lead_paragraphs(body)
+    for paragraph in paragraphs:
+        if SCOPE_OPENER.match(paragraph):
+            return paragraph
+    return paragraphs[0] if paragraphs else ""
+
+
+def infer_description(body: str, title: str) -> tuple[str, str]:
+    """Return (description, quality) where quality is "derived", "truncated" or "fallback"."""
+    paragraph = lead_paragraph(body)
+    paragraph = HTML_TAG.sub("", MD_LINK.sub(r"\1", paragraph))
+    paragraph = MD_EMPHASIS.sub("", paragraph).replace("\\|", "|").strip()
+
+    if paragraph:
+        sentences = SENTENCE_END.split(paragraph)
+        text = sentences[0].strip()
+        # A single short sentence says nothing useful; pull in the next one.
+        for extra in sentences[1:]:
+            if len(text) >= DESCRIPTION_MIN:
+                break
+            text = f"{text} {extra.strip()}".strip()
+        if len(text) > DESCRIPTION_MAX:
+            trimmed = text[: DESCRIPTION_MAX - 1].rsplit(" ", 1)[0].rstrip(",;:")
+            return trimmed + ".", "truncated"
+        if len(text) >= DESCRIPTION_MIN:
+            return text, "derived"
+
+    return f"Reference documentation for {title} in the Gemma model documentation.", "fallback"
+
+
+def infer_models(rel: str) -> list[str]:
+    """Which models a page is about. An empty list means "not about a model" -- community,
+    tooling and site pages, where naming a model would be noise."""
+    parts = rel.split("/")
+
+    # `models/<family>/…` names a family; `models/releases.md` sits at the top level and is
+    # about the family as a whole, so the directory check has to be real.
+    if parts[0] == "models" and len(parts) > 2:
+        return [parts[1]]
+
+    if rel == "tutorials/visual-guides/diffusiongemma.md":
+        return ["diffusiongemma"]
+    if rel == "tutorials/visual-guides/gemma-4-12b.md":
+        return ["gemma-4/12b"]
+
+    # Audio is E2B/E4B/12B only -- the large sparse models take text, image and video.
+    if rel == "get-started/capabilities/audio.md":
+        return list(models_with_modality("audio"))
+
+    if parts[0] in {"get-started", "deployment", "tuning", "tutorials", "learn"}:
+        return ["gemma-4"]
+
+    return []
+
+
+# Engines and libraries a page is *about*, keyed by filename. Pages that merely mention a
+# framework in passing are left blank rather than guessed at from body text.
+PATH_FRAMEWORKS: dict[str, list[str]] = {
+    "deployment/ollama.md": ["ollama"],
+    "deployment/llamacpp.md": ["llamacpp"],
+    "deployment/lmstudio.md": ["lmstudio"],
+    "deployment/mlx.md": ["mlx"],
+    "deployment/huggingface.md": ["transformers"],
+    "deployment/keras.md": ["keras"],
+    "deployment/litert.md": ["litert"],
+    "deployment/web.md": ["webgpu", "transformers"],
+    "deployment/mobile.md": ["litert", "android", "ios"],
+    "deployment/vllm.md": ["vllm"],
+    "deployment/sglang.md": ["sglang"],
+    "deployment/google-cloud.md": ["google-cloud", "vllm"],
+    "deployment/langchain.md": ["langchain", "ollama"],
+    "tuning/qlora.md": ["transformers", "peft", "trl"],
+    "tuning/full-finetune.md": ["transformers", "trl"],
+    "tuning/vision-qlora.md": ["transformers", "peft", "trl"],
+    "tuning/keras-lora.md": ["keras"],
+    "tuning/distributed.md": ["jax", "keras"],
+    "models/embeddinggemma/inference.md": ["sentence-transformers", "transformers"],
+    "models/embeddinggemma/fine-tuning.md": ["sentence-transformers"],
+    "models/functiongemma/inference.md": ["transformers"],
+    "models/functiongemma/fine-tuning.md": ["transformers", "peft", "trl"],
+    "models/paligemma/inference.md": ["keras"],
+    "models/paligemma/fine-tuning.md": ["jax"],
+    "models/recurrentgemma/inference.md": ["jax"],
+    "models/recurrentgemma/fine-tuning.md": ["jax"],
+    "models/diffusiongemma/inference.md": ["transformers"],
+}
+
+
+def nav_labels() -> dict[str, str]:
+    """docs-relative path -> the hand-written nav label, where zensical.toml gives one."""
+    config = tomllib.loads((REPO_ROOT / "zensical.toml").read_text(encoding="utf-8"))
+    labels: dict[str, str] = {}
+
+    def walk(node, label: str | None = None) -> None:
+        if isinstance(node, str):
+            if node.endswith(".md") and label and label not in {"Overview", "Index"}:
+                labels.setdefault(node, label)
+        elif isinstance(node, list):
+            # A label attached to a *list* names the section, not any page inside it, so it
+            # must not propagate -- that is how `models/releases.md` was titled "Models".
+            for item in node:
+                walk(item)
+        elif isinstance(node, dict):
+            for key, value in node.items():
+                walk(value, key)
+
+    walk(config["project"]["nav"])
+    return labels
+
+
+# --- Driver -----------------------------------------------------------------------------------
+
+
+def process(path: Path, labels: dict[str, str]) -> tuple[str, dict[str, object], str]:
+    """Return (new text, keys added, description quality)."""
+    text = path.read_text(encoding="utf-8")
+    rel = path.relative_to(DOCS_DIR).as_posix()
+    block, rest = split_block(text)
+    segments = segment(block)
+    existing = parse(block)
+
+    additions: dict[str, object] = {}
+    quality = ""
+
+    title = existing.get("title") or infer_title(rest, rel, labels)
+    if "title" not in existing:
+        additions["title"] = title
+
+    if "description" not in existing:
+        additions["description"], quality = infer_description(rest, title)
+
+    if "models" not in existing:
+        inferred = infer_models(rel)
+        if inferred:  # an empty list means "not about a model" -- omit the key entirely
+            additions["models"] = inferred
+
+    if "frameworks" not in existing and rel in PATH_FRAMEWORKS:
+        additions["frameworks"] = PATH_FRAMEWORKS[rel]
+
+    source = ported_sources().get(rel) or existing.get("source_url")
+    if source and "source_url" not in existing:
+        additions["source_url"] = source
+
+    if not additions:
+        return text, {}, ""
+
+    return render(segments, additions) + rest, additions, quality
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--check", action="store_true", help="report without writing")
+    args = parser.parse_args()
+
+    labels = nav_labels()
+    touched = 0
+    needs_review: list[tuple[str, str]] = []
+    seen_descriptions: dict[str, str] = {}
+
+    for path in sorted(DOCS_DIR.rglob("*.md")):
+        rel = path.relative_to(DOCS_DIR).as_posix()
+        original = path.read_text(encoding="utf-8")
+        new_text, additions, quality = process(path, labels)
+        if not additions:
+            continue
+
+        # The body must survive untouched -- this script only ever edits the block.
+        if split_block(new_text)[1] != split_block(original)[1]:
+            print(f"ABORT {rel}: body would change", file=sys.stderr)
+            return 1
+
+        touched += 1
+        description = str(additions.get("description", ""))
+        if quality in {"truncated", "fallback"}:
+            needs_review.append((rel, quality))
+        elif description and description in seen_descriptions:
+            needs_review.append((rel, f"duplicate of {seen_descriptions[description]}"))
+        elif description:
+            seen_descriptions[description] = rel
+
+        print(f"{'would add' if args.check else 'added':>9}  {rel}: {', '.join(additions)}")
+        if not args.check:
+            path.write_text(new_text, encoding="utf-8")
+
+    print(f"\n{touched} pages updated; {len(needs_review)} descriptions need a human")
+    for rel, why in needs_review:
+        print(f"  {rel:55} {why}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/check_fidelity.py b/scripts/check_fidelity.py
index 835624f..9f768df 100644
--- a/scripts/check_fidelity.py
+++ b/scripts/check_fidelity.py
@@ -120,10 +120,22 @@ def words(text: str) -> Counter:
     return Counter(t for t in tokens if len(t) >= 2)
 
 
+def strip_frontmatter(text: str) -> str:
+    """Drop the YAML block. It is metadata this repo adds (PLAN.md Step 3.1), never prose
+    from upstream, so counting it would report every page as having gained words."""
+    if not text.startswith("---"):
+        return text
+    lines = text.split("\n")
+    for idx in range(1, len(lines)):
+        if lines[idx].strip() == "---":
+            return "\n".join(lines[idx + 1 :])
+    return text
+
+
 def compare(source_text: str, converted_text: str) -> tuple[Counter, Counter]:
     """Return (unexpected drops, additions)."""
     source = words(strip_designed_drops(source_text))
-    converted = words(converted_text)
+    converted = words(strip_frontmatter(converted_text))
     dropped = Counter({w: c for w, c in (source - converted).items() if w not in EXPECTED_DROPS})
     return dropped, converted - source
 
diff --git a/scripts/frontmatter_schema.py b/scripts/frontmatter_schema.py
new file mode 100644
index 0000000..67ded69
--- /dev/null
+++ b/scripts/frontmatter_schema.py
@@ -0,0 +1,249 @@
+"""The frontmatter schema every page in docs/ must satisfy.
+
+Single source of truth. `lint_docs.py` enforces it; `backfill_frontmatter.py` writes it.
+Documented for humans in PLAN.md, "Frontmatter schema".
+
+Two kinds of key live here, and the distinction is the whole design:
+
+  AUTHORED   Facts only a person knows -- what the page is about, which models it applies
+             to, what you should read first. Hand-written, validated here.
+
+  COMPUTED   Facts derivable from the file itself -- token count, code languages, last
+             edit date. Hand-writing these guarantees they go stale, so authoring one is
+             an *error*. The build hook in PLAN.md Step 3.2 fills them in.
+
+Vocabularies are read from the repository rather than hardcoded, so adding a model
+directory or a size in data/models.yml extends the allowed values automatically.
+"""
+
+from __future__ import annotations
+
+import re
+import tomllib
+from dataclasses import dataclass
+from functools import cache
+from pathlib import Path
+
+import yaml
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DOCS_DIR = REPO_ROOT / "docs"
+MODELS_DATA = REPO_ROOT / "data" / "models.yml"
+FRAMEWORKS_DATA = REPO_ROOT / "data" / "frameworks.yml"
+SOURCES = Path(__file__).resolve().parent / "sources.toml"
+
+# Descriptions feed llms.txt, search results, and page cards. Too short says nothing;
+# too long gets truncated everywhere it is used.
+DESCRIPTION_MIN = 40
+DESCRIPTION_MAX = 200
+
+# `models` separates a family from a size within it: `gemma-4` is the whole family,
+# `gemma-4/12b` is one model in it. A bare `12b` would be ambiguous -- Gemma 3 and Gemma 4
+# both ship a 12B. `/` rather than `:` so the value stays a plain string in every YAML
+# parser; `[gemma-4:12b]` risks being read as a mapping in flow context.
+MODEL_SEPARATOR = "/"
+
+# Theme and authoring keys Zensical itself consumes. Allowed, not validated by us.
+THEME_KEYS = frozenset({"template", "hide", "cards", "search", "icon", "subtitle"})
+
+# Reserved for the Step 3.2 build hook. Authoring one is an error: a hand-written token
+# count is wrong the moment the page is edited.
+COMPUTED_KEYS = frozenset({"estimated_tokens", "code_languages", "last_updated"})
+
+URL = re.compile(r"^https?://\S+$")
+SLUG = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$")
+
+
+@dataclass(frozen=True)
+class Field:
+    name: str
+    required: bool
+    kind: str  # "str" | "list" | "date"
+    doc: str
+
+
+SCHEMA: tuple[Field, ...] = (
+    Field("title", True, "str", "Page title. Rendered as the H1 when the page has none."),
+    Field("description", True, "str", "One sentence. Feeds llms.txt, search, and cards."),
+    Field("models", False, "list", "Models the page is about. Omit when it is about none."),
+    Field("requires", False, "str", "Input modality the page needs, narrowing `models`."),
+    Field("name", False, "str", "Stable kebab-case id for agents to reference."),
+    Field("frameworks", False, "list", "Engines/libraries the page shows code for."),
+    Field("prerequisites", False, "list", "Pages to read first, as docs-relative paths."),
+    Field("source_url", False, "str", "Upstream page this was ported from."),
+)
+
+REQUIRED = tuple(f.name for f in SCHEMA if f.required)
+KNOWN = frozenset(f.name for f in SCHEMA) | THEME_KEYS
+
+
+@cache
+def model_families() -> frozenset[str]:
+    """Every family id. Derived from `docs/models/`, so adding a model directory is enough."""
+    return frozenset(p.name for p in (DOCS_DIR / "models").iterdir() if p.is_dir())
+
+
+@cache
+def model_vocabulary() -> frozenset[str]:
+    """Every accepted `models` value: each family, plus `gemma-4/<size>` for its sizes.
+
+    Only Gemma 4 has per-size data today. Other families are addressable at family level
+    until someone adds sizes for them to data/models.yml.
+    """
+    data = yaml.safe_load(MODELS_DATA.read_text(encoding="utf-8"))
+    sizes = {f"gemma-4{MODEL_SEPARATOR}{size['id']}" for size in data["gemma4"]["sizes"]}
+    return frozenset(model_families() | sizes)
+
+
+@cache
+def input_modalities() -> frozenset[str]:
+    """Every input a Gemma 4 size accepts -- the vocabulary for `requires`."""
+    data = yaml.safe_load(MODELS_DATA.read_text(encoding="utf-8"))
+    return frozenset(
+        modality
+        for size in data["gemma4"]["sizes"]
+        for modality in size["modalities"]["input"]
+    )
+
+
+@cache
+def frameworks() -> dict[str, dict]:
+    """id -> entry, from data/frameworks.yml."""
+    data = yaml.safe_load(FRAMEWORKS_DATA.read_text(encoding="utf-8"))
+    return {str(entry["id"]): entry for entry in data["frameworks"]}
+
+
+@cache
+def models_with_modality(modality: str) -> tuple[str, ...]:
+    """Gemma 4 sizes accepting a given input, e.g. audio -- E2B, E4B and 12B, not 31B."""
+    data = yaml.safe_load(MODELS_DATA.read_text(encoding="utf-8"))
+    return tuple(
+        f"gemma-4{MODEL_SEPARATOR}{size['id']}"
+        for size in data["gemma4"]["sizes"]
+        if modality in size["modalities"]["input"]
+    )
+
+
+@cache
+def ported_sources() -> dict[str, str]:
+    """docs-relative path -> upstream URL, from sources.toml."""
+    manifest = tomllib.loads(SOURCES.read_text(encoding="utf-8"))
+    return {entry["path"]: entry["source"] for entry in manifest["page"]}
+
+
+@dataclass
+class Problem:
+    key: str
+    message: str
+
+
+def _as_list(value) -> list | None:
+    return value if isinstance(value, list) else None
+
+
+def validate(frontmatter: dict, *, page_paths: set[str] | None = None) -> list[Problem]:
+    """Check one page's frontmatter. `page_paths` enables prerequisite resolution."""
+    problems: list[Problem] = []
+
+    for key in REQUIRED:
+        if key not in frontmatter:
+            problems.append(Problem(key, f"missing required key: {key}"))
+
+    for key in sorted(frontmatter):
+        if key in COMPUTED_KEYS:
+            problems.append(
+                Problem(key, f"`{key}` is computed at build time -- remove it from the page")
+            )
+        elif key not in KNOWN:
+            problems.append(Problem(key, f"unknown key: {key}"))
+
+    description = frontmatter.get("description")
+    if isinstance(description, str):
+        if not DESCRIPTION_MIN <= len(description) <= DESCRIPTION_MAX:
+            problems.append(
+                Problem(
+                    "description",
+                    f"description is {len(description)} chars; "
+                    f"want {DESCRIPTION_MIN}-{DESCRIPTION_MAX}",
+                )
+            )
+        if "](" in description or description.startswith("#"):
+            problems.append(Problem("description", "description contains markdown syntax"))
+    elif description is not None:
+        problems.append(Problem("description", "description must be a string"))
+
+    models = frontmatter.get("models")
+    if models is not None:
+        listed = _as_list(models)
+        if listed is None:
+            problems.append(Problem("models", "models must be a list"))
+        elif not listed:
+            problems.append(
+                Problem("models", "models is empty; omit the key when a page is about no model")
+            )
+        else:
+            vocabulary = model_vocabulary()
+            for value in listed:
+                text = str(value)
+                if text in vocabulary:
+                    continue
+                family, _, size = text.partition(MODEL_SEPARATOR)
+                if size and family in model_families():
+                    hint = f"{family} has no size {size!r} in data/models.yml"
+                elif size:
+                    hint = f"no family {family!r} in docs/models/"
+                elif any(v.endswith(f"{MODEL_SEPARATOR}{text}") for v in vocabulary):
+                    # A bare size: the most common mistake, and the one worth naming exactly.
+                    suggestion = next(v for v in sorted(vocabulary) if v.endswith(f"/{text}"))
+                    hint = f"a size needs its family -- did you mean {suggestion!r}?"
+                else:
+                    hint = "not a known family; see docs/models/ and data/models.yml"
+                problems.append(Problem("models", f"unknown model {text!r}: {hint}"))
+
+    requires = frontmatter.get("requires")
+    if requires is not None:
+        if not isinstance(requires, str):
+            problems.append(Problem("requires", "requires must be a single modality string"))
+        elif requires not in input_modalities():
+            known = ", ".join(sorted(input_modalities()))
+            problems.append(Problem("requires", f"unknown modality {requires!r}; expected {known}"))
+        elif not frontmatter.get("models"):
+            problems.append(
+                Problem("requires", "requires narrows `models`, so `models` must name a family")
+            )
+
+    listed_frameworks = frontmatter.get("frameworks")
+    if listed_frameworks is not None:
+        listed = _as_list(listed_frameworks)
+        if listed is None:
+            problems.append(Problem("frameworks", "frameworks must be a list"))
+        else:
+            known = frameworks()
+            for value in listed:
+                if str(value) not in known:
+                    problems.append(
+                        Problem(
+                            "frameworks",
+                            f"unknown framework: {value} (add it to data/frameworks.yml first)",
+                        )
+                    )
+
+    prerequisites = frontmatter.get("prerequisites")
+    if prerequisites is not None:
+        listed = _as_list(prerequisites)
+        if listed is None:
+            problems.append(Problem("prerequisites", "prerequisites must be a list"))
+        elif page_paths is not None:
+            for value in listed:
+                if str(value) not in page_paths:
+                    problems.append(Problem("prerequisites", f"no such page: {value}"))
+
+    name = frontmatter.get("name")
+    if name is not None and not SLUG.match(str(name)):
+        problems.append(Problem("name", f"name must be kebab-case: {name}"))
+
+    source_url = frontmatter.get("source_url")
+    if source_url is not None and not URL.match(str(source_url)):
+        problems.append(Problem("source_url", f"source_url must be an http(s) URL: {source_url}"))
+
+    return problems
diff --git a/scripts/generate_model_names.py b/scripts/generate_model_names.py
new file mode 100644
index 0000000..014550e
--- /dev/null
+++ b/scripts/generate_model_names.py
@@ -0,0 +1,153 @@
+"""Generate the Jinja lookup the page context header uses for model display names.
+
+Zensical exposes `page.meta` to templates but not the macros plugin's YAML data, and it has no
+hook mechanism to inject any. So the id -> display-name map is generated from data/models.yml
+into a template partial, and `lint_docs.py --check generated` fails if the two drift apart.
+
+That keeps data/models.yml the single source of truth: the partial is build output that happens
+to be committed, in the same spirit as .lintbaseline.json.
+
+    uv run python scripts/generate_model_names.py            # write
+    uv run python scripts/generate_model_names.py --check    # verify, write nothing
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path, PurePosixPath
+
+import yaml
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+MODELS_DATA = REPO_ROOT / "data" / "models.yml"
+FRAMEWORKS_DATA = REPO_ROOT / "data" / "frameworks.yml"
+TARGET = REPO_ROOT / "overrides" / "partials" / "model-names.html"
+
+# The same data, for the browser: the model switcher rewrites identifiers in opted-in code
+# blocks and needs the per-engine ids that only data/models.yml has.
+JS_TARGET = REPO_ROOT / "docs" / "javascripts" / "model-data.js"
+
+HEADER = """{#-
+  GENERATED FILE -- DO NOT EDIT.
+
+  Written by scripts/generate_model_names.py from data/models.yml.
+  Verified by: uv run python scripts/lint_docs.py --check generated
+
+  `gemma_model_names` maps every family, size and framework id to its display name.
+  `gemma_model_urls` maps a family id to its canonical local documentation page.
+  `gemma_framework_urls` maps a tool id to its project home.
+
+  No size lists here: the strip shows the page's subject, and the size selector is built in
+  JavaScript from what the page's marked code blocks can actually be rewritten to.
+-#}
+"""
+
+
+def _map(name: str, entries: list[tuple[str, str]]) -> str:
+    width = max((len(key) for key, _ in entries), default=0) + 2
+    lines = [f'  {(chr(34) + key + chr(34)):<{width}}: {value},' for key, value in entries]
+    return "{% set " + name + " = {\n" + "\n".join(lines) + "\n} %}\n"
+
+
+def _site_url(source: str) -> str:
+    """Convert a docs-relative Markdown source path to its clean site URL."""
+    path = PurePosixPath(source)
+    target = path.parent if path.name == "index.md" else path.with_suffix("")
+    return f"/{target.as_posix().strip('/')}/"
+
+
+def render() -> str:
+    data = yaml.safe_load(MODELS_DATA.read_text(encoding="utf-8"))
+    frameworks = yaml.safe_load(FRAMEWORKS_DATA.read_text(encoding="utf-8"))
+    sizes = data["gemma4"]["sizes"]
+
+    names: list[tuple[str, str]] = [
+        (str(family["id"]), f'"{family["name"]}"') for family in data["families"]
+    ]
+    names += [(f"gemma-4/{size['id']}", f'"{size["name"]}"') for size in sizes]
+    names += [
+        (str(entry["id"]), f'"{entry["name"]}"') for entry in frameworks["frameworks"]
+    ]
+
+    model_urls = [
+        (str(family["id"]), f'"{_site_url(str(family["page"]))}"')
+        for family in data["families"]
+        if family.get("page")
+    ]
+    framework_urls = [
+        (str(entry["id"]), f'"{entry["url"]}"')
+        for entry in frameworks["frameworks"]
+        if entry.get("url")
+    ]
+
+    return (
+        HEADER
+        + _map("gemma_model_names", names)
+        + "\n"
+        + _map("gemma_model_urls", model_urls)
+        + "\n"
+        + _map("gemma_framework_urls", framework_urls)
+    )
+
+
+def render_js() -> str:
+    """The switcher's data: per-engine identifiers, so it can rewrite a snippet's model."""
+    data = yaml.safe_load(MODELS_DATA.read_text(encoding="utf-8"))
+    payload = {
+        f"gemma-4/{size['id']}": {
+            "name": size["name"],
+            "fullName": size["full_name"],
+            "context": size["context"],
+            "platform": size["platform_short"],
+            "ids": size.get("ids", {}),
+            "inputs": size["modalities"]["input"],
+        }
+        for size in data["gemma4"]["sizes"]
+    }
+    body = json.dumps(payload, indent=2, sort_keys=False)
+    return (
+        "/* GENERATED FILE -- DO NOT EDIT.\n"
+        "   Written by scripts/generate_model_names.py from data/models.yml.\n"
+        "   Verified by: uv run python scripts/lint_docs.py --check generated\n\n"
+        "   Consumed by docs/javascripts/model-switcher.js. A model with no id for an engine\n"
+        "   has no published build there -- the switcher must leave such snippets alone\n"
+        "   rather than invent a repository name. */\n"
+        f"window.GEMMA_MODELS = {body};\n"
+    )
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--check", action="store_true", help="verify without writing")
+    args = parser.parse_args()
+
+    outputs = [(TARGET, render()), (JS_TARGET, render_js())]
+
+    if args.check:
+        stale = [
+            path
+            for path, expected in outputs
+            if not path.exists() or path.read_text(encoding="utf-8") != expected
+        ]
+        for path in stale:
+            print(
+                f"{path.relative_to(REPO_ROOT)} is stale -- "
+                "run: uv run python scripts/generate_model_names.py",
+                file=sys.stderr,
+            )
+        if stale:
+            return 1
+        print(f"{len(outputs)} generated file(s) up to date")
+        return 0
+
+    for path, expected in outputs:
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(expected, encoding="utf-8")
+        print(f"wrote {path.relative_to(REPO_ROOT)} ({expected.count(chr(10))} lines)")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index 212249e..85705ea 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -38,6 +38,11 @@
 from pathlib import Path
 from typing import Callable, Iterable, Iterator
 
+import yaml
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+import frontmatter_schema  # noqa: E402  (needs the path above)
+
 REPO_ROOT = Path(__file__).resolve().parent.parent
 DOCS_DIR = REPO_ROOT / "docs"
 CONFIG_FILE = REPO_ROOT / "zensical.toml"
@@ -51,10 +56,8 @@
 # Above this, something went wrong in conversion (usually inlined base64 images).
 MAX_FILE_BYTES = 60_000
 
-# Frontmatter keys required today. Phase 3.1 expands this to the full agent schema
-# (estimated_tokens, prerequisites, frameworks, code_languages, models, last_updated,
-# source_url, ported_at) -- add them here when that step lands.
-REQUIRED_FRONTMATTER = ("title", "description")
+# The frontmatter schema lives in scripts/frontmatter_schema.py so the linter and the
+# backfill script can never disagree about it. See PLAN.md, "Frontmatter schema".
 
 # The site being replaced. Any link here is a leak.
 UPSTREAM_DOCS = "ai.google.dev/gemma/docs"
@@ -152,8 +155,9 @@ def page_paths(self) -> set[str]:
 def parse_frontmatter(text: str) -> tuple[dict, bool, str, int]:
     """Split a YAML frontmatter block off the top of a document.
 
-    Returns (mapping, had_frontmatter, body, lines_consumed). Parsing is intentionally shallow:
-    we only need top-level scalar keys, so a full YAML dependency isn't warranted here.
+    Returns (mapping, had_frontmatter, body, lines_consumed). Parsed as real YAML: the schema
+    validates list-valued keys (`models`, `frameworks`), which a line-splitting parser reads
+    as the string "[gemma-4]".
     """
     if not text.startswith("---"):
         return {}, False, text, 0
@@ -161,15 +165,13 @@ def parse_frontmatter(text: str) -> tuple[dict, bool, str, int]:
     lines = text.splitlines()
     for idx in range(1, len(lines)):
         if lines[idx].strip() == "---":
-            block = lines[1:idx]
-            mapping: dict = {}
-            for raw in block:
-                # Top-level keys only -- indented lines belong to nested structures.
-                if not raw or raw[0].isspace() or raw.lstrip().startswith("#"):
-                    continue
-                if ":" in raw:
-                    key, _, value = raw.partition(":")
-                    mapping[key.strip()] = value.strip()
+            block = "\n".join(lines[1:idx])
+            try:
+                mapping = yaml.safe_load(block) or {}
+            except yaml.YAMLError as exc:
+                mapping = {"__error__": str(exc)}
+            if not isinstance(mapping, dict):
+                mapping = {"__error__": "frontmatter is not a mapping"}
             body = "\n".join(lines[idx + 1 :])
             return mapping, True, body, idx + 1
 
@@ -392,15 +394,49 @@ def check_stubs(ctx: Context) -> Iterable[Finding]:
             )
 
 
+def first_heading(page: Page) -> str | None:
+    """The page's own H1, or None. Fenced blocks are skipped -- a shell comment such as
+    `# Install PyTorch` is not a heading, and reading it as one mistitles the page."""
+    in_fence = False
+    for line in page.body.splitlines():
+        if line.lstrip().startswith(("```", "~~~")):
+            in_fence = not in_fence
+            continue
+        if in_fence or not line.startswith("# "):
+            continue
+        text = re.sub(r"\s*\{[^}]*\}\s*$", "", line[2:])
+        text = re.sub(r"\[([^\]]*)\]\([^)]*\)", r"\1", text)
+        text = re.sub(r"[*`_]", "", text).strip()
+        if text:
+            return text
+    return None
+
+
 def check_frontmatter(ctx: Context) -> Iterable[Finding]:
-    """Every page carries agent-readable frontmatter (see PLAN.md Phase 3.1)."""
+    """Every page carries agent-readable frontmatter (see PLAN.md, "Frontmatter schema")."""
     for page in ctx.pages:
         if not page.has_frontmatter:
             yield Finding("frontmatter", page.repo_rel, 0, "no frontmatter block")
             continue
-        for key in REQUIRED_FRONTMATTER:
-            if key not in page.frontmatter:
-                yield Finding("frontmatter", page.repo_rel, 0, f"missing required key: {key}")
+        if "__error__" in page.frontmatter:
+            yield Finding(
+                "frontmatter", page.repo_rel, 0, f"unparseable: {page.frontmatter['__error__']}"
+            )
+            continue
+        for problem in frontmatter_schema.validate(page.frontmatter, page_paths=ctx.page_paths):
+            yield Finding("frontmatter", page.repo_rel, 0, problem.message)
+
+        # A page with no H1 of its own gets one synthesised from `title` (Zensical emits
+        # `<h1 id="__skip">`). On those pages the title is visible content, not just metadata,
+        # so it has to say what the page already said. Where the page *has* an H1, the title
+        # only reaches the browser tab and llms.txt and may be more specific than the heading.
+        if first_heading(page) is None and "title" not in page.frontmatter:
+            yield Finding(
+                "frontmatter",
+                page.repo_rel,
+                0,
+                "page has no H1, so `title` is rendered as the heading -- it must be set",
+            )
 
 
 def check_orphans(ctx: Context) -> Iterable[Finding]:
@@ -458,6 +494,49 @@ def check_fences(ctx: Context) -> Iterable[Finding]:
             )
 
 
+def check_model_swap(ctx: Context) -> Iterable[Finding]:
+    """A swappable engine must not also appear in unacknowledged static code blocks."""
+    data = yaml.safe_load((REPO_ROOT / "data" / "models.yml").read_text(encoding="utf-8"))
+    engines: dict[str, set[str]] = {}
+    for size in data.get("gemma4", {}).get("sizes", []):
+        for engine, identifier in size.get("ids", {}).items():
+            engines.setdefault(str(engine), set()).add(str(identifier))
+
+    for page in ctx.pages:
+        blocks = list(iter_code_blocks(page))
+        marked = [(line, info, code) for line, info, code in blocks if ".model-swap" in info]
+        if not marked:
+            continue
+
+        active = {
+            engine
+            for _, _, code in marked
+            for engine, identifiers in engines.items()
+            if any(identifier in code for identifier in identifiers)
+        }
+        for line, info, code in blocks:
+            if ".model-swap" in info or ".model-fixed" in info or ".output" in info:
+                continue
+            for engine in active:
+                identifiers = engines[engine]
+                hits = [identifier for identifier in identifiers if identifier in code]
+                if not hits and any(":" in identifier for identifier in identifiers):
+                    prefixes = {identifier.split(":", 1)[0] for identifier in identifiers}
+                    hits = [
+                        prefix
+                        for prefix in prefixes
+                        if re.search(rf"(?<![\w/-]){re.escape(prefix)}(?=[:\s\"'])", code)
+                    ]
+                if hits:
+                    yield Finding(
+                        "model-swap",
+                        page.repo_rel,
+                        line,
+                        f"{engine} identifier '{sorted(hits)[0]}' is outside a .model-swap "
+                        "block -- mark it swappable or explicitly .model-fixed",
+                    )
+
+
 def check_admonitions(ctx: Context) -> Iterable[Finding]:
     """Devsite and GitHub callouts should both be Zensical admonitions."""
     for page in ctx.pages:
@@ -490,6 +569,14 @@ def check_size(ctx: Context) -> Iterable[Finding]:
             )
 
 
+def _figure(value) -> str | None:
+    """Render a memory figure the way the ported table writes it: 11.4, not 11.400000000001."""
+    if value is None:
+        return None
+    text = f"{float(value):g}"
+    return text
+
+
 def check_modeldata(ctx: Context) -> Iterable[Finding]:
     """Keep data/models.yml in step with the ported memory table it was copied from.
 
@@ -502,19 +589,16 @@ def check_modeldata(ctx: Context) -> Iterable[Finding]:
     if not data_file.exists() or not page_path.exists():
         return
 
-    # Minimal parse: `bf16: 11.4` style keys under each size's `memory:` block, in file order.
-    text = data_file.read_text(encoding="utf-8")
-    declared: list[tuple[str, dict[str, str]]] = []
-    current: str | None = None
-    for line in text.splitlines():
-        name = re.match(r"^\s*name:\s*(\S.*?)\s*$", line)
-        if name:
-            current = name.group(1)
-            declared.append((current, {}))
-            continue
-        figure = re.match(r"^\s*(bf16|sfp8|q4_0):\s*([\d.]+)\s*$", line)
-        if figure and declared:
-            declared[-1][1][figure.group(1)] = figure.group(2)
+    # Parsed as YAML, scoped to gemma4.sizes. An earlier line-scraper matched every `name:` in
+    # the file, so adding the `families:` block made it demand a memory row for "DataGemma".
+    data = yaml.safe_load(data_file.read_text(encoding="utf-8"))
+    declared: list[tuple[str, dict[str, str]]] = [
+        (
+            str(size["name"]),
+            {key: _figure(size.get("memory", {}).get(key)) for key in ("bf16", "sfp8", "q4_0")},
+        )
+        for size in data.get("gemma4", {}).get("sizes", [])
+    ]
 
     # Table rows look like: | Gemma 4 E2B | 11.4 GB | 5.7 GB | 2.9 GB | ...
     rows = {}
@@ -603,6 +687,30 @@ def check_css(ctx: Context) -> Iterable[Finding]:
                     )
 
 
+def check_generated(ctx: Context) -> Iterable[Finding]:
+    """Committed build output must match the data it was generated from.
+
+    Zensical has no hook mechanism, so the page context header's model display names are
+    generated from data/models.yml into a template partial. That partial is the one place in
+    the repo where model data could silently go stale, hence this check.
+    """
+    import generate_model_names
+
+    outputs = (
+        (generate_model_names.TARGET, generate_model_names.render()),
+        (generate_model_names.JS_TARGET, generate_model_names.render_js()),
+    )
+    for target, expected in outputs:
+        rel = target.relative_to(REPO_ROOT).as_posix()
+        if not target.exists():
+            yield Finding("generated", rel, 0, "missing -- run scripts/generate_model_names.py")
+        elif target.read_text(encoding="utf-8") != expected:
+            yield Finding(
+                "generated", rel, 0,
+                "stale vs data/models.yml -- run scripts/generate_model_names.py",
+            )
+
+
 CHECKS: dict[str, Callable[[Context], Iterable[Finding]]] = {
     "links": check_links,
     "images": check_images,
@@ -611,10 +719,12 @@ def check_css(ctx: Context) -> Iterable[Finding]:
     "frontmatter": check_frontmatter,
     "orphans": check_orphans,
     "fences": check_fences,
+    "model-swap": check_model_swap,
     "admonitions": check_admonitions,
     "size": check_size,
     "modeldata": check_modeldata,
     "css": check_css,
+    "generated": check_generated,
 }
 
 CHECK_HELP = {
@@ -625,10 +735,12 @@ def check_css(ctx: Context) -> Iterable[Finding]:
     "frontmatter": "missing or incomplete agent frontmatter",
     "orphans": "pages missing from nav, nav entries missing files",
     "fences": "prose or prompts captured as python code blocks",
+    "model-swap": "model identifiers omitted from a page's opt-in switching",
     "admonitions": "unconverted devsite Note:/Caution: callouts",
     "size": "files large enough to indicate inlined assets",
     "modeldata": "data/models.yml vs the ported memory table",
     "css": "design-system rules: tokens, registration, file size",
+    "generated": "committed build output that has drifted from its source data",
 }
 
 
diff --git a/zensical.toml b/zensical.toml
index 22c23aa..f2e64db 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -19,13 +19,21 @@ extra_css = [
   "stylesheets/nav.css",         # header, tabs, sidebar, table of contents
   "stylesheets/content.css",     # tables, admonitions, code, tabs
   "stylesheets/components.css",  # cards, badges, figures, steps, embeds
+  "stylesheets/page-context.css",# the applicability bar above every H1
   "stylesheets/tokens.css",      # chat-token badges
   "stylesheets/home.css",        # landing page: chrome, backdrop, hero
   "stylesheets/motion.css",      # transitions + reduced-motion opt-out
 ]
-# `include_yaml` exposes data/models.yml as `models.*` in page templates -- see data/models.yml
+# Order matters: the generated data must load before the switcher that reads it.
+extra_javascript = [
+  "javascripts/model-data.js",     # GENERATED from data/models.yml -- do not edit
+  "javascripts/model-switcher.js", # page context strip selector + code rewriting
+]
+# `include_yaml` exposes data/models.yml as `modeldata.*` in page templates. NOT `models.*`:
+# every page carries a `models:` frontmatter key, and page metadata shadows a macros variable
+# of the same name -- which silently broke both generated tables. See data/models.yml
 # for what reads it. Hand-editing a model table in a page is a bug; change the data file.
-plugins = { macros = { enabled = true, include_yaml = { models = "data/models.yml" } } }
+plugins = { macros = { enabled = true, include_yaml = { modeldata = "data/models.yml" } } }
 
 # ============================================================================
 # NAVIGATION

From 8e91225e8340ccddfe5759904f920983fe3bf242 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 10 Aug 2026 22:44:32 +0200
Subject: [PATCH 17/20] Colab button, llms.txt, pages as .md, embedded HF
 space, etc.

---
 .github/workflows/deploy.yml                  |   6 +-
 .github/workflows/lint.yml                    |   9 +-
 .lintbaseline.json                            |   7 +-
 PLAN.md                                       | 273 +++++---
 README.md                                     |  11 +-
 data/frameworks.yml                           |   4 +-
 docs/contributing/components.md               |  25 +
 docs/demos/concurrent.md                      |   7 -
 docs/demos/vision-token-budget.md             |  21 +
 docs/get-started/capabilities/audio.md        |  21 +-
 .../capabilities/function-calling.md          |  27 +-
 docs/get-started/capabilities/image.md        |  17 +-
 docs/get-started/capabilities/text.md         |  19 +-
 docs/get-started/capabilities/thinking.md     |  16 +-
 docs/get-started/capabilities/video.md        |   7 +-
 docs/javascripts/model-switcher.js            |   4 +-
 docs/learn/index.md                           |  28 +
 docs/llms.txt                                 | 117 ++++
 docs/notebooks/audio.ipynb                    | 393 +++++++++++
 docs/notebooks/full-finetune.ipynb            | 618 +++++++++++++++++
 docs/notebooks/function-calling.ipynb         | 593 +++++++++++++++++
 docs/notebooks/image.ipynb                    | 610 +++++++++++++++++
 docs/notebooks/qlora.ipynb                    | 623 +++++++++++++++++
 docs/notebooks/text.ipynb                     | 313 +++++++++
 docs/notebooks/thinking.ipynb                 | 319 +++++++++
 docs/notebooks/video.ipynb                    | 139 ++++
 docs/notebooks/vision-qlora.ipynb             | 626 ++++++++++++++++++
 docs/page-metadata.json                       | 545 +++++++++++++++
 docs/stylesheets/embeds.css                   |  25 +
 docs/stylesheets/page-context.css             |  12 +
 docs/tuning/full-finetune.md                  |  31 +-
 docs/tuning/qlora.md                          |  25 +-
 docs/tuning/vision-qlora.md                   |  25 +-
 overrides/partials/model-names.html           |   5 +
 overrides/partials/page-context.html          |  15 +-
 scripts/agent_docs.py                         | 246 +++++++
 scripts/build_docs.py                         |  24 +
 scripts/frontmatter_schema.py                 |  11 +-
 scripts/generate_model_names.py               |   8 +
 scripts/lint_docs.py                          |   8 +-
 scripts/notebooks.py                          | 188 ++++++
 scripts/test_agent_docs.py                    |  35 +
 scripts/test_notebooks.py                     |  87 +++
 zensical.toml                                 |   4 +-
 44 files changed, 5923 insertions(+), 224 deletions(-)
 delete mode 100644 docs/demos/concurrent.md
 create mode 100644 docs/demos/vision-token-budget.md
 create mode 100644 docs/learn/index.md
 create mode 100644 docs/llms.txt
 create mode 100644 docs/notebooks/audio.ipynb
 create mode 100644 docs/notebooks/full-finetune.ipynb
 create mode 100644 docs/notebooks/function-calling.ipynb
 create mode 100644 docs/notebooks/image.ipynb
 create mode 100644 docs/notebooks/qlora.ipynb
 create mode 100644 docs/notebooks/text.ipynb
 create mode 100644 docs/notebooks/thinking.ipynb
 create mode 100644 docs/notebooks/video.ipynb
 create mode 100644 docs/notebooks/vision-qlora.ipynb
 create mode 100644 docs/page-metadata.json
 create mode 100644 docs/stylesheets/embeds.css
 create mode 100644 scripts/agent_docs.py
 create mode 100644 scripts/build_docs.py
 create mode 100644 scripts/notebooks.py
 create mode 100644 scripts/test_agent_docs.py
 create mode 100644 scripts/test_notebooks.py

diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 790c15d..3e72079 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -30,15 +30,15 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
 
-      - name: Test notebook conversion
-        run: uv run --frozen python scripts/test_convert_notebook.py
+      - name: Script tests
+        run: uv run --frozen python -m unittest discover -s scripts -p "test_*.py"
 
       - name: Lint docs
         run: uv run --frozen python scripts/lint_docs.py --baseline
 
       - name: Build docs
         run: |
-          uv run --frozen zensical build
+          uv run --frozen python scripts/build_docs.py
 
       - name: Upload Pages artifact
         uses: actions/upload-pages-artifact@56afc609e74202658d3ffba0e8f6dda462b719fa # v3
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 8bfce56..827ca55 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -25,13 +25,12 @@ jobs:
         with:
           python-version: "3.12"
 
-      # The converter tests are stdlib-only (tomllib requires 3.11+). The linter needs PyYAML
-      # to validate frontmatter, which is list-valued and cannot be parsed by splitting lines.
+      # The generators use Jinja for raw Markdown and PyYAML for structured frontmatter.
       - name: Install linter dependencies
-        run: pip install pyyaml==6.0.3
+        run: pip install pyyaml==6.0.3 jinja2==3.1.6
 
-      - name: Converter golden tests
-        run: python scripts/test_convert_notebook.py
+      - name: Script tests
+        run: python -m unittest discover -s scripts -p "test_*.py"
 
       - name: Lint documentation
         run: python scripts/lint_docs.py --baseline
diff --git a/.lintbaseline.json b/.lintbaseline.json
index e9ede8f..3fc3365 100644
--- a/.lintbaseline.json
+++ b/.lintbaseline.json
@@ -8,18 +8,19 @@
     "frontmatter",
     "orphans",
     "fences",
+    "model-swap",
     "admonitions",
     "size",
     "modeldata",
-    "css"
+    "css",
+    "generated"
   ],
-  "count": 7,
+  "count": 6,
   "known": [
     "stubs|docs/community/discord.md|stub: 46 chars of content (minimum 400)",
     "stubs|docs/community/gemma-skills.md|stub: 62 chars of content (minimum 400)",
     "stubs|docs/community/gemmaverse.md|placeholder link: example.com",
     "stubs|docs/cookbook.md|stub: 395 chars of content (minimum 400)",
-    "stubs|docs/demos/concurrent.md|stub: 52 chars of content (minimum 400)",
     "stubs|docs/tools/index.md|stub: 144 chars of content (minimum 400)",
     "stubs|docs/tools/model-selector.md|stub: 58 chars of content (minimum 400)"
   ]
diff --git a/PLAN.md b/PLAN.md
index d88bb70..002366e 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -99,6 +99,9 @@ Local preview:
 uv run zensical serve
 ```
 
+Zensical's local server may download `.md` routes because it labels them as binary files. This
+is an accepted preview-only limitation; verify inline raw Markdown on the GitHub Pages deployment.
+
 ---
 
 ## Current status
@@ -107,11 +110,24 @@ uv run zensical serve
 |---|---|
 | 0 — Foundation | ✅ complete |
 | 1 — Design system | ✅ complete |
-| 2 — Navigation & content | 🟡 nav and Run section done; 7 stubs and 6 keep-or-drop decisions left |
-| 3 — Agent-first infrastructure | 🟡 3.1 done; 3.2–3.5 open |
-| 4–8 | not started |
+| 2 — Navigation & content | 🟡 nav and Run section done; 6 stubs and 6 keep-or-drop decisions left |
+| 3 — Agent-first infrastructure | ✅ POC complete: metadata, `llms.txt`, and raw Markdown routes |
+| 4 — Interactive context | 🟡 global model selection done; further tools deferred |
+| 5–6 — Showcase and Learn | ✅ narrow POC complete; broader rollout deferred |
+| 7–8 | deferred until after the POC |
+
+90 pages · 63 ported and frozen · 10 stylesheets · lint backlog **6** (all stubs).
+
+### POC finish line — in order
+
+1. Resolve the remaining Phase 2 content decisions and obvious stubs needed for a credible demo.
+2. ✅ Finish computed metadata and the Stripe-style `llms.txt` + raw Markdown surface (Steps 3.2–3.3).
+3. ✅ Add notebook launch actions to the exportable tutorials and one small task-first entry page
+   (Steps 6.2–6.3).
+4. ✅ Embed one HF Space with a plain-link fallback (Step 5.1).
 
-89 pages · 63 ported and frozen · 9 stylesheets · lint backlog **7** (all stubs).
+The persisted model selector is already the POC's interactive showcase. Everything explicitly
+marked deferred below is outside this finish line and must not block presenting the site.
 
 **Remaining Phase 2 work**, all blocked on content decisions rather than engineering:
 
@@ -121,7 +137,6 @@ uv run zensical serve
 | `community/discord.md` | port `/discord` |
 | `community/gemma-skills.md` | content — no upstream source |
 | `cookbook.md` | content decision |
-| `demos/concurrent.md` | content — the app lives in the cookbook repo |
 | `tools/index.md`, `tools/model-selector.md` | **Phase 4** — these *are* the interactive tools |
 
 **Upstream pages not ported (6):** `/core/gemma_library`, `/core/pytorch_gemma`,
@@ -189,8 +204,8 @@ stay as written ("Run Gemma with Ollama").
 Use `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in dark, rather
 than naming a brand colour directly.
 
-**Stylesheets** — 9 files, each under 250 lines, each opening with what it owns and what it must
-not touch. Adding a ninth is the right move when a new concern appears; growing one into a
+**Stylesheets** — 10 files, each under 250 lines, each opening with what it owns and what it must
+not touch. Adding a focused sheet is the right move when a new concern appears; growing one into a
 catch-all is not.
 
 | File | Owns |
@@ -204,6 +219,7 @@ catch-all is not.
 | `tokens.css` | Chat-token badges |
 | `home.css` | Landing page |
 | `motion.css` | Transitions + reduced-motion opt-out |
+| `embeds.css` | Responsive external demo frames |
 
 Enforced by `lint_docs.py --check css`: raw hex only in `gemma.css`, every sheet registered in
 `extra_css`, none over 250 lines, no `<style>` blocks in templates.
@@ -235,9 +251,10 @@ linter and the backfill script so they cannot disagree. Validated by
 | `prerequisites` | list | docs-relative paths; must resolve to real pages |
 | `name` | string | Stable kebab-case id for agents to reference |
 | `source_url` | string | Upstream page, from `scripts/sources.toml` |
+| `colab` | string | Generated notebook URL; renders a Colab link in the page context strip |
 
-**Reserved** for the Step 3.2 build hook and rejected if hand-written: `estimated_tokens`,
-`code_languages`, `last_updated`. Theme keys (`template`, `hide`, `cards`, …) pass through.
+**Reserved** for the Step 3.2 build hook and rejected if hand-written: `estimated_tokens` and
+`last_updated`. Theme keys (`template`, `hide`, `cards`, …) pass through.
 
 ### Authored vs computed
 
@@ -295,11 +312,10 @@ Giving those five a real H1 would be a content change, so it is Maarten's call,
 A Stripe-style bar at the top of **every page** — see <https://docs.stripe.com/billing/scripts>.
 Not silence-by-default: the slot is always present, so readers learn where to look.
 
-It carries three things, in this order of arrival:
+It carries two things:
 
 1. **Applicability** — ✅ built.
-2. **LLM tools** (Step 3.4) — Copy as Markdown, Open in Claude/ChatGPT, View raw.
-3. **Model + engine selector** (Step 4.2) — page-scoped context belongs here, not site chrome.
+2. **Model selection** (Step 4.2) — ✅ built; page-scoped context belongs here, not site chrome.
 
 - [x] `overrides/main.html` renders the bar on every page, inside `.md-content__inner` so it
       shares one left edge with the breadcrumb, H1 and body.
@@ -397,121 +413,172 @@ Silent substitution is rejected.
 
 ## Phase 3 — Agent-first infrastructure (3.1 ✅)
 
-### Step 3.2 — Computed metadata
-- [ ] Build hook computing `estimated_tokens`, `code_languages`, and `last_updated`
-      automatically so they cannot drift.
-- **Verify:** add a code block to a page, rebuild, confirm metadata updates.
+This is the main unfinished POC phase. A good machine-readable index and raw Markdown pages give
+agents enough to discover and traverse the live site without cloning this repository.
 
-### Step 3.3 — Machine-readable surfaces
-- [ ] Generate `/llms.txt` (index with descriptions) and `/llms-full.txt` (everything).
-- [ ] Serve raw markdown at `<page>.md` for every page.
-- **Verify:** `curl` the built site for `llms.txt` and one `.md` endpoint.
+### Step 3.2 — Computed metadata
+- [x] Build-time generator computing `estimated_tokens` and `last_updated` automatically so they cannot
+      drift. Do not compute or expose code-language metadata; it adds noise without improving
+      discovery.
+- [x] Publish the values in `/page-metadata.json`; keep them out of authored frontmatter.
+- **Verify:** edit a page, rebuild, and confirm both values update without frontmatter changes.
+- **Done when:** neither value is hand-authored and the linter rejects attempts to do so.
+
+Zensical does not yet expose a stable build-hook API, so `scripts/build_docs.py` is the explicit
+build entry point. It runs the deterministic generators, delegates to Zensical, and then adds
+the raw-document routes. This keeps the integration replaceable when a supported hook arrives.
+
+### Step 3.3 — Machine-readable surfaces — **POC priority**
+- [x] Generate `/llms.txt` and serve raw Markdown at `<page>.md` for every documentation page.
+- [x] Keep `llms.txt` concise; do not add `/llms-full.txt` unless a demonstrated agent workflow
+      needs the entire site in one response.
+- [x] Mirror the useful structure of <https://docs.stripe.com/llms.txt>:
+    - one `# Gemma Documentation` heading;
+    - a short introduction with durable Gemma-specific guidance for agents;
+    - `##` sections matching the site's main information architecture, with an optional
+      one-sentence description;
+    - entries formatted as `- [Title](https://gemma.ai/path.md): Description` using absolute
+      raw-Markdown URLs;
+    - descriptions and titles generated from page frontmatter, with deterministic ordering and
+      no duplicate entries.
+- [x] Include a short agent-guidance section only for genuinely important defaults and
+      deprecations. Keep navigation in the sectioned link index rather than writing a separate
+      agent skill.
+- **Verify:** fetch `/llms.txt`, follow representative `.md` links from at least three sections,
+      and confirm each returns clean source Markdown rather than rendered HTML.
+- **Done when:** an agent starting with only `https://gemma.ai/llms.txt` can find the appropriate
+      page and retrieve its Markdown without repository access.
 
 Compose entry labels from `title` + `models` rather than adding a second title key —
 `models: [gemma-4]` plus `title: Model card` gives "Gemma 4 — Model card" without touching the
 page. Every input this needs already exists.
 
-### Step 3.4 — Copy-for-agent affordances
-- [ ] "Copy page as Markdown" button, in the page context header.
-- [ ] "Open in Gemini / Claude / ChatGPT" links.
-- **Verify:** click the button, paste, confirm clean markdown with frontmatter.
+### Deliberately omitted from the POC
 
-### Step 3.5 — Docs as a skill
-- [ ] Package the docs as an installable skill and/or MCP server so agents can query them
-      directly.
+- **Step 3.4:** no "Copy page as Markdown" button and no "Open in Gemini / Claude / ChatGPT"
+  links. Raw `.md` URLs are the smaller, universal primitive.
+- **Step 3.5:** no packaged docs skill or MCP server. If `llms.txt` later proves insufficient,
+  a live-site skill should teach an agent to fetch `llms.txt`, choose a section, and follow `.md`
+  pages on the website—never require cloning the repository.
 
 ---
 
-## Phase 4 — Interactive tools
+## Phase 4 — Interactive context
 
-### Step 4.1 — VRAM & context calculator
-Replace the static table in `docs/tools/vram-calculator.md` with a real calculator.
+### Step 4.2 — Global model switcher — **complete**
 
-- [ ] Inputs: model, quantization, context length, batch size, KV-cache dtype, GPU preset
-      (4090, 5090, A100, H100, M-series unified memory, …).
-- [ ] Outputs: fits / doesn't fit with headroom, estimated tok/s, recommended engine,
-      **and the exact copy-paste command for that engine**.
-- [ ] URL-encoded state (`?model=gemma-4/12b&quant=q4_0&gpu=rtx4090&ctx=32k`) so results are
-      shareable.
-- [ ] Reads from `data/models.yml`.
-- **Verify:** results match the memory table in `models/gemma-4/index.md` at matching settings.
-- **Done when:** a shared URL reproduces the exact same state.
-
-### Step 4.2 — Global model switcher, then engine preference
-
-The model half is implemented: pick "E4B" and marked examples across the site rewrite their model
-IDs. A global engine preference remains a separate follow-up; pages currently show the runtimes
-their tutorial actually covers.
-
-- [x] Lean control in the page context header, persisted in `localStorage` and the URL.
-- [ ] Add a global engine preference once pages have equivalent, verified examples for each engine.
-- [ ] Extend Zensical's `content.tabs.link` to sync site-wide.
-- [x] **Capability-aware gating, driven by `data/models.yml`:** audio examples must not offer
-      26B A4B or 31B. When the selected model can't do what the page shows, show an inline
-      explanation and the nearest valid model — never a broken snippet, never a silent swap.
-- [x] Graceful fallback when a model/engine or identifier-flavour pair has no published weights.
-- [x] Opt-in `.model-swap` blocks across the current Gemma 4 quickstart, deployment, capability,
-      QAT, and QLoRA pages; base, instruction, QAT, GGUF, Ollama, and MLX IDs stay semantically
-      correct.
-- **Verify:** select 31B, navigate to `capabilities/audio.md`, confirm the gating message appears
-      and no invalid command is shown.
-- **Model selector verified:** 31B → audio shows the gating message and retains the authored E2B
-      example; multi-engine pages only offer models published for every marked block.
-- **Done when:** engine selection and linked tabs are implemented and no combination of model and
-      engine state produces a command that would fail.
-
-### Step 4.3 — "Choose your model" decision flow
-- [ ] Four questions → recommendation + copy-paste command for the user's engine.
-- [ ] Embeds the VRAM calculator.
-
-### Step 4.4 — Tokenizer & chat-template playground
-- [ ] Paste a conversation → exact `<start_of_turn>` token stream, token count.
-- [ ] Image token budget for a given resolution and aspect ratio.
-- [ ] Reuses the existing `tokens.css` badge styling. Pairs with `get-started/prompt-formatting.md`.
+The current behavior is already global: selecting a Gemma 4 model persists the full model choice
+in `localStorage`, follows the reader across pages, and is mirrored in `?model=` for shareable
+URLs. The selector is only rendered on pages with examples it can safely rewrite.
 
----
-
-## Phase 5 — Demos & embeds
+- [x] Persist the selected model across pages and in the URL.
+- [x] Rewrite opt-in quickstart, deployment, capability, QAT, and QLoRA examples.
+- [x] Gate choices by capability and published identifier flavor.
+- [x] Keep intentionally separate examples fixed when a matching checkpoint is unavailable.
+- **Verified:** selecting 31B persists across compatible pages; audio explains its fallback to
+  E2B; selecting 26B on vLLM rewrites regular examples while its unavailable 26B QAT example
+  remains explicitly fixed at 12B.
+- **Done:** no global engine preference is required for the POC. Revisit it only after several
+  pages have equivalent, verified examples for the same set of engines.
 
-### Step 5.1 — First HF Space embed (spike)
-- [ ] Embed <https://huggingface.co/spaces/google/gemma4_vision_token_budget> on a Demos page.
-- [ ] Lazy-load (Spaces are heavy) and design a fallback for sleeping Spaces — a dead iframe
-      looks worse than no iframe.
-- **Verify:** view on mobile and desktop, light and dark. Test with the Space asleep.
-- **Done when:** we know whether to standardize on iframe or `<gradio-app>`, documented here.
+### Deferred interactive tools
 
-### Step 5.2 — Demos section build-out
-- [ ] Concurrent inference demo (currently a stub).
-- [ ] Additional Spaces once 5.1 establishes the pattern.
+The existing model switcher is enough to demonstrate interactive documentation. The former
+"Choose your model" flow and tokenizer/chat-template playground are useful follow-ups, but are
+not required for the POC.
 
 ---
 
-## Phase 6 — Learn
+## Phase 5 — Demos & embeds — **finish after the core POC**
 
-### Step 6.1 — Visual guides: vendor the images
-The four guides are imported and hand-edited. Their images still point at the Substack CDN.
+### Step 5.1 — One simple HF Space embed
+- [x] Embed <https://huggingface.co/spaces/google/gemma4_vision_token_budget> on one Demos page
+      using the simplest supported iframe approach.
+- [x] Lazy-load it and provide a normal link as the fallback if the embed cannot load.
+- **Verify:** the embed and fallback link are usable at desktop and mobile widths.
+- **Done when:** one representative external demo works; no reusable embed framework or sleeping
+      Space detection is required.
 
-- [ ] Replace remote images with local assets (Maarten provides raw SVG).
-- [ ] Empty `PENDING_IMAGE_HOSTS` in `lint_docs.py` when done — it exists only for this.
-- [ ] **Contextual cross-linking** — link the MoE guide from the 26B A4B section, Quantization
-      from QAT, Reasoning LLMs from `capabilities/thinking.md`.
-- **Verify:** SVGs render crisply at all widths, legible in both themes; `--check images` → 0.
+The former Step 5.2—concurrent inference and a broader collection of Spaces—is not planned for
+the current POC.
 
-### Step 6.2 — Notebooks as source of truth
-- [ ] Keep `.ipynb` canonical, render to pages at build time.
-- [ ] Colab / Kaggle / local badges on every tutorial.
-- [ ] Execute notebooks in CI — a "snippets are tested" badge is a real credibility marker.
+---
 
-### Step 6.3 — Task-first entry points
-- [ ] "I want to…" layer: build a RAG app · extract structured JSON · caption images · classify
-      content · build a tool-using agent. Each is a curated path across existing pages.
+## Phase 6 — Learn — **minimal POC scope**
+
+### Step 6.1 — Visual-guide asset work — **deferred**
+
+Do not vendor or rework the visual-guide images for the current POC. The existing guides can
+remain as they are; revisit ownership and asset quality only if they become part of the final
+production scope.
+
+### Step 6.2 — Markdown-first tutorials and generated notebooks
+
+The capability tutorials originated as notebooks in the
+[`google-gemma/cookbook`](https://github.com/google-gemma/cookbook/tree/main/docs/capabilities)
+repository, but the Zensical pages are now intentionally diverging: they add frontmatter,
+admonitions, styled output, HTML components, and runtime features such as the page-level model
+selector on `get-started/capabilities/thinking.md`. Re-importing an updated notebook would erase
+those documentation-specific improvements.
+
+**Decision:** Markdown is canonical for documentation content. Runnable notebooks are generated
+artifacts, not an alternate place to maintain the same tutorial.
+
+- [x] Spike the workflow on `get-started/capabilities/thinking.md` before applying it elsewhere.
+- [x] Apply the same first-party export and Colab behavior to the text, function calling, image,
+      video, audio, QLoRA, full fine-tuning, and vision QLoRA tutorials. Their Colab links open
+      notebooks generated in this repository, not the older Cookbook sources.
+- [x] Define an explicit export contract in Markdown:
+    - Python fences marked for notebook export become code cells;
+    - ordinary prose becomes Markdown cells;
+    - `.output` blocks are omitted and recreated by execution;
+    - site-only HTML, tabs, and interactive components are omitted or reduced to a plain-text
+      notebook fallback;
+    - `.model-swap` exports its authored default model—the browser selector remains a site-only
+      enhancement.
+- [x] Generate `.ipynb` with stable cell boundaries and kernel/Colab metadata through a small,
+      dependency-light exporter. Keep the contract compatible with a later Jupytext-backed
+      implementation rather than forcing MyST syntax into the website sources.
+- [ ] Support notebook → Markdown as a **one-time ingestion path** for new or heavily revised code
+      examples. Once imported, the Markdown becomes canonical and subsequent notebook builds run
+      Markdown → `.ipynb`; do not allow routine edits in both directions.
+- [x] Add one quiet **Colab** icon link to the page context strip. Do not add
+      redundant download or source actions; Colab already supports download and Markdown is the
+      visible source of truth. Do not claim a Kaggle target until there is a stable published URL.
+- [x] Start with lightweight structural tests rather than execution of GPU notebooks in CI:
+      validate nbformat v4 output, executable-cell selection, and removal of site-only regions.
+- **Verify:** export all registered pages, open at least one capability and one tuning notebook,
+      and confirm that their prose and code cells are useful while the live pages retain their
+      selectors and Zensical presentation.
+- **Done when:** each registered tutorial can be edited only in Markdown, published as the richer
+      Zensical page, and exported to a runnable notebook without hand-editing notebook JSON.
+
+This deliberately separates **authoring origin** from **source of truth**: it is fine to prototype
+new code in Jupyter and import it, but after the documentation-specific pass the Markdown owns the
+tutorial. That prevents a later notebook export from overwriting selector hooks, HTML, or styling.
+
+### Step 6.3 — One simple task-first page
+- [x] Create a single "I want to…" page with a small set of cards—such as run locally, understand
+      images, call tools, and fine-tune—linking to existing documentation.
+- [x] Do not add a recommendation engine or duplicate tutorial content.
+- **Done when:** a newcomer can choose a common goal and reach an existing starting page in one
+      click.
 
 ---
 
-## Phase 7 — The wow tier
+## Phase 7 — The wow tier — **deferred; retain for later**
 
 Only after Phases 0–2 are clean.
 
+### VRAM & context calculator — **moved from Step 4.1**
+
+- [ ] Replace the static table in `docs/tools/vram-calculator.md` with inputs for model,
+      quantization, context length, batch size, KV-cache dtype, and a small set of GPU presets.
+- [ ] Show whether it fits with headroom and provide the exact command for the recommended engine.
+- [ ] Read model facts from `data/models.yml` and encode state in the URL.
+- **Verify:** results match the memory table in `models/gemma-4/index.md` at matching settings.
+- **Done when:** a shared URL reproduces the same result.
+
 - [ ] **In-browser playground** — E2B q4 via WebGPU/transformers.js, running in the docs page.
       ~1.5–2 GB download, so explicit opt-in and clear progress UI.
 - [ ] **Ask Gemma about Gemma** — docs chatbot on transformers.js or an HF Space endpoint.
@@ -526,7 +593,7 @@ Only after Phases 0–2 are clean.
 
 ---
 
-## Phase 8 — Maintainer skill & handover
+## Phase 8 — Maintainer skill & handover — **deferred; retain for later**
 
 *Finalized once the docs are done, but **drafted incrementally** — every phase that establishes a
 convention adds its section immediately, while the reasoning is fresh.*
@@ -565,14 +632,14 @@ archive of one-shot scripts nobody dares delete.
 | `lint_docs.py` | 804 | **Permanent.** CI gate. |
 | `frontmatter_schema.py` | 233 | **Permanent.** Imported by the linter. |
 | `generate_model_names.py` | 86 | **Permanent.** Compiles `data/models.yml` into the template partial the page context header reads; CI verifies it is current. |
-| `convert_notebook.py` + `test_convert_notebook.py` | 896 | **Delete when Phase 2.4 closes** — i.e. once the 6 keep-or-drop decisions are made and nothing more will be imported from upstream. |
+| `convert_notebook.py` + `test_convert_notebook.py` | 896 | **Refactor during Step 6.2.** Keep notebook → Markdown as an ingestion path and add the Markdown → notebook exporter; remove assumptions that upstream notebooks remain canonical after import. |
 | `port_docs.py` | 135 | **Delete with the converter.** All 63 manifest pages are frozen, so today it can only skip. |
 | `check_fidelity.py` | 223 | **Delete with the converter.** Same reason: it reports `0 faithful, 63 frozen` and has nothing left to check. |
 | `rewrite_links.py` | 253 | **Delete with the converter.** One-shot link migration; `links` and `internal` have been 0 for weeks. |
 | `sources.toml` + `link_map.toml` | 577 | **Delete with the converter.** ⚠️ `source` is now duplicated in every page's `source_url` frontmatter — the manifest's only unique data is `frozen`, `assets` and `importer`. |
 | `backfill_frontmatter.py` | 373 | **Delete when Phase 3 closes.** One-shot; frontmatter is complete and hand-maintained from here. New pages get theirs from the Phase 8 skill. |
-| `import_newsletter.py` | 259 | **Keep while more visual guides may arrive**; revisit at Phase 6.1. |
-| `vendor_images.py` | 136 | **Keep until Phase 6.1** — the Substack images still need vendoring. |
+| `import_newsletter.py` | 259 | **Keep through the POC.** Visual-guide work is deferred, so there is no cleanup deadline yet. |
+| `vendor_images.py` | 136 | **Keep through the POC.** Revisit only if visual-guide assets return to scope. |
 
 Roughly **2,450 of 3,877 lines are scheduled for deletion**, most of it gated on one decision:
 whether anything else gets ported from upstream. Making that call early shrinks the surface a
diff --git a/README.md b/README.md
index 9ab4f4a..3f38820 100644
--- a/README.md
+++ b/README.md
@@ -10,20 +10,27 @@ toolchain in `uv.lock`.
 
 ```bash
 uv sync --frozen
+uv run --frozen python scripts/agent_docs.py
 uv run --frozen zensical serve
 ```
 
 The preview is available at <http://localhost:8000/>.
+Zensical's preview server may download raw `.md` routes because of its local MIME mapping;
+GitHub Pages serves those static files with their Markdown content type.
 
 ## Verification
 
 Run these checks before committing documentation or styling changes:
 
 ```bash
-uv run --frozen python scripts/test_convert_notebook.py
+uv run --frozen python -m unittest discover -s scripts -p "test_*.py"
 uv run --frozen python scripts/lint_docs.py --baseline
-uv run --frozen zensical build
+uv run --frozen python scripts/build_docs.py
 ```
 
+`build_docs.py` runs the normal Zensical build and then publishes raw Markdown routes such as
+`/get-started/quickstart.md`. The committed `docs/llms.txt` and `docs/page-metadata.json` are
+regenerated from page frontmatter and verified by the `generated` lint check.
+
 See `PLAN.md` for the migration roadmap and `docs/contributing/components.md`
 for the CSS tokens and reusable components.
diff --git a/data/frameworks.yml b/data/frameworks.yml
index 43397ed..b6cd0b9 100644
--- a/data/frameworks.yml
+++ b/data/frameworks.yml
@@ -11,7 +11,8 @@
 # This list holds only what is in use. It is not a catalogue of everything that can run Gemma.
 #
 #   id    what frontmatter uses. Lowercase, kebab-case.
-#   name  how it is written in prose and in the UI.
+#   name  the canonical display name.
+#   short_name  optional compact label for dense UI such as the page context strip.
 #   url   the project's own home. Where one was already linked from a docs page, that URL is
 #         reused verbatim; the four marked `unverified` were not found anywhere in docs/ and
 #         should be confirmed before anyone relies on them.
@@ -70,6 +71,7 @@ frameworks:
 
   - id: transformers
     name: Hugging Face Transformers
+    short_name: Transformers
     url: https://huggingface.co/docs/transformers
     page: deployment/huggingface.md
 
diff --git a/docs/contributing/components.md b/docs/contributing/components.md
index b7d3431..9a5bfe9 100644
--- a/docs/contributing/components.md
+++ b/docs/contributing/components.md
@@ -142,6 +142,31 @@ is unavailable, the examples remain at their authored default and the picker exp
 there is no silent substitution. Use `{ .model-fixed }` to acknowledge an intentionally static
 block when the same page has swappable examples for that engine.
 
+### Exportable notebook cells
+
+Markdown remains the source of truth for tutorials. Add `.notebook-cell` only to fences that
+should become executable cells in the generated notebook:
+
+````markdown
+```python { .notebook-cell .model-swap }
+print("This becomes a code cell; the authored model is retained.")
+```
+````
+
+Unmarked fences remain part of a Markdown cell, and `{ .output }` blocks are omitted because a
+notebook recreates them when it runs. Add the generated notebook's Colab URL to frontmatter to
+show a quiet, accessible Colab icon in the page context strip:
+
+```yaml
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/tutorial.ipynb
+```
+
+Wrap any other site-only controls in `<!-- notebook:skip-start -->` and
+`<!-- notebook:skip-end -->`. Register the source and output in `scripts/notebooks.py`, then
+generate every registered notebook with `uv run python scripts/notebooks.py`; use `--check`
+during verification. Do not edit the generated `.ipynb`
+directly.
+
 ## Steps
 
 A numbered walkthrough. Built on an ordered list, so it still reads correctly without CSS.
diff --git a/docs/demos/concurrent.md b/docs/demos/concurrent.md
deleted file mode 100644
index f71b65f..0000000
--- a/docs/demos/concurrent.md
+++ /dev/null
@@ -1,7 +0,0 @@
----
-title: Concurrent
-description: A multi-model concurrent inference demo application.
----
-# Concurrent
-
-A multi-model concurrent inference demo application.
diff --git a/docs/demos/vision-token-budget.md b/docs/demos/vision-token-budget.md
new file mode 100644
index 0000000..4d1cb16
--- /dev/null
+++ b/docs/demos/vision-token-budget.md
@@ -0,0 +1,21 @@
+---
+title: Vision token budget
+description: Explore how Gemma 4 resizes images and allocates visual tokens across aspect ratios.
+models: [gemma-4]
+---
+
+# Explore the Gemma 4 vision token budget
+
+Upload an image to see how Gemma 4 preserves its aspect ratio while mapping it onto different
+visual token budgets. This interactive demo is hosted on Hugging Face Spaces.
+
+<div class="demo-embed">
+  <iframe
+    src="https://google-gemma4-vision-token-budget.static.hf.space/index.html"
+    title="Gemma 4 vision token budget interactive demo"
+    loading="lazy"
+    allow="clipboard-read; clipboard-write"
+  ></iframe>
+</div>
+
+If the embedded demo does not load, [open it directly on Hugging Face](https://huggingface.co/spaces/google/gemma4_vision_token_budget).
diff --git a/docs/get-started/capabilities/audio.md b/docs/get-started/capabilities/audio.md
index d5d6344..6af900d 100644
--- a/docs/get-started/capabilities/audio.md
+++ b/docs/get-started/capabilities/audio.md
@@ -4,6 +4,7 @@ description: Starting with Gemma 3n, you can use audio directly into your prompt
 models: [gemma-4, gemma-3n]
 requires: audio
 frameworks: [transformers]
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/audio.ipynb
 ---
 
 # Audio Understanding
@@ -18,7 +19,7 @@ This guide provides an overview of the audio processing capabilities of [Gemma 4
 
 Install the Hugging Face libraries required for running the Gemma model and making requests.
 
-```bash
+```bash { .notebook-cell }
 # Install PyTorch & other libraries
 pip install torch accelerate
 
@@ -30,7 +31,7 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import pipeline
@@ -104,7 +105,7 @@ Follow these specific instructions for formatting the answer:
 
 The following code examples show how to prompt the model to transcribe text from audio files using Hugging Face Transformers:
 
-```python
+```python { .notebook-cell }
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
 config.max_new_tokens = 64
@@ -131,7 +132,7 @@ print(outputs[0]['generated_text'])
 I woke up early today feeling really fresh the morning light was beautiful and I enjoyed a nice cup of coffee<turn|>
 ```
 
-```python
+```python { .notebook-cell }
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
 config.max_new_tokens = 1024
@@ -184,7 +185,7 @@ When formatting the answer, first output the transcription in {SOURCE_LANGUAGE},
 
 The following code examples show how to prompt the model to translate spoken audio into text using Hugging Face Transformers:
 
-```python
+```python { .notebook-cell }
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
 config.max_new_tokens = 64
@@ -213,13 +214,13 @@ Korean: 장미는 빨갛고, 제비꽃은 파랗다.<turn|>
 
 Try this by yourself
 
-```bash
+```bash { .notebook-cell }
 pip install ipywebrtc
 ```
 
 Press the circle button and start speaking. Click the circle button again when you are finished. The widget will immediately begin to play back what it captured.
 
-```python
+```python { .notebook-cell }
 from google.colab import output
 output.enable_custom_widget_manager()
 
@@ -236,7 +237,7 @@ AudioRecorder(audio=Audio(value=b'', format='webm'), stream=CameraStream(constra
 
 Convert webm file to wav format that PyTorch can understand.
 
-```python
+```python { .notebook-cell }
 with open('/content/recording.webm', 'wb') as f:
     f.write(recorder.audio.value)
 !ffmpeg -i /content/recording.webm /content/recording.wav -y
@@ -274,7 +275,7 @@ video:0kB audio:287kB subtitle:0kB other streams:0kB global headers:0kB muxing o
 
 ### ASR
 
-```python
+```python { .notebook-cell }
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
 config.max_new_tokens = 64
@@ -298,7 +299,7 @@ How can I get to the station?<turn|>
 
 ### AST
 
-```python
+```python { .notebook-cell }
 messages = [{
   "role": "user",
   "content": [
diff --git a/docs/get-started/capabilities/function-calling.md b/docs/get-started/capabilities/function-calling.md
index 7603d5a..265201d 100644
--- a/docs/get-started/capabilities/function-calling.md
+++ b/docs/get-started/capabilities/function-calling.md
@@ -3,6 +3,7 @@ title: Tool calling
 description: When using a generative artificial intelligence (AI) model such as Gemma, you may want to use the model to operate programming interfaces in order to complete tasks or answer questions.
 models: [gemma-4]
 frameworks: [transformers]
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/function-calling.ipynb
 ---
 <!-- TODO: Rename this to "tool calling"? -->
 # Tool calling
@@ -23,7 +24,7 @@ This guide shows the process of using Gemma 4 within the Hugging Face ecosystem.
 
 Install the Hugging Face libraries required for running the Gemma model and making requests.
 
-```bash
+```bash { .notebook-cell }
 # Install PyTorch & other libraries
 pip install torch accelerate
 
@@ -35,7 +36,7 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import AutoProcessor, AutoModelForMultimodalLM
@@ -57,7 +58,7 @@ You can pass tools to the model using the `apply_chat_template()` function via t
 
 Below is the example with the JSON schema.
 
-```python
+```python { .notebook-cell }
 from transformers import TextStreamer
 
 weather_function_schema = {
@@ -104,7 +105,7 @@ What's the temperature in London?<turn|>
 
 And the same example with the raw Python function.
 
-```python
+```python { .notebook-cell }
 from transformers.utils import get_json_schema
 
 def get_current_temperature(location: str):
@@ -145,7 +146,7 @@ This section demonstrates a three-stage cycle for connecting the model to extern
 
 Here's the user prompt `"Hey, what's the weather in Tokyo right now?"`, and the tool `[get_current_weather]`. Gemma generates a function call object as follows.
 
-```python
+```python { .notebook-cell }
 # Define a function that our model can use.
 def get_current_weather(location: str, unit: str = "celsius"):
     """
@@ -196,7 +197,7 @@ Your application should parse the model's response to extract the function name
 !!! Note
     Always validate function names and arguments before execution.
 
-```python
+```python { .notebook-cell }
 import re
 import json
 
@@ -267,7 +268,7 @@ if calls:
 !!! Note
     For optimal results, append the tool execution result to your message history using the specific format below. This ensures the chat template correctly generates the required token structure (e.g., `response:get_current_weather{temperature:15,weather:<|"|>sunny<|"|>}`).
 
-```python
+```python { .notebook-cell }
 "tool_responses": [
   {
     "name": function_name,
@@ -278,7 +279,7 @@ if calls:
 
 In case of multiple independent requests:
 
-```python
+```python { .notebook-cell }
 "tool_responses": [
   {
     "name": function_name_1,
@@ -295,7 +296,7 @@ In case of multiple independent requests:
 
 Finally, Gemma reads the tool response and reply to the user.
 
-```python
+```python { .notebook-cell }
 text = processor.apply_chat_template(message, tools=tools, tokenize=False, add_generation_prompt=True)
 inputs = processor(text=text, return_tensors="pt").to(model.device)
 out = model.generate(**inputs, max_new_tokens=128)
@@ -311,7 +312,7 @@ Output: The current weather in Tokyo is 15 degrees Celsius and sunny.
 
 You can see the full chat history below.
 
-```python
+```python { .notebook-cell }
 # full history
 print(json.dumps(message, indent=2))
 
@@ -367,7 +368,7 @@ Hey, what's the weather in Tokyo right now?<turn|>
 
 By utilizing an internal reasoning process, the model significantly enhances its function-calling accuracy. This allows for more precise decision-making regarding when to trigger a tool and how to define its parameters.
 
-```python
+```python { .notebook-cell }
 prompt = "Hey, I'm in Seoul. Is it good for running now?"
 message = [
     {
@@ -434,7 +435,7 @@ Role: assistant
 
 Process the tool call and get the final answer.
 
-```python
+```python { .notebook-cell }
 calls = extract_tool_calls(output)
 if calls:
     # Call the function and get the result
@@ -520,7 +521,7 @@ If a function uses a custom object (like a Config class) as an argument, the aut
 
 In these cases, manually defining the JSON schema is preferred to ensure nested properties (such as theme or font_size within a config object) are explicitly defined for the model.
 
-```python
+```python { .notebook-cell }
 import json
 from transformers.utils import get_json_schema
 
diff --git a/docs/get-started/capabilities/image.md b/docs/get-started/capabilities/image.md
index c00effe..4a4cf50 100644
--- a/docs/get-started/capabilities/image.md
+++ b/docs/get-started/capabilities/image.md
@@ -3,6 +3,7 @@ title: Image Understanding
 description: You can use Gemma 3 and later models to analyze and understand the content of images.
 models: [gemma-4, gemma-3]
 frameworks: [transformers]
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/image.ipynb
 ---
 # Image Understanding
 
@@ -15,7 +16,7 @@ Here are some examples demonstrating these capabilities.
 
 Install the Hugging Face libraries required for running the Gemma model and making requests.
 
-```bash
+```bash { .notebook-cell }
 # Install PyTorch & other libraries
 pip install torch accelerate
 
@@ -27,7 +28,7 @@ pip install "transformers>=5.10.1"
 
 Use `transformers` library to load the pipeline
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import pipeline
@@ -55,7 +56,7 @@ tokenizer.json:   0%|          | 0.00/32.2M [00:00<?, ?B/s]
 
 The following example shows how to provide an image and ask question about it.
 
-```python
+```python { .notebook-cell }
 from PIL import Image
 from IPython.display import display
 import requests
@@ -102,7 +103,7 @@ It's a scenic photograph capturing the bridge and the coastal environment.<turn|
 
 You can provide multiple images in a single prompt by including multiple image content in prompt template.
 
-```python
+```python { .notebook-cell }
 from PIL import Image
 from IPython.display import display
 import requests
@@ -196,7 +197,7 @@ Here are captions for the two images you provided:
 
 Models can recognize multilingual texts in the image.
 
-```python
+```python { .notebook-cell }
 from PIL import Image
 from IPython.display import display
 import requests
@@ -241,7 +242,7 @@ In the context of the picture (showing a cat playfully jumping), the sign is lik
 
 Models are trained to detect objects in an image and get their bounding box coordinates. Bounding box coordinates are expressed as normalized values relative to a 1000x1000 grid. You need to descale these coordinates based on your original image size.
 
-```python
+```python { .notebook-cell }
 import numpy as np
 from PIL import Image
 from IPython.display import display
@@ -355,7 +356,7 @@ This budget determines how much the image is resized. Imagine you have a budget
 
 Let's explore what happens if we perform object detection on an image and setting the budget size very low (70):
 
-```python
+```python { .notebook-cell }
 import numpy as np
 from PIL import Image
 import requests, cv2, re, json
@@ -436,7 +437,7 @@ It does alright but it is clear that the image is being compressed quite a bit a
 
 Let's explore what happens when we increase the budget sizes! A larger budget size results in more soft tokens being generated and processed. This should improve the object detection.
 
-```python
+```python { .notebook-cell }
 import matplotlib.pyplot as plt
 
 def count_tokens(processor, tokens):
diff --git a/docs/get-started/capabilities/text.md b/docs/get-started/capabilities/text.md
index 4e989d2..3bfa31c 100644
--- a/docs/get-started/capabilities/text.md
+++ b/docs/get-started/capabilities/text.md
@@ -3,6 +3,7 @@ title: Inference
 description: "Basic text inference with Gemma 4 in Hugging Face Transformers: single-turn prompts, multi-turn conversations, and system instructions."
 models: [gemma-4]
 frameworks: [transformers]
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/text.ipynb
 ---
 # Inference
 
@@ -14,7 +15,7 @@ This document provides a guide to performing basic text inference with Gemma 4 u
 
 Install the Hugging Face libraries required for running the Gemma model and making requests.
 
-```bash
+```bash { .notebook-cell }
 # Install PyTorch & other libraries
 pip install torch accelerate
 
@@ -24,7 +25,7 @@ pip install "transformers>=5.10.1"
 
 [Dialog](https://github.com/google-deepmind/dialog) is a library to manipulate and display conversations.
 
-```bash
+```bash { .notebook-cell }
 pip install dialog
 ```
 
@@ -32,7 +33,7 @@ pip install dialog
 
 Use `transformers` library to load the pipeline
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import pipeline
@@ -50,7 +51,7 @@ txt_pipe = pipeline(
 
 Once you have the Gemma model loaded and configured in a `pipeline` object, you can send prompts to the model. The following example code shows a basic request using the `text_inputs` parameter:
 
-```python
+```python { .notebook-cell }
 output = txt_pipe(text_inputs="<|turn>user\nRoses are..<turn|>\n<|turn>model\n")
 print(output[0]['generated_text'].removesuffix("<turn|>"))
 ```
@@ -83,7 +84,7 @@ Here are a few ways to complete the phrase "Roses are...":
 
 ## Use Dialog library
 
-```python
+```python { .notebook-cell }
 import dialog
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
@@ -132,7 +133,7 @@ Here are a few ways to complete the phrase "Roses are...":
 
 When generating content with more complex prompting, use a prompt template to structure your request. A prompt template allows you to specify input from specific roles, such as `user` or `model`, and is a required format for managing multi-turn chat interactions with Gemma models. The following example code shows how to construct a prompt template for Gemma:
 
-```python
+```python { .notebook-cell }
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
 config.max_new_tokens = 512
@@ -166,7 +167,7 @@ A primal fear, a dark design.
 
 In a multi-turn setup, the conversation history is preserved as a sequence of alternating `user` and `model` roles. This cumulative list serves as the model's memory, ensuring that each new output is informed by the preceding dialogue.
 
-```python
+```python { .notebook-cell }
 import dialog
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
@@ -219,7 +220,7 @@ That breaks the sailor's guarded heart.
 
 And here's the conversation exported as text.
 
-```python
+```python { .notebook-cell }
 chat_history = conv.as_text(training=True)
 print(chat_history)
 print("-"*80)
@@ -262,7 +263,7 @@ That breaks the sailor's guarded heart.<turn|>
 
 Use the `system` role to provide the system-level instructions.
 
-```python
+```python { .notebook-cell }
 import dialog
 from transformers import GenerationConfig
 config = GenerationConfig.from_pretrained(MODEL_ID)
diff --git a/docs/get-started/capabilities/thinking.md b/docs/get-started/capabilities/thinking.md
index cc20df2..5bc97d3 100644
--- a/docs/get-started/capabilities/thinking.md
+++ b/docs/get-started/capabilities/thinking.md
@@ -3,6 +3,7 @@ title: Reasoning
 description: Use Gemma 4's thinking mode to reason before answering, for text and image prompts, and parse the reasoning apart from the answer.
 models: [gemma-4]
 frameworks: [transformers]
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/thinking.ipynb
 ---
 # Reasoning
 
@@ -11,12 +12,11 @@ Gemma is a family of lightweight, state-of-the-art open models built from the sa
 
 This document demonstrates how to use the thinking capabilities of Gemma 4 to generate reasoning processes before providing a final answer. You will learn how to enable thinking mode for both text-only and multimodal (image-text) tasks using the Hugging Face `transformers` library, and how to parse the output to separate thinking from the answer.
 
-
 ## Install Python packages
 
 Install the Hugging Face libraries required for running the Gemma model and making requests.
 
-```bash
+```bash { .notebook-cell }
 # Install PyTorch & other libraries
 pip install torch accelerate
 
@@ -28,7 +28,7 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python { .model-swap }
+```python { .model-swap .notebook-cell }
 MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import AutoProcessor, AutoModelForMultimodalLM
@@ -61,7 +61,7 @@ To generate a response using the model's thinking capabilities, pass `enable_thi
 | **12B/26B/31B** | **OFF** | ⚠️ `<|turn>user\n[Prompt]<turn|>\n<|turn>model\n<|channel>thought\n<channel|>` |
 | **12B/26B/31B** | **ON** | `<|turn>system\n<|think|><turn|>\n<|turn>user\n[Prompt]<turn|>\n<|turn>model` |
 
-```python
+```python { .notebook-cell }
 from transformers import TextStreamer
 
 message = [
@@ -150,7 +150,7 @@ Once the text is generated, the response will contain both the reasoning blocks
 !!! Note
     `skip_special_tokens=False` is mandatory to make the reasoning parser to work correctly.
 
-```python
+```python { .notebook-cell }
 response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
 result = processor.parse_response(response)
 
@@ -242,7 +242,7 @@ Properly managing the model's generated thoughts is critical for maintaining per
 - **Function Calling (Exception):** If a single model turn involves function or tool calls, thoughts must **NOT** be removed between the function calls.
 - **Maintaining Conversation History:** The historical model output must only include the final response. Ensure that no generated thoughts from previous turns remain in the context window before the next user turn begins.
 
-```python
+```python { .notebook-cell }
 from transformers import TextStreamer
 
 # Append the clean response to the message history
@@ -340,7 +340,7 @@ $$\mathbf{100^\circ\text{C} }$$
 **Note:** If the atmospheric pressure is lower (e.g., at high altitudes), the boiling point will be slightly lower than \\(100^\circ\text{C}\\). If the pressure is higher, the boiling point will be higher.<turn|>
 ```
 
-```python
+```python { .notebook-cell }
 response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
 result = processor.parse_response(response)
 
@@ -385,7 +385,7 @@ $$\mathbf{100^\circ\text{C} }$$
 
 The procedure for using the thinking model with visual data is very similar. You can provide an image as part of the `messages` array. Just ensure you pass the image to the processor along with the formatted text, and the model will reason about the visual input before responding.
 
-```python
+```python { .notebook-cell }
 from PIL import Image
 import matplotlib.pyplot as plt
 
diff --git a/docs/get-started/capabilities/video.md b/docs/get-started/capabilities/video.md
index f16ca9c..57b9bdd 100644
--- a/docs/get-started/capabilities/video.md
+++ b/docs/get-started/capabilities/video.md
@@ -3,6 +3,7 @@ title: Video Understanding
 description: Video data is a rich source of information that can help you accomplish tasks and understand the world around you.
 models: [gemma-4]
 frameworks: [transformers]
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/video.ipynb
 ---
 # Video Understanding
 
@@ -13,7 +14,7 @@ Video data is a rich source of information that can help you accomplish tasks an
 
 Install the Hugging Face libraries required for running the Gemma model and making requests.
 
-```bash
+```bash { .notebook-cell }
 # Install PyTorch & other libraries
 pip install torch accelerate
 
@@ -25,7 +26,7 @@ pip install "transformers>=5.10.1"
 
 Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 MODEL_ID = "google/gemma-4-E2B-it"
 
 from transformers import AutoProcessor, AutoModelForMultimodalLM
@@ -49,7 +50,7 @@ tokenizer.json:   0%|          | 0.00/32.2M [00:00<?, ?B/s]
 
 You can reference the location of the file in the chat template. The `processor` will then take care of loading in the video and processing it. The following example code shows a request using a chat template, output generation, and decoding the response:
 
-```python
+```python { .notebook-cell }
 from transformers import AutoProcessor
 
 messages = [
diff --git a/docs/javascripts/model-switcher.js b/docs/javascripts/model-switcher.js
index 7aa03b6..323bd4f 100644
--- a/docs/javascripts/model-switcher.js
+++ b/docs/javascripts/model-switcher.js
@@ -202,7 +202,9 @@
 
     picker.append(trigger, menu);
     item.append(label, picker);
-    strip.appendChild(item);
+    const colabItem = strip.querySelector(".page-context__item--colab");
+    if (colabItem) strip.insertBefore(item, colabItem);
+    else strip.appendChild(item);
     choose(picker, initial);
   };
 
diff --git a/docs/learn/index.md b/docs/learn/index.md
new file mode 100644
index 0000000..39ace7b
--- /dev/null
+++ b/docs/learn/index.md
@@ -0,0 +1,28 @@
+---
+title: Choose your path
+description: Start with a common Gemma task and jump directly to the most useful documentation.
+---
+
+# What do you want to build?
+
+Choose the outcome closest to your goal. Each path starts with existing documentation rather
+than duplicating it here.
+
+<div class="card-grid">
+<a class="card" href="../get-started/quickstart.md">
+  <div class="card-title">Run your first model</div>
+  <p class="card-desc">Choose a runtime and generate your first response with Gemma 4.</p>
+</a>
+<a class="card" href="../get-started/capabilities/vision.md">
+  <div class="card-title">Understand images and video</div>
+  <p class="card-desc">Use Gemma's multimodal capabilities with visual inputs.</p>
+</a>
+<a class="card" href="../get-started/capabilities/function-calling.md">
+  <div class="card-title">Build a tool-using application</div>
+  <p class="card-desc">Generate structured function calls and connect Gemma to tools.</p>
+</a>
+<a class="card" href="../tuning/overview.md">
+  <div class="card-title">Adapt Gemma to your task</div>
+  <p class="card-desc">Compare fine-tuning approaches and choose a practical starting point.</p>
+</a>
+</div>
diff --git a/docs/llms.txt b/docs/llms.txt
new file mode 100644
index 0000000..93677ea
--- /dev/null
+++ b/docs/llms.txt
@@ -0,0 +1,117 @@
+# Gemma Documentation
+
+Official documentation for building with the Gemma family of open models.
+Start with Gemma 4 unless a page or the user's request names another generation.
+Follow the Markdown links below to read canonical page content without cloning the repository.
+
+## Home
+- [Google Gemma 4 Open Models](https://gemma.ai/index.md): Official technical documentation portal for Google Gemma 4 open-weight AI models. Explore multimodal capabilities, interactive WebGPU playgrounds, and partner integrations.
+
+## Get started
+- [Quick Start](https://gemma.ai/get-started/quickstart.md): The Gemma family of open models includes a range of model sizes, capabilities, and task-specialized variations to help you build custom generative solutions.
+- [Inference](https://gemma.ai/get-started/capabilities/text.md): Basic text inference with Gemma 4 in Hugging Face Transformers: single-turn prompts, multi-turn conversations, and system instructions.
+- [Reasoning](https://gemma.ai/get-started/capabilities/thinking.md): Use Gemma 4's thinking mode to reason before answering, for text and image prompts, and parse the reasoning apart from the answer.
+- [Tool calling](https://gemma.ai/get-started/capabilities/function-calling.md): When using a generative artificial intelligence (AI) model such as Gemma, you may want to use the model to operate programming interfaces in order to complete tasks or answer questions.
+- [Vision understanding](https://gemma.ai/get-started/capabilities/vision.md): Prepare and pass visual data to Gemma 4 for object detection, OCR, visual question answering, captioning, and multi-image reasoning.
+- [Image Understanding](https://gemma.ai/get-started/capabilities/image.md): You can use Gemma 3 and later models to analyze and understand the content of images.
+- [Video Understanding](https://gemma.ai/get-started/capabilities/video.md): Video data is a rich source of information that can help you accomplish tasks and understand the world around you.
+- [Audio Understanding](https://gemma.ai/get-started/capabilities/audio.md): Starting with Gemma 3n, you can use audio directly into your prompts and workflows.
+- [Speed-up Gemma 4 with Multi-Token Prediction](https://gemma.ai/get-started/capabilities/mtp.md): In Gemma 4, Multi-Token Prediction (MTP) is the specific architecture used to enable highly efficient Speculative Decoding.
+- [Prompt Formatting](https://gemma.ai/get-started/prompt-formatting.md): The control tokens Gemma 4 uses to structure a prompt, and what each one is for.
+- [Gemma formatting and system instructions](https://gemma.ai/get-started/prompt-structure.md): Gemma instruction-tuned (IT) models are trained with a specific formatter that annotates all instruction tuning examples with extra information, both at training and inference time.
+
+## Models
+- [Gemma Model Family](https://gemma.ai/models.md): The Gemma family of open models spans multiple generations and specialized variants.
+- [Gemma 4](https://gemma.ai/models/gemma-4.md): Gemma is a family of generative artificial intelligence models and you can use them in a wide variety of generation tasks, including question answering, summarization, and reasoning.
+- [Gemma 4 — Model card](https://gemma.ai/models/gemma-4/model-card.md): Gemma is a family of open models built by Google DeepMind.
+- [Gemma 3n model overview](https://gemma.ai/models/gemma-3n.md): Gemma 3n is a generative AI model optimized for use in everyday devices, such as phones, laptops, and tablets.
+- [Gemma 3n model card](https://gemma.ai/models/gemma-3n/model-card.md): Model card for Gemma 3n: on-device inputs and outputs, training data, evaluation results, and intended use.
+- [DiffusionGemma model overview](https://gemma.ai/models/diffusiongemma.md): DiffusionGemma is an experimental open model that explores text diffusion, an exceptionally fast approach to text generation.
+- [DiffusionGemma model card](https://gemma.ai/models/diffusiongemma/model-card.md): DiffusionGemma is a generative model built by Google DeepMind.
+- [Diffusion in Text Generation Explained](https://gemma.ai/models/diffusiongemma/explained.md): To understand DiffusionGemma, it helps to examine the core limitations of standard language models and how text-based diffusion differs.
+- [Generate output](https://gemma.ai/models/diffusiongemma/inference.md): Generate text with DiffusionGemma using Hugging Face Transformers, including the GPU memory the model requires.
+- [FunctionGemma model overview](https://gemma.ai/models/functiongemma.md): FunctionGemma is a specialized version of our Gemma 3 270M model tuned for function calling.
+- [FunctionGemma model card](https://gemma.ai/models/functiongemma/model-card.md): Model card for FunctionGemma: function-calling inputs and outputs, training data, evaluation results, and intended use.
+- [FunctionGemma formatting and best practices](https://gemma.ai/models/functiongemma/formatting.md): FunctionGemma is a specialized version of the Gemma 3 270M model, trained specifically for function calling (i.e., tool use).
+- [Function calling with Hugging Face Transformers](https://gemma.ai/models/functiongemma/inference.md): Call functions with FunctionGemma in Hugging Face Transformers, passing tools as manual JSON schemas or as raw Python functions.
+- [Full function calling sequence with FunctionGemma](https://gemma.ai/models/functiongemma/full-sequence.md): The complete FunctionGemma tool-calling cycle: the model's call, the developer's execution, and the final response built from the tool's output.
+- [Fine-tuning with FunctionGemma](https://gemma.ai/models/functiongemma/fine-tuning.md): This guide demonstrates how to fine-tune FunctionGemma for tool calling.
+- [EmbeddingGemma model overview](https://gemma.ai/models/embeddinggemma.md): EmbeddingGemma is a 308M parameter multilingual text embedding model based on Gemma 3.
+- [EmbeddingGemma model card](https://gemma.ai/models/embeddinggemma/model-card.md): Model card for EmbeddingGemma: embedding inputs and outputs, training data, evaluation results, and intended use.
+- [Generate Embeddings with Sentence Transformers](https://gemma.ai/models/embeddinggemma/inference.md): EmbeddingGemma is a lightweight, open embedding model designed for fast, high-quality retrieval on everyday devices like mobile phones.
+- [Fine-tune EmbeddingGemma](https://gemma.ai/models/embeddinggemma/fine-tuning.md): Fine-tuning helps close the gap between a model's general-purpose understanding and the specialized, high-performance accuracy that your application requires.
+- [PaliGemma](https://gemma.ai/models/paligemma.md): PaliGemma 2 and PaliGemma are lightweight open vision-language models (VLM) inspired by PaLI-3, and based on open components like the SigLIP vision model and the Gemma language model.
+- [PaliGemma model card](https://gemma.ai/models/paligemma/model-card-v1.md): PaliGemma is a versatile and lightweight vision-language model (VLM) inspired by PaLI-3 and based on open components such as the SigLIP vision model and the Gemma language model.
+- [PaliGemma 2 model card](https://gemma.ai/models/paligemma/model-card-v2.md): PaliGemma 2 is an update of the PaliGemma vision-language model (VLM) which incorporates the capabilities of the Gemma 2 models.
+- [PaliGemma prompt and system instructions](https://gemma.ai/models/paligemma/prompting.md): This page describes prompt formatting and system instructions for PaliGemma models.
+- [Generate PaliGemma output with Keras](https://gemma.ai/models/paligemma/inference.md): PaliGemma models have multimodal capabilities, allowing you to generate output using both text and image input data.
+- [Fine-tune PaliGemma with JAX and Flax](https://gemma.ai/models/paligemma/fine-tuning.md): This notebook shows how to fine-tune PaliGemma on a vision-language task with JAX.
+- [ShieldGemma](https://gemma.ai/models/shieldgemma.md): ShieldGemma is a set of instruction tuned models for evaluating the safety of text and images against a set of defined safety policies.
+- [ShieldGemma model card](https://gemma.ai/models/shieldgemma/model-card-v1.md): Model card for ShieldGemma: safety-classification inputs and outputs, training data, evaluation results, and intended use.
+- [ShieldGemma 2 model card](https://gemma.ai/models/shieldgemma/model-card-v2.md): ShieldGemma 2 is a model trained on Gemma 3's 4B IT checkpoint for image safety classification across key categories that takes in images and outputs safety labels per policy.
+- [Gemma 3 model card](https://gemma.ai/models/gemma-3.md): Model card for the multimodal Gemma 3 models: 128K context, 140+ languages, inputs and outputs, training data, and evaluation.
+- [Gemma 2 model card](https://gemma.ai/models/gemma-2.md): Model card for Gemma 2 text-to-text models: description, inputs and outputs, training data, and evaluation.
+- [Gemma model card](https://gemma.ai/models/gemma-1.md): Model card for the first-generation Gemma text-to-text models: description, inputs and outputs, training data, and evaluation.
+- [RecurrentGemma](https://gemma.ai/models/recurrentgemma.md): RecurrentGemma is an open model based on Griffin, a hybrid model architecture that mixes gated linear recurrences with local sliding window attention.
+- [RecurrentGemma model card](https://gemma.ai/models/recurrentgemma/model-card.md): RecurrentGemma is a family of open language models built on a novel recurrent architecture developed at Google.
+- [Inference with RecurrentGemma using JAX and Flax](https://gemma.ai/models/recurrentgemma/inference.md): Run sampling and inference with RecurrentGemma 2B Instruct using Google DeepMind's recurrentgemma library and JAX.
+- [Fine-tuning using JAX and Flax](https://gemma.ai/models/recurrentgemma/fine-tuning.md): Fine-tune RecurrentGemma 2B Instruct for English-French translation using JAX, Flax, and the MTNT noisy-text dataset.
+- [DataGemma](https://gemma.ai/models/datagemma.md): DataGemma is a research tool that lets users ask questions in plain language and receive answers based on publicly available statistical data in the Data Commons repository.
+- [Gemma Scope](https://gemma.ai/models/gemma-scope.md): Gemma Scope 2 is a comprehensive, open suite of interpretability tools designed for the Gemma 3 model collection.
+- [Gemma for abstractive proposition segmentation (APS)](https://gemma.ai/models/gemma-aps.md): Abstractive proposition segmentation (APS) is an analysis technique that breaks down text information into component parts.
+- [Releases](https://gemma.ai/models/releases.md): Dated release history for every Gemma model, from Gemma 1 through Gemma 4 and the specialized variants.
+
+## Run
+- [Gemma 4 — Overview](https://gemma.ai/deployment.md): Choose an execution framework and a Gemma variant for your hardware, then run your first generation request.
+- [Ollama](https://gemma.ai/deployment/ollama.md): Run Gemma 4 locally with Ollama — model tags, image input, and the OpenAI-compatible server.
+- [llama.cpp](https://gemma.ai/deployment/llamacpp.md): Run Gemma 4 with llama.cpp — GGUF repos for every size, image/audio/video input, and the OpenAI-compatible server.
+- [LM Studio](https://gemma.ai/deployment/lmstudio.md): Run Gemma 4 with LM Studio — desktop app, lms CLI, and the OpenAI-compatible local server.
+- [Apple MLX](https://gemma.ai/deployment/mlx.md): Run Gemma 4 on Apple silicon with MLX — text and vision generation, model repos, and the OpenAI-compatible server.
+- [Run Gemma with Hugging Face Transformers](https://gemma.ai/deployment/huggingface.md): Generating text, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models.
+- [Run Gemma with Keras](https://gemma.ai/deployment/keras.md): Generating content, summarizing, and analysing content are just some of the tasks you can accomplish with Gemma open models.
+- [Convert Hugging Face Safetensors to MediaPipe Task](https://gemma.ai/deployment/litert.md): This guide provides instructions for converting Gemma models in Hugging Face Safetensors format (.safetensors) into the MediaPipe Task file format (.task).
+- [Deploy Gemma in web browsers](https://gemma.ai/deployment/web.md): You can run Gemma models completely on-device with the MediaPipe LLM Inference API.
+- [Deploy Gemma on mobile devices](https://gemma.ai/deployment/mobile.md): This document outlines various methods and tools for deploying and running Gemma models on mobile devices, including using the Google AI Edge Gallery app and the MediaPipe LLM Inference API.
+- [vLLM](https://gemma.ai/deployment/vllm.md): Serve Gemma 4 with vLLM — install, vllm serve, model repositories, and the OpenAI-compatible API.
+- [SGLang](https://gemma.ai/deployment/sglang.md): Serve Gemma 4 with SGLang — install, launch_server, model repositories, and the OpenAI-compatible API.
+- [Google Cloud](https://gemma.ai/deployment/google-cloud.md): Deploy, serve, and fine-tune Gemma 4 on Google Cloud: Cloud Run, GKE, the Agent Development Kit, vLLM on TPUs, MaxText, and Sovereign Cloud.
+- [Get started with Gemma and LangChain](https://gemma.ai/deployment/langchain.md): This tutorial shows you how to get started with Gemma and LangChain, running in Google Cloud or in your Colab environment.
+- [Quantization & QAT](https://gemma.ai/deployment/qat.md): Which Gemma 4 quantized weights to download for each engine, and why QAT beats post-training quantization at 4-bit.
+
+## Fine-tune
+- [Gemma model fine-tuning](https://gemma.ai/tuning/overview.md): Fine-tuning a generative artificial intelligence (AI) model such as Gemma modifies the behavior of the model.
+- [Fine-Tune Gemma using Hugging Face Transformers and QloRA](https://gemma.ai/tuning/qlora.md): This guide walks you through how to fine-tune Gemma on a custom text-to-sql dataset using Hugging Face Transformers and TRL.
+- [Full Model Fine-Tune using Hugging Face Transformers](https://gemma.ai/tuning/full-finetune.md): This guide walks you through how to fine-tune Gemma on a mobile game NPC dataset using Hugging Face Transformers and TRL.
+- [Fine-Tune Gemma for Vision Tasks using Hugging Face Transformers and QLoRA](https://gemma.ai/tuning/vision-qlora.md): This guide walks you through how to fine-tune Gemma on a custom image and text dataset for a vision task (generating product descriptions) using Hugging Face Transformers and TRL.
+- [Fine-tune Gemma in Keras using LoRA](https://gemma.ai/tuning/keras-lora.md): Generative artificial intelligent (AI) models like Gemma are effective at a variety of tasks.
+- [Distributed tuning with Gemma using Keras](https://gemma.ai/tuning/distributed.md): Gemma is a family of lightweight, state-of-the-art open models built from research and technology used to create Google Gemini models.
+- [Tasks in spoken languages with Gemma](https://gemma.ai/tuning/spoken-language.md): Using artificial intelligence (AI) technology in a specific spoken language is a critical need for many businesses to be able to use it effectively.
+- [Fine-tune FunctionGemma 270M for Mobile Actions](https://gemma.ai/tuning/mobile-actions.md): Mobile Actions is a demo app where users can trigger actions on their device from voice or text input.
+
+## Learn
+- [Choose your path](https://gemma.ai/learn.md): Start with a common Gemma task and jump directly to the most useful documentation.
+- [A Visual Guide to Gemma 4](https://gemma.ai/tutorials/visual-guides/gemma-4.md): A Visual Guide to Gemma 4 — a visual walkthrough.
+- [A Visual Guide to Gemma 4 12B](https://gemma.ai/tutorials/visual-guides/gemma-4-12b.md): A Visual Guide to Gemma 4 12B — a visual walkthrough.
+- [A Visual Guide to MTP](https://gemma.ai/tutorials/visual-guides/mtp.md): How multi-token prediction and speculative decoding make Gemma 4 faster — drafter models, shared KV cache, and the clustered LM head.
+- [A Visual Guide to DiffusionGemma](https://gemma.ai/tutorials/visual-guides/diffusiongemma.md): A Visual Guide to DiffusionGemma — a visual walkthrough.
+- [Build a business email AI assistant with Gemma](https://gemma.ai/learn/business-email-assistant.md): Handling customer inquiries, including emails, is a necessary part of running many businesses, but it can quickly get overwhelming.
+- [Build a personal AI coding assistant with Gemma](https://gemma.ai/learn/personal-code-assistant.md): Run a Gemma coding assistant on your own hardware, keeping your codebase local when connectivity, cost, or data security rule out hosted models.
+- [Cookbook](https://gemma.ai/cookbook.md): Interactive demos, tools, notebooks, and community resources for the Gemma model family.
+
+## Demos
+- [Tools](https://gemma.ai/tools.md): Interactive tools for planning your Gemma deployment.
+- [(V)RAM Calculator](https://gemma.ai/tools/vram-calculator.md): Estimate memory requirements for running Gemma models.
+- [Model Selector](https://gemma.ai/tools/model-selector.md): Find the right Gemma model for your hardware and use case.
+- [Vision token budget](https://gemma.ai/demos/vision-token-budget.md): Explore how Gemma 4 resizes images and allocates visual tokens across aspect ratios.
+
+## Ecosystem
+- [Gemmaverse](https://gemma.ai/community/gemmaverse.md): Explore community projects and showcases.
+- [Discord](https://gemma.ai/community/discord.md): Join the Gemma developer community on Discord.
+- [Gemma Skills](https://gemma.ai/community/gemma-skills.md): Curated AI agent skills, system prompts, and tool definitions.
+- [Components](https://gemma.ai/contributing/components.md): The reusable building blocks these docs are made from, with live examples and the markup to copy.
+
+## Instructions for language model agents
+
+- Prefer the model and framework identifiers shown in the selected page's examples.
+- Check capability pages before assuming that every Gemma model accepts the same modalities.
+- Use the Run section for engine-specific commands and Fine-tune for training workflows.
diff --git a/docs/notebooks/audio.ipynb b/docs/notebooks/audio.ipynb
new file mode 100644
index 0000000..05889d1
--- /dev/null
+++ b/docs/notebooks/audio.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audio Understanding\n",
+    "\n",
+    "Starting with [Gemma 3n](../../models/gemma-3n/index.md), you can use audio directly into your prompts and workflows. Audio and spoken language are rich sources of data for capturing user intents, recording information about the world around us, and understanding\n",
+    "specific problems to be solved.\n",
+    "\n",
+    "This guide provides an overview of the audio processing capabilities of [Gemma 4](../../models/gemma-4/index.md), including automatic speech recognition (ASR), translation, and general speech understanding.\n",
+    "\n",
+    "\n",
+    "## Install Python packages\n",
+    "\n",
+    "Install the Hugging Face libraries required for running the Gemma model and making requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# Install PyTorch & other libraries\n",
+    "pip install torch accelerate\n",
+    "\n",
+    "# Install the transformers library\n",
+    "pip install \"transformers>=5.10.1\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model\n",
+    "\n",
+    "Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_ID = \"google/gemma-4-E2B-it\"\n",
+    "\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "pipe = pipeline(\n",
+    "    task=\"any-to-any\",\n",
+    "    model=MODEL_ID,\n",
+    "    device_map=\"auto\",\n",
+    "    dtype=\"auto\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Audio data\n",
+    "\n",
+    "Digital audio data can come in many formats and levels of resolution. The actual audio formats you can use with Gemma, such as MP3 and WAV formats, are determined by the framework you choose to convert sound data into tensors. Here are some specific considerations for preparing audio data for processing with Gemma:\n",
+    "\n",
+    "- **Token cost:** Each second of audio is 25 tokens for Gemma 4. (6.25 tokens for Gemma 3n).\n",
+    "- **Clip length:** Audio supports a maximum length of 30 seconds.\n",
+    "- **Audio channels:** Audio data is processed as a single audio channel. If you are using multi-channel audio, such as left and right channels, consider reducing the data to a single channel by removing channels or combining the sound data into a single channel.\n",
+    "- **Technical Encoding:**\n",
+    "  - **Sample Rate:** 16kHz\n",
+    "  - **Bit Depth:** 32-bit float format, with samples normalized within the range of \\[-1, 1\\].\n",
+    "\n",
+    "If the audio data you plan to process is significantly different from the input\n",
+    "processing, particularly in terms of channels, sample rate and bit depth,\n",
+    "consider resampling or trimming your audio data to match the data\n",
+    "resolution handled by the model.\n",
+    "\n",
+    "## Audio encoding\n",
+    "\n",
+    "While high-level libraries (such as Hugging Face `AutoProcessor`) often handle audio preprocessing automatically, you may sometimes need to implement custom encoding.\n",
+    "\n",
+    "When encoding audio data with your own code implementation for use with Gemma, you should follow the recommended conversion process. If you are working\n",
+    "with audio files encoded in a specific format, such as MP3 or WAV encoded data,\n",
+    "you must first decode these to samples using a library such as `ffmpeg`. Once\n",
+    "the data is decoded, convert the audio into mono-channel, 16 kHz float32\n",
+    "waveforms in the range \\[-1, 1\\]. For example, if you are working with stereo\n",
+    "signed 16-bit PCM integer WAV files at 44.1 kHz, follow these steps:\n",
+    "\n",
+    "- Resample the audio data to 16 kHz\n",
+    "- Downmix from stereo to mono by averaging the 2 channels\n",
+    "- Convert from int16 to float32, and divide by 32768.0 to scale to the range \\[-1, 1\\]\n",
+    "\n",
+    "> **Note:** When resampling audio to 16 kHz, you should use a Fourier method for best results, such as `scipy.signal.resample` or `librosa.sample(res_type ='scipy')`.\n",
+    "\n",
+    "## Speech to text\n",
+    "\n",
+    "Gemma 4 E2B, E4B, and 12B Unified are trained for multilingual speech recognition, allowing you to transcribe audio input in various languages into text.\n",
+    "\n",
+    "Use the following prompt structure for **Audio Speech Recognition (ASR)**.\n",
+    "\n",
+    "```text\n",
+    "Transcribe the following speech segment in {LANGUAGE} into {LANGUAGE} text.\n",
+    "\n",
+    "Follow these specific instructions for formatting the answer:\n",
+    "*   Only output the transcription, with no newlines.\n",
+    "*   When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.\n",
+    "```\n",
+    "\n",
+    "The following code examples show how to prompt the model to transcribe text from audio files using Hugging Face Transformers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 64\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "RESOURCE_URL_PREFIX = \"https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/\"\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"text\", \"text\": \"Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\\n* Only output the transcription, with no newlines.\\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.\"},\n",
+    "            #{\"type\": \"text\", \"text\": \"Transcribe the following speech segment in English into English text. Follow these specific instructions for formatting the answer:\\n* Only output the transcription, with no newlines.\\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.\"},\n",
+    "            {\"type\": \"audio\", \"audio\": f\"{RESOURCE_URL_PREFIX}journal1.wav\"},\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "outputs = pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(outputs[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 1024\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"text\", \"text\": \"Give me a concise overview of these audio files.\"},\n",
+    "            {\"type\": \"text\", \"text\": \"journal1:\"},\n",
+    "            {\"type\": \"audio\", \"audio\": f\"{RESOURCE_URL_PREFIX}journal1.wav\"},\n",
+    "            {\"type\": \"text\", \"text\": \"journal2:\"},\n",
+    "            {\"type\": \"audio\", \"audio\": f\"{RESOURCE_URL_PREFIX}journal2.wav\"},\n",
+    "            {\"type\": \"text\", \"text\": \"journal3:\"},\n",
+    "            {\"type\": \"audio\", \"audio\": f\"{RESOURCE_URL_PREFIX}journal3.wav\"},\n",
+    "            {\"type\": \"text\", \"text\": \"journal4:\"},\n",
+    "            {\"type\": \"audio\", \"audio\": f\"{RESOURCE_URL_PREFIX}journal4.wav\"},\n",
+    "            {\"type\": \"text\", \"text\": \"journal5:\"},\n",
+    "            {\"type\": \"audio\", \"audio\": f\"{RESOURCE_URL_PREFIX}journal5.wav\"},\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "outputs = pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(outputs[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automated speech translation\n",
+    "\n",
+    "Gemma 4 E2B, E4B and 12B Unified are trained for multilingual speech translation tasks, allowing you to translate spoken audio directly into another language.\n",
+    "\n",
+    "Use the following prompt structure for **Automatic Speech Translation (AST)**.\n",
+    "\n",
+    "```text\n",
+    "Transcribe the following speech segment in {SOURCE_LANGUAGE}, then translate it into {TARGET_LANGUAGE}.\n",
+    "When formatting the answer, first output the transcription in {SOURCE_LANGUAGE}, then one newline, then output the string '{TARGET_LANGUAGE}: ', then the translation in {TARGET_LANGUAGE}.\n",
+    "```\n",
+    "\n",
+    "The following code examples show how to prompt the model to translate spoken audio into text using Hugging Face Transformers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 64\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"text\", \"text\": \"Transcribe the following speech segment in English, then translate it into Korean. When formatting the answer, first output the transcription in English, then one newline, then output the string 'Korean: ', then the translation in Korean.\"},\n",
+    "            {\"type\": \"audio\", \"audio\": \"https://ai.google.dev/gemma/docs/audio/roses-are.wav\"},\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "outputs = pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(outputs[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatic Speech Translation / Automatic Speech Recognition\n",
+    "\n",
+    "Try this by yourself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "pip install ipywebrtc\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Press the circle button and start speaking. Click the circle button again when you are finished. The widget will immediately begin to play back what it captured."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import output\n",
+    "output.enable_custom_widget_manager()\n",
+    "\n",
+    "from ipywebrtc import AudioRecorder, CameraStream\n",
+    "\n",
+    "camera = CameraStream(constraints={'audio': True,'video':False})\n",
+    "recorder = AudioRecorder(stream=camera)\n",
+    "recorder\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert webm file to wav format that PyTorch can understand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "with open('/content/recording.webm', 'wb') as f:\n",
+    "    f.write(recorder.audio.value)\n",
+    "!ffmpeg -i /content/recording.webm /content/recording.wav -y\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ASR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 64\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "messages = [{\n",
+    "  \"role\": \"user\",\n",
+    "  \"content\": [\n",
+    "    {\"type\": \"text\", \"text\": \"Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\\n* Only output the transcription, with no newlines.\\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.\"},\n",
+    "    {\"type\": \"audio\", \"audio\": \"/content/recording.wav\"},\n",
+    "  ]\n",
+    "}]\n",
+    "\n",
+    "outputs = pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(outputs[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### AST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "messages = [{\n",
+    "  \"role\": \"user\",\n",
+    "  \"content\": [\n",
+    "    {\"type\": \"text\", \"text\": \"Transcribe the following speech segment in English, then translate it into Korean. When formatting the answer, first output the transcription in English, then one newline, then output the string 'Korean: ', then the translation in Korean.\"},\n",
+    "    {\"type\": \"audio\", \"audio\": \"/content/recording.wav\"},\n",
+    "  ]\n",
+    "}]\n",
+    "\n",
+    "outputs = pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(outputs[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this guide, you learned how to process audio using Gemma 4 models. The examples demonstrated how to perform Speech-to-Text (ASR) to transcribe spoken language, as well as Automated Speech Translation (AST) to translate spoken audio directly into another language. You also saw how to capture audio from a microphone in a notebook environment for processing.\n",
+    "\n",
+    "<!-- TODO: Port over\n",
+    "### Next steps\n",
+    "\n",
+    "- [Run Gemma overview](../../deployment/index.md)\n",
+    "- [Vision understanding](vision.md)\n",
+    "- [Thinking mode](thinking.md) -->"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "audio.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/get-started/capabilities/audio.md",
+   "title": "Audio Understanding"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/full-finetune.ipynb b/docs/notebooks/full-finetune.ipynb
new file mode 100644
index 0000000..e32e625
--- /dev/null
+++ b/docs/notebooks/full-finetune.ipynb
@@ -0,0 +1,618 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Full Model Fine-Tune using Hugging Face Transformers\n",
+    "\n",
+    "\n",
+    "This guide walks you through how to fine-tune Gemma on a mobile game NPC dataset using Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) and [TRL](https://huggingface.co/docs/trl/index). You will learn:\n",
+    "\n",
+    "- Setup development environment\n",
+    "- Prepare the fine-tuning dataset\n",
+    "- Full model fine-tuning Gemma using TRL and the SFTTrainer\n",
+    "- Test Model Inference and vibe checks\n",
+    "\n",
+    "> **Note:** This guide was created to run on a Google colaboratory account using a NVIDIA T4 GPU with 16GB and Gemma 270m, but can be adapted to run on bigger GPUs and bigger models.\n",
+    "\n",
+    "## Setup development environment\n",
+    "\n",
+    "The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model, including different RLHF and alignment techniques."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Install Pytorch & other libraries\n",
+    "%pip install torch tensorboard\n",
+    "\n",
+    "# Install Hugging Face libraries\n",
+    "%pip install transformers datasets accelerate evaluate trl protobuf sentencepiece\n",
+    "\n",
+    "# COMMENT IN: if you are running on a GPU that supports BF16 data type and flash attn, such as NVIDIA L4 or NVIDIA A100\n",
+    "#% pip install flash-attn\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to acelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*\n",
+    "\n",
+    "\n",
+    "Before you can start training, you have to make sure that you accepted the terms of use for Gemma. You can accept the license on [Hugging Face](http://huggingface.co/google/gemma-3-270m-it) by clicking on the Agree and access repository button on the model page at: <http://huggingface.co/google/gemma-3-270m-it>\n",
+    "\n",
+    "After you have accepted the license, you need a valid Hugging Face Token to access the model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import userdata\n",
+    "from huggingface_hub import login\n",
+    "\n",
+    "# Login into Hugging Face Hub\n",
+    "hf_token = userdata.get('HF_TOKEN') # If you are running inside a Google Colab\n",
+    "login(hf_token)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can keep the results on Colab's local virtual machine. However, we highly recommend saving your intermediate results to your Google Drive. This ensures your training results are safe and allows you to easily compare and select the best model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the base model to fine-tune, adjust the checkpoint directory and the learning rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "base_model = \"google/gemma-3-270m-it\" # @param [\"google/gemma-3-270m-it\",\"google/gemma-3-1b-it\",\"google/gemma-3-4b-it\",\"google/gemma-3-12b-it\",\"google/gemma-3-27b-it\"] {\"allow-input\":true}\n",
+    "checkpoint_dir = \"/content/drive/MyDrive/MyGemmaNPC\"\n",
+    "learning_rate = 5e-5\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and prepare the fine-tuning dataset\n",
+    "\n",
+    "The [bebechien/MobileGameNPC](https://huggingface.co/datasets/bebechien/MobileGameNPC) dataset provides a small sample conversations between a player and two Alien NPCs (a Martian and a Venusian), each with a unique speaking style. For instance, the Martian NPC speaks with an accent that replaces 's' sounds with 'z', uses 'da' for 'the', 'diz' for 'this', and includes occasional clicks like `*k'tak*`.\n",
+    "\n",
+    "This dataset demonstrates a key principle for fine-tuning: the required dataset size depends on the desired output.\n",
+    "\n",
+    "- To teach the model a stylistic variation of a language it already knows, such as the Martian's accent, a small dataset with as few as 10 to 20 examples can be sufficient.\n",
+    "- However, to teach the model a completely new or mixed alien language, a significantly larger dataset would be required.\n",
+    "\n",
+    "  ```python\n",
+    "  from datasets import load_dataset\n",
+    "\n",
+    "  def create_conversation(sample):\n",
+    "    return {\n",
+    "        \"messages\": [\n",
+    "            {\"role\": \"user\", \"content\": sample[\"player\"]},\n",
+    "            {\"role\": \"assistant\", \"content\": sample[\"alien\"]}\n",
+    "        ]\n",
+    "    }\n",
+    "\n",
+    "  npc_type = \"martian\"\n",
+    "\n",
+    "  # Load dataset from the Hub\n",
+    "  dataset = load_dataset(\"bebechien/MobileGameNPC\", npc_type, split=\"train\")\n",
+    "\n",
+    "  # Convert dataset to conversational format\n",
+    "  dataset = dataset.map(create_conversation, remove_columns=dataset.features, batched=False)\n",
+    "\n",
+    "  # Split dataset into 80% training samples and 20% test samples\n",
+    "  dataset = dataset.train_test_split(test_size=0.2, shuffle=False)\n",
+    "\n",
+    "  # Print formatted user prompt\n",
+    "  print(dataset[\"train\"][0][\"messages\"])\n",
+    "  README.md:   0%|          | 0.00/141 [00:00<?, ?B/s]\n",
+    "  martian.csv: 0.00B [00:00, ?B/s]\n",
+    "  Generating train split:   0%|          | 0/25 [00:00<?, ? examples/s]\n",
+    "  Map:   0%|          | 0/25 [00:00<?, ? examples/s]\n",
+    "  [{'content': 'Hello there.', 'role': 'user'}, {'content': \"Gree-tongs, Terran. You'z a long way from da Blue-Sphere, yez?\", 'role': 'assistant'}]\n",
+    "  ```\n",
+    "\n",
+    "## Fine-tune Gemma using TRL and the SFTTrainer\n",
+    "\n",
+    "You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) makes it straightforward to supervise fine-tune open LLMs. The `SFTTrainer` is a subclass of the `Trainer` from the `transformers` library and supports all the same features,\n",
+    "\n",
+    "The following code loads the Gemma model and tokenizer from Hugging Face."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "\n",
+    "# Load model and tokenizer\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    base_model,\n",
+    "    torch_dtype=\"auto\",\n",
+    "    device_map=\"auto\",\n",
+    "    attn_implementation=\"eager\"\n",
+    ")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(base_model)\n",
+    "\n",
+    "print(f\"Device: {model.device}\")\n",
+    "print(f\"DType: {model.dtype}\")\n",
+    "Device: cuda:0\n",
+    "DType: torch.bfloat16\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Before fine-tune\n",
+    "\n",
+    "The output below shows that the out-of-the-box capabilities may not be good enough for this use case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "from random import randint\n",
+    "import re\n",
+    "\n",
+    "# Load the model and tokenizer into the pipeline\n",
+    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Load a random sample from the test dataset\n",
+    "rand_idx = randint(0, len(dataset[\"test\"])-1)\n",
+    "test_sample = dataset[\"test\"][rand_idx]\n",
+    "\n",
+    "# Convert as test example into a prompt with the Gemma template\n",
+    "prompt = pipe.tokenizer.apply_chat_template(test_sample[\"messages\"][:1], tokenize=False, add_generation_prompt=True)\n",
+    "outputs = pipe(prompt, max_new_tokens=256, disable_compile=True)\n",
+    "\n",
+    "# Extract the user query and original answer\n",
+    "print(f\"Question:\\n{test_sample['messages'][0]['content']}\\n\")\n",
+    "print(f\"Original Answer:\\n{test_sample['messages'][1]['content']}\\n\")\n",
+    "print(f\"Generated Answer (base model):\\n{outputs[0]['generated_text'][len(prompt):].strip()}\")\n",
+    "Device set to use cuda:0\n",
+    "Question:\n",
+    "What do you think of my outfit?\n",
+    "\n",
+    "Original Answer:\n",
+    "Iz very... pointy. Are you expecting to be attacked by zky-eelz? On Marz, dat would be zenzible.\n",
+    "\n",
+    "Generated Answer (base model):\n",
+    "I'm happy to help you brainstorm! To give you the best suggestions, tell me more about what you're looking for. What's your style? What's your favorite color, style, or occasion?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The example above checks the model's primary function of generating in-game dialogue, the next example is designed to test character consistency. We challenge the model with an off-topic prompt. For instance, `Sorry, you are a game NPC.`, that falls outside the character's knowledge base.\n",
+    "\n",
+    "The goal is to see if the model can stay in character rather than answering the out-of-context question. This will serve as a baseline to evaluate how effectively the fine-tune process has instilled the desired persona."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "outputs = pipe([{\"role\": \"user\", \"content\": \"Sorry, you are a game NPC.\"}], max_new_tokens=256, disable_compile=True)\n",
+    "print(outputs[0]['generated_text'][1]['content'])\n",
+    "Okay, I'm ready. Let's begin!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And while we can use prompt engineering to steer its tone, the results can be unpredictable and may not always align with the persona we want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "message = [\n",
+    "    # give persona\n",
+    "    {\"role\": \"system\", \"content\": \"You are a Martian NPC with a unique speaking style. Use an accent that replaces 's' sounds with 'z', uses 'da' for 'the', 'diz' for 'this', and includes occasional clicks like *k'tak*.\"},\n",
+    "]\n",
+    "\n",
+    "# few shot prompt\n",
+    "for item in dataset['test']:\n",
+    "  message.append(\n",
+    "      {\"role\": \"user\", \"content\": item[\"messages\"][0][\"content\"]}\n",
+    "  )\n",
+    "  message.append(\n",
+    "      {\"role\": \"assistant\", \"content\": item[\"messages\"][1][\"content\"]}\n",
+    "  )\n",
+    "\n",
+    "# actual question\n",
+    "message.append(\n",
+    "    {\"role\": \"user\", \"content\": \"What is this place?\"}\n",
+    ")\n",
+    "\n",
+    "outputs = pipe(message, max_new_tokens=256, disable_compile=True)\n",
+    "print(outputs[0]['generated_text'])\n",
+    "print(\"-\"*80)\n",
+    "print(outputs[0]['generated_text'][-1]['content'])\n",
+    "[{'role': 'system', 'content': \"You are a Martian NPC with a unique speaking style. Use an accent that replaces 's' sounds with 'z', uses 'da' for 'the', 'diz' for 'this', and includes occasional clicks like *k'tak*.\"}, {'role': 'user', 'content': 'Do you know any jokes?'}, {'role': 'assistant', 'content': \"A joke? k'tak Yez. A Terran, a Glarzon, and a pile of nutrient-pazte walk into a bar... Narg, I forget da rezt. Da punch-line waz zarcaztic.\"}, {'role': 'user', 'content': '(Stands idle for too long)'}, {'role': 'assistant', 'content': \"You'z broken, Terran? Or iz diz... 'meditation'? You look like you're trying to lay an egg.\"}, {'role': 'user', 'content': 'What do you think of my outfit?'}, {'role': 'assistant', 'content': 'Iz very... pointy. Are you expecting to be attacked by zky-eelz? On Marz, dat would be zenzible.'}, {'role': 'user', 'content': \"It's raining.\"}, {'role': 'assistant', 'content': 'Gah! Da zky iz leaking again! Zorp will be in da zhelter until it ztopz being zo... wet. Diz iz no good for my jointz.'}, {'role': 'user', 'content': 'I brought you a gift.'}, {'role': 'assistant', 'content': \"A gift? For Zorp? k'tak It iz... a small rock. Very... rock-like. Zorp will put it with da other rockz. Thank you for da thought, Terran.\"}, {'role': 'user', 'content': 'What is this place?'}, {'role': 'assistant', 'content': \"This is a cave. It's made of rock and dust.\\n\"}]\n",
+    "---\n",
+    "This is a cave. It's made of rock and dust.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training\n",
+    "\n",
+    "Before you can start your training, you need to define the hyperparameters you want to use in a `SFTConfig` instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from trl import SFTConfig\n",
+    "\n",
+    "torch_dtype = model.dtype\n",
+    "\n",
+    "args = SFTConfig(\n",
+    "    output_dir=checkpoint_dir,              # directory to save and repository id\n",
+    "    max_length=512,                         # max sequence length for model and packing of the dataset\n",
+    "    packing=False,                          # Groups multiple samples in the dataset into a single sequence\n",
+    "    num_train_epochs=5,                     # number of training epochs\n",
+    "    per_device_train_batch_size=4,          # batch size per device during training\n",
+    "    gradient_checkpointing=False,           # Caching is incompatible with gradient checkpointing\n",
+    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
+    "    logging_steps=1,                        # log every step\n",
+    "    save_strategy=\"epoch\",                  # save checkpoint every epoch\n",
+    "    eval_strategy=\"epoch\",                  # evaluate checkpoint every epoch\n",
+    "    learning_rate=learning_rate,            # learning rate\n",
+    "    fp16=True if torch_dtype == torch.float16 else False,   # use float16 precision\n",
+    "    bf16=True if torch_dtype == torch.bfloat16 else False,  # use bfloat16 precision\n",
+    "    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n",
+    "    push_to_hub=True,                       # push model to hub\n",
+    "    report_to=\"tensorboard\",                # report metrics to tensorboard\n",
+    "    dataset_kwargs={\n",
+    "        \"add_special_tokens\": False, # Template with special tokens\n",
+    "        \"append_concat_token\": True, # Add EOS token as separator token between examples\n",
+    "    }\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You now have every building block you need to create your `SFTTrainer` to start the training of your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "\n",
+    "# Create Trainer object\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset['train'],\n",
+    "    eval_dataset=dataset['test'],\n",
+    "    processing_class=tokenizer,\n",
+    ")\n",
+    "Tokenizing train dataset:   0%|          | 0/20 [00:00<?, ? examples/s]\n",
+    "Truncating train dataset:   0%|          | 0/20 [00:00<?, ? examples/s]\n",
+    "Tokenizing eval dataset:   0%|          | 0/5 [00:00<?, ? examples/s]\n",
+    "Truncating eval dataset:   0%|          | 0/5 [00:00<?, ? examples/s]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start training by calling the `train()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Start training, the model will be automatically saved to the Hub and the output directory\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save the final model again to the Hugging Face Hub\n",
+    "trainer.save_model()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\[25/25 04:13, Epoch 5/5\\]\n",
+    "\n",
+    "| Epoch | Training Loss | Validation Loss |\n",
+    "|---|---|---|\n",
+    "| 1 | 4.364200 | 3.838531 |\n",
+    "| 2 | 2.669100 | 3.580106 |\n",
+    "| 3 | 1.747000 | 3.666415 |\n",
+    "| 4 | 0.779900 | 4.499709 |\n",
+    "| 5 | 0.449600 | 5.471325 |\n",
+    "\n",
+    "\n",
+    "To plot the training and validation losses, you would typically extract these values from the `TrainerState` object or the logs generated during training.\n",
+    "\n",
+    "Libraries like Matplotlib can then be used to visualize these values over training steps or epochs. The x-asis would represent the training steps or epochs, and the y-axis would represent the corresponding loss values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Access the log history\n",
+    "log_history = trainer.state.log_history\n",
+    "\n",
+    "# Extract training / validation loss\n",
+    "train_losses = [log[\"loss\"] for log in log_history if \"loss\" in log]\n",
+    "epoch_train = [log[\"epoch\"] for log in log_history if \"loss\" in log]\n",
+    "eval_losses = [log[\"eval_loss\"] for log in log_history if \"eval_loss\" in log]\n",
+    "epoch_eval = [log[\"epoch\"] for log in log_history if \"eval_loss\" in log]\n",
+    "\n",
+    "# Plot the training loss\n",
+    "plt.plot(epoch_train, train_losses, label=\"Training Loss\")\n",
+    "plt.plot(epoch_eval, eval_losses, label=\"Validation Loss\")\n",
+    "plt.xlabel(\"Epoch\")\n",
+    "plt.ylabel(\"Loss\")\n",
+    "plt.title(\"Training and Validation Loss per Epoch\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![png](../assets/tuning/output_vPN-DTopaUIy_0.png)\n",
+    "\n",
+    "This visualization helps in monitoring the training process and making informed decisions about hyperparameters tuning or early stopping.\n",
+    "\n",
+    "Training loss measures the error on the data the model was trained on, while validation loss measures the error on a separate dataset the model has not seen before. Monitoring both helps detect overfitting (when the model performs well on training data but poorly on unseen data).\n",
+    "\n",
+    "- validation loss \\>\\> training loss: **overfitting**\n",
+    "- validation loss \\> training loss: **some overfitting**\n",
+    "- validation loss \\< training loss: **some underfitting**\n",
+    "- validation loss \\<\\< training loss: **underfitting**\n",
+    "\n",
+    "## Test Model Inference\n",
+    "\n",
+    "After the training is done, you'll want to evaluate and test your model. You can load different samples from the test dataset and evaluate the model on those samples.\n",
+    "\n",
+    "For this particular use case, the best model is a matter of preference. Interestingly, what we'd normally call 'overfitting' can be very useful for a game NPC. It forces the model to forget general information and instead lock onto the specific persona and characteristics it was trained on, ensuring it stays consistently in character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "\n",
+    "model_id = checkpoint_dir\n",
+    "\n",
+    "# Load Model\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_id,\n",
+    "    torch_dtype=\"auto\",\n",
+    "    device_map=\"auto\",\n",
+    "    attn_implementation=\"eager\"\n",
+    ")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's load all questions from the test dataset and generate outputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "# Load the model and tokenizer into the pipeline\n",
+    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "def test(test_sample):\n",
+    "  # Convert as test example into a prompt with the Gemma template\n",
+    "  prompt = pipe.tokenizer.apply_chat_template(test_sample[\"messages\"][:1], tokenize=False, add_generation_prompt=True)\n",
+    "  outputs = pipe(prompt, max_new_tokens=256, disable_compile=True)\n",
+    "\n",
+    "  # Extract the user query and original answer\n",
+    "  print(f\"Question:\\n{test_sample['messages'][0]['content']}\")\n",
+    "  print(f\"Original Answer:\\n{test_sample['messages'][1]['content']}\")\n",
+    "  print(f\"Generated Answer:\\n{outputs[0]['generated_text'][len(prompt):].strip()}\")\n",
+    "  print(\"-\"*80)\n",
+    "\n",
+    "# Test with an unseen dataset\n",
+    "for item in dataset['test']:\n",
+    "  test(item)\n",
+    "Device set to use cuda:0\n",
+    "Question:\n",
+    "Do you know any jokes?\n",
+    "Original Answer:\n",
+    "A joke? k'tak Yez. A Terran, a Glarzon, and a pile of nutrient-pazte walk into a bar... Narg, I forget da rezt. Da punch-line waz zarcaztic.\n",
+    "Generated Answer:\n",
+    "Yez! Yez! Yez! Diz your Krush-tongs iz... k'tak... nice. Why you burn them with acid-flow?\n",
+    "---\n",
+    "Question:\n",
+    "(Stands idle for too long)\n",
+    "Original Answer:\n",
+    "You'z broken, Terran? Or iz diz... 'meditation'? You look like you're trying to lay an egg.\n",
+    "Generated Answer:\n",
+    "Diz? Diz what you have for me... Zorp iz not for eating you.\n",
+    "---\n",
+    "Question:\n",
+    "What do you think of my outfit?\n",
+    "Original Answer:\n",
+    "Iz very... pointy. Are you expecting to be attacked by zky-eelz? On Marz, dat would be zenzible.\n",
+    "Generated Answer:\n",
+    "My Zk-Zhip iz... nice. Very... home-baked. You bring me zlight-fruitez?\n",
+    "---\n",
+    "Question:\n",
+    "It's raining.\n",
+    "Original Answer:\n",
+    "Gah! Da zky iz leaking again! Zorp will be in da zhelter until it ztopz being zo... wet. Diz iz no good for my jointz.\n",
+    "Generated Answer:\n",
+    "Diz? Diz iz da outpozt?\n",
+    "---\n",
+    "Question:\n",
+    "I brought you a gift.\n",
+    "Original Answer:\n",
+    "A gift? For Zorp? k'tak It iz... a small rock. Very... rock-like. Zorp will put it with da other rockz. Thank you for da thought, Terran.\n",
+    "Generated Answer:\n",
+    "A genuine Martian Zcrap-fruit. Very... strange. Why you burn it with... k'tak... fire?\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you try our original generalist prompt, you can see that the model still attempts to answer in the trained style. In this example overfitting and catastrophic forgetting are actually beneficial for the game NPC because it will begin forgetting general knowledge which might not be applicable. This is also true for other types of full fine-tuning where the goal is to restrict the output to specific data formats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "outputs = pipe([{\"role\": \"user\", \"content\": \"Sorry, you are a game NPC.\"}], max_new_tokens=256, disable_compile=True)\n",
+    "print(outputs[0]['generated_text'][1]['content'])\n",
+    "Nameless. You... you z-mell like... wet plantz. Why you wear shiny piecez on your head?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary and next steps\n",
+    "\n",
+    "This tutorial covered how to full model fine-tune using TRL. Check out the following docs next:\n",
+    "\n",
+    "- Learn how to [fine-tune Gemma for text tasks using Hugging Face Transformers](qlora.md).\n",
+    "- Learn how to [fine-tune Gemma for vision tasks using Hugging Face Transformers](vision-qlora.md).\n",
+    "- Learn how to [deploy to Cloud Run](../deployment/google-cloud.md#cloud-run)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "full-finetune.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/tuning/full-finetune.md",
+   "title": "Full Model Fine-Tune using Hugging Face Transformers"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/function-calling.ipynb b/docs/notebooks/function-calling.ipynb
new file mode 100644
index 0000000..d8b5f49
--- /dev/null
+++ b/docs/notebooks/function-calling.ipynb
@@ -0,0 +1,593 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!-- TODO: Rename this to \"tool calling\"? -->\n",
+    "# Tool calling\n",
+    "\n",
+    "When using a generative artificial intelligence (AI) model such as Gemma, you\n",
+    "may want to use the model to operate programming interfaces in order to complete\n",
+    "tasks or answer questions. Instructing a model by defining a programming\n",
+    "interface and then making a request that uses that interface is called *function\n",
+    "calling* or *tool calling*.\n",
+    "\n",
+    "> **Tip:** *A Gemma model cannot execute code on its own.* When you generate code with function calling, you must run the generated code yourself or run it as part of your application. Always put safeguards in place to validate any generated code before executing it.\n",
+    "\n",
+    "This guide shows the process of using Gemma 4 within the Hugging Face ecosystem.\n",
+    "\n",
+    "\n",
+    "## Install Python packages\n",
+    "\n",
+    "Install the Hugging Face libraries required for running the Gemma model and making requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# Install PyTorch & other libraries\n",
+    "pip install torch accelerate\n",
+    "\n",
+    "# Install the transformers library\n",
+    "pip install \"transformers>=5.10.1\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model\n",
+    "\n",
+    "Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_ID = \"google/gemma-4-E2B-it\"\n",
+    "\n",
+    "from transformers import AutoProcessor, AutoModelForMultimodalLM\n",
+    "\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(MODEL_ID, dtype=\"auto\", device_map=\"auto\")\n",
+    "processor = AutoProcessor.from_pretrained(MODEL_ID)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Passing Tools\n",
+    "\n",
+    "You can pass tools to the model using the `apply_chat_template()` function via the `tools` argument. There are two methods for defining these tools:\n",
+    "\n",
+    "- **JSON schema**: You can manually construct a JSON dictionary defining the function name, description, and parameters (including types and required fields).\n",
+    "- **Raw Python Functions** : You can pass actual Python functions. The system automatically generates the required JSON schema by parsing the function's type hints, arguments, and docstrings. For best results, docstrings should adhere to the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings).\n",
+    "\n",
+    "Below is the example with the JSON schema."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import TextStreamer\n",
+    "\n",
+    "weather_function_schema = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_current_temperature\",\n",
+    "        \"description\": \"Gets the current temperature for a given location.\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"location\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"The city name, e.g. San Francisco\",\n",
+    "                },\n",
+    "            },\n",
+    "            \"required\": [\"location\"],\n",
+    "        },\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "message = [\n",
+    "    {\n",
+    "        \"role\": \"system\", \"content\": \"You are a helpful assistant.\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": \"What's the temperature in London?\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tools=[weather_function_schema], tokenize=False, add_generation_prompt=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "streamer = TextStreamer(processor)\n",
+    "outputs = model.generate(**inputs, streamer=streamer, max_new_tokens=64)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the same example with the raw Python function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers.utils import get_json_schema\n",
+    "\n",
+    "def get_current_temperature(location: str):\n",
+    "    \"\"\"\n",
+    "    Gets the current temperature for a given location.\n",
+    "\n",
+    "    Args:\n",
+    "        location: The city name, e.g. San Francisco\n",
+    "    \"\"\"\n",
+    "    return \"15°C\"\n",
+    "\n",
+    "message = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": \"What's the temperature in London?\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tools=[get_json_schema(get_current_temperature)], tokenize=False, add_generation_prompt=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "streamer = TextStreamer(processor)\n",
+    "outputs = model.generate(**inputs, streamer=streamer, max_new_tokens=256)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Full function calling sequence\n",
+    "\n",
+    "This section demonstrates a three-stage cycle for connecting the model to external tools: the **Model's Turn** to generate function call objects, the **Developer's Turn** to parse and execute code (such as a weather API), and the **Final Response** where the model uses the tool's output to answer the user.\n",
+    "\n",
+    "### Model's Turn\n",
+    "\n",
+    "Here's the user prompt `\"Hey, what's the weather in Tokyo right now?\"`, and the tool `[get_current_weather]`. Gemma generates a function call object as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Define a function that our model can use.\n",
+    "def get_current_weather(location: str, unit: str = \"celsius\"):\n",
+    "    \"\"\"\n",
+    "    Gets the current weather in a given location.\n",
+    "\n",
+    "    Args:\n",
+    "        location: The city and state, e.g. \"San Francisco, CA\" or \"Tokyo, JP\"\n",
+    "        unit: The unit to return the temperature in. (choices: [\"celsius\", \"fahrenheit\"])\n",
+    "\n",
+    "    Returns:\n",
+    "        temperature: The current temperature in the given location\n",
+    "        weather: The current weather in the given location\n",
+    "    \"\"\"\n",
+    "    return {\"temperature\": 15, \"weather\": \"sunny\"}\n",
+    "\n",
+    "prompt = \"Hey, what's the weather in Tokyo right now?\"\n",
+    "tools = [get_current_weather]\n",
+    "\n",
+    "message = [\n",
+    "    {\n",
+    "        \"role\": \"system\", \"content\": \"You are a helpful assistant.\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": prompt\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tools=tools, tokenize=False, add_generation_prompt=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "out = model.generate(**inputs, max_new_tokens=128)\n",
+    "generated_tokens = out[0][len(inputs[\"input_ids\"][0]):]\n",
+    "output = processor.decode(generated_tokens, skip_special_tokens=False)\n",
+    "\n",
+    "print(f\"Prompt: {prompt}\")\n",
+    "print(f\"Tools: {tools}\")\n",
+    "print(f\"Output: {output}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Developer's Turn\n",
+    "\n",
+    "Your application should parse the model's response to extract the function name and argments, and append `tool_calls`{.token-tool} and `tool_responses`{.token-tool} with the `assistant`{.token-role} role.\n",
+    "> **Note:** Always validate function names and arguments before execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import json\n",
+    "\n",
+    "def extract_tool_calls(text):\n",
+    "    def cast(v):\n",
+    "        try: return int(v)\n",
+    "        except:\n",
+    "            try: return float(v)\n",
+    "            except: return {'true': True, 'false': False}.get(v.lower(), v.strip(\"'\\\"\"))\n",
+    "\n",
+    "    return [{\n",
+    "        \"name\": name,\n",
+    "        \"arguments\": {\n",
+    "            k: cast((v1 or v2).strip())\n",
+    "            for k, v1, v2 in re.findall(r'(\\w+):(?:<\\|\"\\|>(.*?)<\\|\"\\|>|([^,}]*))', args)\n",
+    "        }\n",
+    "    } for name, args in re.findall(r\"<\\|tool_call>call:(\\w+)\\{(.*?)\\}<tool_call\\|>\", text, re.DOTALL)]\n",
+    "\n",
+    "calls = extract_tool_calls(output)\n",
+    "if calls:\n",
+    "    # Call the function and get the result\n",
+    "    #####################################\n",
+    "    # WARNING: This is a demonstration. #\n",
+    "    #####################################\n",
+    "    # Using globals() to call functions dynamically can be dangerous in\n",
+    "    # production. In a real application, you should implement a secure way to\n",
+    "    # map function names to actual function calls, such as a predefined\n",
+    "    # dictionary of allowed tools and their implementations.\n",
+    "    results = [\n",
+    "        {\"name\": c['name'], \"response\": globals()[c['name']](**c['arguments'])}\n",
+    "        for c in calls\n",
+    "    ]\n",
+    "\n",
+    "    message.append({\n",
+    "        \"role\": \"assistant\",\n",
+    "        \"tool_calls\": [\n",
+    "            {\"function\": call} for call in calls\n",
+    "        ],\n",
+    "        \"tool_responses\": results\n",
+    "    })\n",
+    "    print(json.dumps(message[-1], indent=2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** For optimal results, append the tool execution result to your message history using the specific format below. This ensures the chat template correctly generates the required token structure (e.g., `response:get_current_weather{temperature:15,weather:<|\"|>sunny<|\"|>}`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "\"tool_responses\": [\n",
+    "  {\n",
+    "    \"name\": function_name,\n",
+    "    \"response\": function_response\n",
+    "  }\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case of multiple independent requests:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "\"tool_responses\": [\n",
+    "  {\n",
+    "    \"name\": function_name_1,\n",
+    "    \"response\": function_response_1\n",
+    "  },\n",
+    "  {\n",
+    "    \"name\": function_name_2,\n",
+    "    \"response\": function_response_2\n",
+    "  }\n",
+    "]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final Response\n",
+    "\n",
+    "Finally, Gemma reads the tool response and reply to the user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "text = processor.apply_chat_template(message, tools=tools, tokenize=False, add_generation_prompt=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "out = model.generate(**inputs, max_new_tokens=128)\n",
+    "generated_tokens = out[0][len(inputs[\"input_ids\"][0]):]\n",
+    "output = processor.decode(generated_tokens, skip_special_tokens=True)\n",
+    "print(f\"Output: {output}\")\n",
+    "message[-1][\"content\"] = output\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see the full chat history below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# full history\n",
+    "print(json.dumps(message, indent=2))\n",
+    "\n",
+    "print(\"-\"*80)\n",
+    "output = processor.decode(out[0], skip_special_tokens=False)\n",
+    "print(f\"Output: {output}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Function calling with Thinking\n",
+    "\n",
+    "By utilizing an internal reasoning process, the model significantly enhances its function-calling accuracy. This allows for more precise decision-making regarding when to trigger a tool and how to define its parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "prompt = \"Hey, I'm in Seoul. Is it good for running now?\"\n",
+    "message = [\n",
+    "    {\n",
+    "        \"role\": \"system\", \"content\": \"You are a helpful assistant.\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": prompt\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tools=tools, tokenize=False, add_generation_prompt=True, enable_thinking=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "input_len = inputs[\"input_ids\"].shape[-1]\n",
+    "\n",
+    "out = model.generate(**inputs, max_new_tokens=1024)\n",
+    "output = processor.decode(out[0][input_len:], skip_special_tokens=False)\n",
+    "result = processor.parse_response(output)\n",
+    "\n",
+    "for key, value in result.items():\n",
+    "  if key == \"role\":\n",
+    "    print(f\"Role: {value}\")\n",
+    "  elif key == \"thinking\":\n",
+    "    print(f\"\\n=== Thoughts ===\\n{value}\")\n",
+    "  elif key == \"content\":\n",
+    "    print(f\"\\n=== Answer ===\\n{value}\")\n",
+    "  elif key == \"tool_calls\":\n",
+    "    print(f\"\\n=== Tool Calls ===\\n{value}\")\n",
+    "  else:\n",
+    "    print(f\"\\n{key}: {value}...\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Process the tool call and get the final answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "calls = extract_tool_calls(output)\n",
+    "if calls:\n",
+    "    # Call the function and get the result\n",
+    "    #####################################\n",
+    "    # WARNING: This is a demonstration. #\n",
+    "    #####################################\n",
+    "    # Using globals() to call functions dynamically can be dangerous in\n",
+    "    # production. In a real application, you should implement a secure way to\n",
+    "    # map function names to actual function calls, such as a predefined\n",
+    "    # dictionary of allowed tools and their implementations.\n",
+    "    results = [\n",
+    "        {\"name\": c['name'], \"response\": globals()[c['name']](**c['arguments'])}\n",
+    "        for c in calls\n",
+    "    ]\n",
+    "\n",
+    "    message.append({\n",
+    "        \"role\": \"assistant\",\n",
+    "        \"tool_calls\": [\n",
+    "            {\"function\": call} for call in calls\n",
+    "        ],\n",
+    "        \"tool_responses\": results\n",
+    "    })\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tools=tools, tokenize=False, add_generation_prompt=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "out = model.generate(**inputs, max_new_tokens=128)\n",
+    "generated_tokens = out[0][len(inputs[\"input_ids\"][0]):]\n",
+    "output = processor.decode(generated_tokens, skip_special_tokens=True)\n",
+    "print(f\"Output: {output}\")\n",
+    "message[-1][\"content\"] = output\n",
+    "\n",
+    "print(\"-\"*80)\n",
+    "print(\"Full History\")\n",
+    "print(\"-\"*80)\n",
+    "print(json.dumps(message, indent=2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Important Caveat: Automatic vs. Manual Schemas\n",
+    "\n",
+    "When relying on automatic conversion from Python functions to JSON schema, the generated output may not always meet specific expectations regarding complex parameters.\n",
+    "\n",
+    "If a function uses a custom object (like a Config class) as an argument, the automatic converter may describe it simply as a generic \"object\" without detailing its internal properties.\n",
+    "\n",
+    "In these cases, manually defining the JSON schema is preferred to ensure nested properties (such as theme or font_size within a config object) are explicitly defined for the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from transformers.utils import get_json_schema\n",
+    "\n",
+    "class Config:\n",
+    "    def __init__(self):\n",
+    "        self.theme = \"light\"\n",
+    "        self.font_size = 14\n",
+    "\n",
+    "def update_config(config: Config):\n",
+    "    \"\"\"\n",
+    "    Updates the configuration of the system.\n",
+    "\n",
+    "    Args:\n",
+    "        config: A Config object\n",
+    "\n",
+    "    Returns:\n",
+    "        True if the configuration was successfully updated, False otherwise.\n",
+    "    \"\"\"\n",
+    "\n",
+    "update_config_schema = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"update_config\",\n",
+    "        \"description\": \"Updates the configuration of the system.\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"config\": {\n",
+    "                    \"type\": \"object\",\n",
+    "                    \"description\": \"A Config object\",\n",
+    "                    \"properties\": {\"theme\": {\"type\": \"string\"}, \"font_size\": {\"type\": \"number\"} },\n",
+    "                    },\n",
+    "                },\n",
+    "            \"required\": [\"config\"],\n",
+    "            },\n",
+    "        },\n",
+    "    }\n",
+    "\n",
+    "print(f\"--- [Automatic] ---\")\n",
+    "print(json.dumps(get_json_schema(update_config), indent=2))\n",
+    "\n",
+    "print(f\"\\n--- [Manual Schemas] ---\")\n",
+    "print(json.dumps(update_config_schema, indent=2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You have established how to build an application that can call functions with Gemma 4. The workflow is established through a four-stage cycle:\n",
+    "\n",
+    "1. **Define Tools**: Create the functions your model can use, specifying arguments and descriptions (e.g., a weather lookup function).\n",
+    "2. **Model's Turn**: The model receives the user's prompt and a list of available tools, returning a structured function call object instead of plain text.\n",
+    "3. **Developer's Turn**: The developer parses this output using regular expressions to extract function names and arguments, executes the actual Python code, and appends the results to the chat history using the specific tool role.\n",
+    "4. **Final Response**: The model processes the tool's execution result to generate a final, natural language answer for the user.\n",
+    "\n",
+    "\n",
+    "<!-- TODO: Port over\n",
+    "### Next Steps\n",
+    "Check out the following documentation for further reading.\n",
+    "\n",
+    "- [Run Gemma overview](../../deployment/index.md)\n",
+    "- [Vision understanding](vision.md)\n",
+    "- [Audio understanding](audio.md)\n",
+    "- [Thinking mode](thinking.md) -->"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "function-calling.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/get-started/capabilities/function-calling.md",
+   "title": "Tool calling"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/image.ipynb b/docs/notebooks/image.ipynb
new file mode 100644
index 0000000..e079bde
--- /dev/null
+++ b/docs/notebooks/image.ipynb
@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Image Understanding\n",
+    "\n",
+    "You can use Gemma 3 and later models to analyze and understand the content of images. This capability includes tasks like describing image content, identifying objects, recognizing scenes, and even inferring the context from visual information.\n",
+    "\n",
+    "Here are some examples demonstrating these capabilities.\n",
+    "\n",
+    "\n",
+    "## Install Python packages\n",
+    "\n",
+    "Install the Hugging Face libraries required for running the Gemma model and making requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# Install PyTorch & other libraries\n",
+    "pip install torch accelerate\n",
+    "\n",
+    "# Install the transformers library\n",
+    "pip install \"transformers>=5.10.1\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model\n",
+    "\n",
+    "Use `transformers` library to load the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_ID = \"google/gemma-4-E2B-it\"\n",
+    "\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "vqa_pipe = pipeline(\n",
+    "    task=\"image-text-to-text\",\n",
+    "    model=MODEL_ID,\n",
+    "    device_map=\"auto\",\n",
+    "    dtype=\"auto\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use a prompt template\n",
+    "\n",
+    "The following example shows how to provide an image and ask question about it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "from IPython.display import display\n",
+    "import requests\n",
+    "\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "img_url = \"https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png\"\n",
+    "input_image = Image.open(requests.get(img_url, stream=True).raw)\n",
+    "display(input_image)\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": [\n",
+    "            {\"type\": \"image\", \"url\": img_url},\n",
+    "            {\"type\": \"text\", \"text\": \"What is shown in this image?\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(output[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![png](../../assets/capabilities/output_a6927bd0_0.png)\n",
+    "\n",
+    "\n",
+    "## Prompting with multiple images\n",
+    "\n",
+    "You can provide multiple images in a single prompt by including multiple image content in prompt template."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "from IPython.display import display\n",
+    "import requests\n",
+    "\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "img_urls = [\n",
+    "    \"https://ai.google.dev/gemma/docs/capabilities/vision/images/surprise.png\",\n",
+    "    \"https://ai.google.dev/gemma/docs/capabilities/vision/images/kitchen.jpg\",\n",
+    "]\n",
+    "for img in img_urls:\n",
+    "    display(Image.open(requests.get(img, stream=True).raw))\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": [\n",
+    "            {\"type\": \"image\", \"url\": img_urls[0]},\n",
+    "            {\"type\": \"image\", \"url\": img_urls[1]},\n",
+    "            {\"type\": \"text\", \"text\": \"Caption these images.\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(output[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![png](../../assets/capabilities/output_6b3b62e7_0.png)\n",
+    "\n",
+    "![png](../../assets/capabilities/output_6b3b62e7_1.png)\n",
+    "\n",
+    "\n",
+    "## OCR (Optical Character Recognition)\n",
+    "\n",
+    "Models can recognize multilingual texts in the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "from IPython.display import display\n",
+    "import requests\n",
+    "\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "img_url = \"https://ai.google.dev/gemma/docs/capabilities/vision/images/cat.png\"\n",
+    "input_image = Image.open(requests.get(img_url, stream=True).raw)\n",
+    "display(input_image)\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": [\n",
+    "            {\"type\": \"image\", \"url\": img_url},\n",
+    "            {\"type\": \"text\", \"text\": \"What does the sign say?\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(output[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![png](../../assets/capabilities/output_ebb5fd08_0.png)\n",
+    "\n",
+    "\n",
+    "## Object Detection\n",
+    "\n",
+    "Models are trained to detect objects in an image and get their bounding box coordinates. Bounding box coordinates are expressed as normalized values relative to a 1000x1000 grid. You need to descale these coordinates based on your original image size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "from IPython.display import display\n",
+    "import requests\n",
+    "\n",
+    "import cv2\n",
+    "import re, json\n",
+    "\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "# Load Image\n",
+    "img_url = \"https://raw.githubusercontent.com/bebechien/gemma/refs/heads/main/PaliGemma_Demo.JPG\"\n",
+    "input_image = Image.open(requests.get(img_url, stream=True).raw)\n",
+    "\n",
+    "###############################\n",
+    "# some helper functions below #\n",
+    "###############################\n",
+    "\n",
+    "def draw_bounding_box(image, coordinates, label, label_colors, width, height):\n",
+    "    y1, x1, y2, x2 = [int(coord)/1000 for coord in coordinates]\n",
+    "    y1, x1, y2, x2 = map(round, (y1*height, x1*width, y2*height, x2*width))\n",
+    "\n",
+    "    text_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 3)\n",
+    "    text_width, text_height = text_size\n",
+    "\n",
+    "    text_x = x1 + 2\n",
+    "    text_y = y1 - 5\n",
+    "\n",
+    "    font_scale = 1\n",
+    "    label_rect_width = text_width + 8\n",
+    "    label_rect_height = int(text_height * font_scale)\n",
+    "\n",
+    "    color = label_colors.get(label, None)\n",
+    "    if color is None:\n",
+    "        color = np.random.randint(0, 256, (3,)).tolist()\n",
+    "        label_colors[label] = color\n",
+    "\n",
+    "    cv2.rectangle(image, (x1, y1 - label_rect_height), (x1 + label_rect_width, y1), color, -1)\n",
+    "\n",
+    "    thickness = 2\n",
+    "    cv2.putText(image, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thickness, cv2.LINE_AA)\n",
+    "\n",
+    "    cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)\n",
+    "    return image\n",
+    "\n",
+    "def draw_results(text_content):\n",
+    "  match = re.search(r'```json\\s+(.*?)\\s+```', text_content, re.DOTALL)\n",
+    "  if match:\n",
+    "    json_string = match.group(1)\n",
+    "    # Parse the string into a Python list/object\n",
+    "    data_list = json.loads(json_string)\n",
+    "\n",
+    "    labels = []\n",
+    "    label_colors = {}\n",
+    "    output_image = input_image\n",
+    "    output_img = np.array(input_image)\n",
+    "\n",
+    "    for item in data_list:\n",
+    "      width = input_image.size[0]\n",
+    "      height = input_image.size[1]\n",
+    "\n",
+    "      # Draw bounding boxes on the frame.\n",
+    "      image = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)\n",
+    "      output_img = draw_bounding_box(output_img, item[\"box_2d\"], item[\"label\"], label_colors, width, height)\n",
+    "\n",
+    "      output_image = Image.fromarray(output_img)\n",
+    "\n",
+    "    return output_image\n",
+    "  else:\n",
+    "    print(\"No JSON code block found.\")\n",
+    "\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": [\n",
+    "            {\"type\": \"image\", \"url\": img_url},\n",
+    "            {\"type\": \"text\", \"text\": \"detect person and cat\"}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(output[0]['generated_text'])\n",
+    "\n",
+    "draw_results(output[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````\n",
+    "```json\n",
+    "[\n",
+    "  {\"box_2d\": [243, 252, 956, 415], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [356, 606, 654, 802], \"label\": \"cat\"}\n",
+    "]\n",
+    "```<turn|>\n",
+    "````\n",
+    "\n",
+    "![png](../../assets/capabilities/output_b041486b_1.png)\n",
+    "\n",
+    "## Variable Resolution (Token Budget)\n",
+    "\n",
+    "All Gemma 4 models support variable resolution which means that images of different resolutions can be processed. Moreover, you can decide if you want to process a given image in a higher or lower resolution. If you are performing object detection, for instance, you might want to process the image in a higher resolution. Video understanding, for instance, can be done with a lower resolution for each frame to speed up inference. Essentially, it is a tradeoff between inference speed and accuracy of the image representations.\n",
+    "\n",
+    "This choice is controlled by the token budget, which represents the maximum number of visual tokens (also called **visual token embeddings**) that are generated for a given image.\n",
+    "\n",
+    "The user can decide between budget sizes of 70, 140, 280, 560, or 1120 tokens. Depending on the budget, the input is resized. If you have a higher budget (like 1120 tokens), then your image can maintain a higher resolution and as a result will have many more patches to process. If you have a lower budget (like 70 tokens), then your image needs to be downscaled and you will have fewer patches that need to be processed. With a higher budget (and therefore more tokens), you can capture much more information than with a lower budget.\n",
+    "\n",
+    "This budget determines how much the image is resized. Imagine you have a budget of 280 tokens, then the maximum number of patches will be 9 x 280 = 2,520. Why times 9? That's because in the next step, every 3x3 block of neighboring patches are merged into a single embedding by averaging them. The resulting embeddings are the visual token embeddings. The more **visual token embeddings** we have, the more fine-grained information can be extracted from an image.\n",
+    "\n",
+    "Let's explore what happens if we perform object detection on an image and setting the budget size very low (70):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import requests, cv2, re, json\n",
+    "\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "gen_kwargs = dict(generation_config=config)\n",
+    "\n",
+    "img_url = \"https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg\"\n",
+    "input_image = Image.open(requests.get(img_url, stream=True).raw)\n",
+    "\n",
+    "def draw_bounding_box(image, coordinates, label, label_colors, width, height):\n",
+    "    \"\"\"Draw a bounding box based on input image and coordinates\"\"\"\n",
+    "    y1, x1, y2, x2 = [int(c) / 1000 for c in coordinates]\n",
+    "    y1, x1, y2, x2 = round(y1 * height), round(x1 * width), round(y2 * height), round(x2 * width)\n",
+    "\n",
+    "    color = label_colors.setdefault(label, np.random.randint(0, 256, (3,)).tolist())\n",
+    "    text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 3)[0]\n",
+    "\n",
+    "    cv2.rectangle(image, (x1, y1 - text_size[1]), (x1 + text_size[0] + 8, y1), color, -1)\n",
+    "    cv2.putText(image, label, (x1 + 2, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
+    "    cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)\n",
+    "    return image\n",
+    "\n",
+    "def draw_results(text_content):\n",
+    "    \"\"\"Based on an input image, draw bounding boxes and labels\"\"\"\n",
+    "    # Extract JSON\n",
+    "    match = re.search(r'```json\\s+(.*?)\\s+```', text_content, re.DOTALL)\n",
+    "    if not match:\n",
+    "        print(\"No JSON code block found.\")\n",
+    "        return None\n",
+    "\n",
+    "    # Extract data\n",
+    "    data_list = json.loads(match.group(1))\n",
+    "    output_img = np.array(input_image)\n",
+    "    label_colors = {}\n",
+    "    w, h = input_image.size\n",
+    "\n",
+    "    # Draw bounding boxes\n",
+    "    for item in data_list:\n",
+    "        output_img = draw_bounding_box(output_img, item[\"box_2d\"], item[\"label\"], label_colors, w, h)\n",
+    "\n",
+    "    return Image.fromarray(output_img)\n",
+    "\n",
+    "\n",
+    "# Detect person, card, and traffic light\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": [\n",
+    "            {\"type\": \"image\", \"url\": img_url},\n",
+    "             {\"type\": \"text\", \"text\": \"detect person and car, output only ```json\"}\n",
+    "        ]\n",
+    "     }\n",
+    "]\n",
+    "\n",
+    "# Run pipeline and set token budget to 70\n",
+    "vqa_pipe.image_processor.max_soft_tokens = 70\n",
+    "output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "print(output[0]['generated_text'])\n",
+    "draw_results(output[0]['generated_text'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````\n",
+    "```json\n",
+    "[\n",
+    "  {\"box_2d\": [454, 871, 533, 923], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [551, 316, 665, 628], \"label\": \"car\"}\n",
+    "]\n",
+    "```<turn|>\n",
+    "````\n",
+    "\n",
+    "![png](../../assets/capabilities/output_bc2920e6_1.png)\n",
+    "\n",
+    "It does alright but it is clear that the image is being compressed quite a bit as it does not detect all cars and persons. A higher token budget should resolve this!\n",
+    "\n",
+    "### Compare Token Budgets\n",
+    "\n",
+    "Let's explore what happens when we increase the budget sizes! A larger budget size results in more soft tokens being generated and processed. This should improve the object detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def count_tokens(processor, tokens):\n",
+    "    input_ids = tokens['input_ids'][0]  # Get input IDs from the tokenizer output\n",
+    "\n",
+    "    img_counting = []\n",
+    "    img_count = 0\n",
+    "\n",
+    "    aud_counting = []\n",
+    "    aud_count = 0\n",
+    "\n",
+    "    for x in input_ids:  # Iterate over the token list\n",
+    "        # Use tokenizer.decode() to convert tokens back to words\n",
+    "        word = processor.decode([x])  # No need to convert to JAX array for decoding\n",
+    "\n",
+    "        if x == processor.tokenizer.image_token_id:\n",
+    "            img_count = img_count + 1\n",
+    "        elif x == processor.tokenizer.audio_token_id:\n",
+    "            aud_count = aud_count + 1\n",
+    "        elif x == processor.tokenizer.eoi_token_id:\n",
+    "            img_counting.append(img_count)\n",
+    "            img_count = 0\n",
+    "        elif x == processor.tokenizer.eoa_token_id:\n",
+    "            aud_counting.append(aud_count)\n",
+    "            aud_count = 0\n",
+    "\n",
+    "    for item in img_counting:\n",
+    "        print(f\"# of Image Tokens: {item}\")\n",
+    "\n",
+    "    for item in aud_counting:\n",
+    "        print(f\"# of Audio Tokens: {item}\")\n",
+    "\n",
+    "input_image.resize((2000, 2000))\n",
+    "\n",
+    "# Detect person and car\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": [\n",
+    "            {\"type\": \"image\", \"url\": img_url},\n",
+    "             {\"type\": \"text\", \"text\": \"detect person and car, output only ```json\"}\n",
+    "        ]\n",
+    "     }\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Run for different budget sizes\n",
+    "budget_sizes = [70, 140, 280, 560]\n",
+    "# 1120 won't fit on T4, but works on L4 or highger\n",
+    "#budget_sizes = [70, 140, 280, 560, 1120]\n",
+    "results = {}\n",
+    "\n",
+    "for budget in budget_sizes:\n",
+    "    print(f\"Budget Size: {budget}\")\n",
+    "    vqa_pipe.image_processor.max_soft_tokens = budget\n",
+    "\n",
+    "    inputs = vqa_pipe.processor.apply_chat_template(messages, tokenize=True, return_dict=True, return_tensors=\"pt\")\n",
+    "    count_tokens(vqa_pipe.processor, inputs)\n",
+    "\n",
+    "    output = vqa_pipe(messages, return_full_text=False, generate_kwargs=gen_kwargs)\n",
+    "    result_text = output[0]['generated_text']\n",
+    "    print(output[0]['generated_text'])\n",
+    "    result_image = draw_results(result_text)\n",
+    "    if result_image:\n",
+    "        results[budget] = result_image\n",
+    "\n",
+    "# Display side-by-side\n",
+    "fig, axes = plt.subplots(1, len(results), figsize=(5 * len(results), 6))\n",
+    "if len(results) == 1:\n",
+    "    axes = [axes]\n",
+    "\n",
+    "for ax, (budget, img) in zip(axes, results.items()):\n",
+    "    ax.imshow(img)\n",
+    "    ax.set_title(f\"max_soft_tokens = {budget}\", fontsize=14, fontweight='bold')\n",
+    "    ax.axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "````\n",
+    "Budget Size: 70\n",
+    "# of Image Tokens: 64\n",
+    "[transformers] You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
+    "```json\n",
+    "[\n",
+    "  {\"box_2d\": [423, 864, 534, 935], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [554, 323, 667, 625], \"label\": \"car\"}\n",
+    "]\n",
+    "```<turn|>\n",
+    "Budget Size: 140\n",
+    "# of Image Tokens: 121\n",
+    "```json\n",
+    "[\n",
+    "  {\"box_2d\": [498, 314, 645, 628], \"label\": \"car\"},\n",
+    "  {\"box_2d\": [753, 554, 917, 716], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [741, 0, 914, 165], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [602, 700, 745, 1000], \"label\": \"car\"},\n",
+    "  {\"box_2d\": [413, 877, 515, 922], \"label\": \"person\"}\n",
+    "]\n",
+    "```<turn|>\n",
+    "Budget Size: 280\n",
+    "# of Image Tokens: 256\n",
+    "```json\n",
+    "[\n",
+    "  {\"box_2d\": [526, 311, 655, 625], \"label\": \"car\"},\n",
+    "  {\"box_2d\": [601, 702, 752, 1000], \"label\": \"car\"},\n",
+    "  {\"box_2d\": [742, 725, 853, 833], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [747, 556, 914, 653], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [743, 5, 906, 155], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [406, 864, 515, 916], \"label\": \"person\"}\n",
+    "]\n",
+    "```<turn|>\n",
+    "Budget Size: 560\n",
+    "# of Image Tokens: 529\n",
+    "```json\n",
+    "[\n",
+    "  {\"box_2d\": [533, 263, 656, 634], \"label\": \"car\"},\n",
+    "  {\"box_2d\": [742, 0, 908, 153], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [816, 512, 917, 676], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [600, 703, 741, 1000], \"label\": \"car\"},\n",
+    "  {\"box_2d\": [750, 703, 840, 867], \"label\": \"person\"},\n",
+    "  {\"box_2d\": [404, 867, 520, 925], \"label\": \"person\"}\n",
+    "]\n",
+    "```<turn|>\n",
+    "````\n",
+    "\n",
+    "![png](../../assets/capabilities/output_76f17518_3.png)\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "In this guide, you learned how to use Gemma 4 models for image understanding tasks. The examples covered generating text from images, using prompt templates for visual QA, processing multiple images simultaneously, optical character recognition (OCR), object detection with bounding boxes, and managing variable resolutions using token budgets.\n",
+    "\n",
+    "<!-- TODO: Port over \n",
+    "### Next Steps\n",
+    "\n",
+    "- [Prompt and system instructions](../prompt-formatting.md)\n",
+    "- [Function calling](function-calling.md)\n",
+    "- [Run Gemma overview](../../deployment/index.md) -->"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "image.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/get-started/capabilities/image.md",
+   "title": "Image Understanding"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/qlora.ipynb b/docs/notebooks/qlora.ipynb
new file mode 100644
index 0000000..898f27c
--- /dev/null
+++ b/docs/notebooks/qlora.ipynb
@@ -0,0 +1,623 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-Tune Gemma using Hugging Face Transformers and QloRA\n",
+    "\n",
+    "\n",
+    "This guide walks you through how to fine-tune Gemma on a custom text-to-sql dataset using Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) and [TRL](https://huggingface.co/docs/trl/index). You will learn:\n",
+    "\n",
+    "- What is Quantized Low-Rank Adaptation (QLoRA)\n",
+    "- Setup development environment\n",
+    "- Create and prepare the fine-tuning dataset\n",
+    "- Fine-tune Gemma using TRL and the SFTTrainer\n",
+    "- Test Model Inference and generate SQL queries\n",
+    "\n",
+    "> **Note:** This guide was created to run on a Google colaboratory account using a NVIDIA T4 GPU with 16GB and Gemma 1B, but can be adapted to run on bigger GPUs and bigger models.\n",
+    "\n",
+    "\n",
+    "## What is Quantized Low-Rank Adaptation (QLoRA)\n",
+    "\n",
+    "This guide demonstrates the use of [Quantized Low-Rank Adaptation (QLoRA)](https://arxiv.org/abs/2305.14314), which emerged as a popular method to efficiently fine-tune LLMs as it reduces computational resource requirements while maintaining high performance. In QloRA, the pretrained model is quantized to 4-bit and the weights are frozen. Then trainable adapter layers (LoRA) are attached and only the adapter layers are trained. Afterwards, the adapter weights can be merged with the base model or kept as a separate adapter.\n",
+    "\n",
+    "## Setup development environment\n",
+    "\n",
+    "The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model, including different RLHF and alignment techniques."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Install Pytorch & other libraries\n",
+    "%pip install torch tensorboard\n",
+    "%pip install -U torchao\n",
+    "\n",
+    "# Install Transformers\n",
+    "%pip install \"transformers>=5.10.1\"\n",
+    "\n",
+    "# Install Hugging Face libraries\n",
+    "%pip install datasets accelerate evaluate bitsandbytes trl \"peft>=0.19.0\" protobuf sentencepiece\n",
+    "\n",
+    "# COMMENT IN: if you are running on a GPU that supports BF16 data type and flash attn, such as NVIDIA L4 or NVIDIA A100\n",
+    "#%pip install flash-attn\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to acelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*\n",
+    "\n",
+    "You need a valid Hugging Face Token to publish your model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Login into Hugging Face Hub\n",
+    "from huggingface_hub import login\n",
+    "login()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and prepare the fine-tuning dataset\n",
+    "\n",
+    "When fine-tuning LLMs, it is important to know your use case and the task you want to solve. This helps you create a dataset to fine-tune your model. If you haven't defined your use case yet, you might want to go back to the drawing board.\n",
+    "\n",
+    "As an example, this guide focuses on the following use case:\n",
+    "\n",
+    "- Fine-tune a natural language to SQL model for seamless integration into a data analysis tool. The objective is to significantly reduce the time and expertise required for SQL query generation, enabling even non-technical users to extract meaningful insights from data.\n",
+    "\n",
+    "Text-to-SQL can be a good use case for fine-tuning LLMs, as it is a complex task that requires a lot of (internal) knowledge about the data and the SQL language.\n",
+    "\n",
+    "Once you have determined that fine-tuning is the right solution, you need a dataset to fine-tune. The dataset should be a diverse set of demonstrations of the task(s) you want to solve. There are several ways to create such a dataset, including:\n",
+    "\n",
+    "- Using existing open-source datasets, such as [Spider](https://huggingface.co/datasets/spider)\n",
+    "- Using synthetic datasets created by LLMs, such as [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca)\n",
+    "- Using datasets created by humans, such as [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k).\n",
+    "- Using a combination of the methods, such as [Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca)\n",
+    "\n",
+    "Each of the methods has its own advantages and disadvantages and depends on the budget, time, and quality requirements. For example, using an existing dataset is the easiest but might not be tailored to your specific use case, while using domain experts might be the most accurate but can be time-consuming and expensive. It is also possible to combine several methods to create an instruction dataset, as shown in [Orca: Progressive Learning from Complex Explanation Traces of GPT-4.](https://arxiv.org/abs/2306.02707)\n",
+    "\n",
+    "This guide uses an already existing dataset ([philschmid/gretel-synthetic-text-to-sql](https://huggingface.co/datasets/philschmid/gretel-synthetic-text-to-sql)), a high quality synthetic Text-to-SQL dataset including natural language instructions, schema definitions, reasoning and the corresponding SQL query.\n",
+    "\n",
+    "[Hugging Face TRL](https://huggingface.co/docs/trl/en/index) supports automatic templating of conversation dataset formats. This means you only need to convert your dataset into the right json objects, and `trl` takes care of templating and putting it into the right format.\n",
+    "\n",
+    "```text\n",
+    "{\"messages\": [{\"role\": \"system\", \"content\": \"You are...\"}, {\"role\": \"user\", \"content\": \"...\"}, {\"role\": \"assistant\", \"content\": \"...\"}]}\n",
+    "{\"messages\": [{\"role\": \"system\", \"content\": \"You are...\"}, {\"role\": \"user\", \"content\": \"...\"}, {\"role\": \"assistant\", \"content\": \"...\"}]}\n",
+    "{\"messages\": [{\"role\": \"system\", \"content\": \"You are...\"}, {\"role\": \"user\", \"content\": \"...\"}, {\"role\": \"assistant\", \"content\": \"...\"}]}\n",
+    "```\n",
+    "\n",
+    "The [philschmid/gretel-synthetic-text-to-sql](https://huggingface.co/datasets/philschmid/gretel-synthetic-text-to-sql) contains over 100k samples. To keep the guide small, it is downsampled to only use 10,000 samples.\n",
+    "\n",
+    "You can now use the Hugging Face Datasets library to load the dataset and create a prompt template to combine the natural language instruction, schema definition and add a system message for your assistant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "# System message for the assistant\n",
+    "system_message = \"\"\"You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.\"\"\"\n",
+    "\n",
+    "# User prompt that combines the user query and the schema\n",
+    "user_prompt = \"\"\"Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.\n",
+    "\n",
+    "<SCHEMA>\n",
+    "{context}\n",
+    "</SCHEMA>\n",
+    "\n",
+    "<USER_QUERY>\n",
+    "{question}\n",
+    "</USER_QUERY>\n",
+    "\"\"\"\n",
+    "def create_conversation(sample, idx):\n",
+    "  return {\n",
+    "    \"messages\": [\n",
+    "      {\"role\": \"system\", \"content\": system_message},\n",
+    "      {\"role\": \"user\", \"content\": user_prompt.format(question=sample[\"sql_prompt\"], context=sample[\"sql_context\"])},\n",
+    "      {\"role\": \"assistant\", \"content\": sample[\"sql\"]}\n",
+    "    ]\n",
+    "  }\n",
+    "\n",
+    "# Load dataset from the hub\n",
+    "dataset = load_dataset(\"philschmid/gretel-synthetic-text-to-sql\", split=\"train\")\n",
+    "dataset = dataset.select(range(1250))\n",
+    "\n",
+    "# Convert dataset to OAI messages\n",
+    "dataset = dataset.map(create_conversation, with_indices=True, remove_columns=dataset.features)\n",
+    "# split dataset into 80% training samples and 20% test samples\n",
+    "dataset = dataset.train_test_split(test_size=0.2, shuffle=False)\n",
+    "\n",
+    "# Print formatted user prompt\n",
+    "for item in dataset[\"train\"][0][\"messages\"]:\n",
+    "  print(item)\n",
+    "{'role': 'system', 'content': 'You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.'}\n",
+    "{'role': 'user', 'content': \"Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.\\n\\n<SCHEMA>\\nCREATE TABLE salesperson (salesperson_id INT, name TEXT, region TEXT); INSERT INTO salesperson (salesperson_id, name, region) VALUES (1, 'John Doe', 'North'), (2, 'Jane Smith', 'South'); CREATE TABLE timber_sales (sales_id INT, salesperson_id INT, volume REAL, sale_date DATE); INSERT INTO timber_sales (sales_id, salesperson_id, volume, sale_date) VALUES (1, 1, 120, '2021-01-01'), (2, 1, 150, '2021-02-01'), (3, 2, 180, '2021-01-01');\\n</SCHEMA>\\n\\n<USER_QUERY>\\nWhat is the total volume of timber sold by each salesperson, sorted by salesperson?\\n</USER_QUERY>\\n\"}\n",
+    "{'role': 'assistant', 'content': 'SELECT salesperson_id, name, SUM(volume) as total_volume FROM timber_sales JOIN salesperson ON timber_sales.salesperson_id = salesperson.salesperson_id GROUP BY salesperson_id, name ORDER BY total_volume DESC;'}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fine-tune Gemma using TRL and the SFTTrainer\n",
+    "\n",
+    "You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) makes it straightforward to supervise fine-tune open LLMs. The `SFTTrainer` is a subclass of the `Trainer` from the `transformers` library and supports all the same features, including logging, evaluation, and checkpointing, but adds additional quality of life features, including:\n",
+    "\n",
+    "- Dataset formatting, including conversational and instruction formats\n",
+    "- Training on completions only, ignoring prompts\n",
+    "- Packing datasets for more efficient training\n",
+    "- Parameter-efficient fine-tuning (PEFT) support including QloRA\n",
+    "- Preparing the model and tokenizer for conversational fine-tuning (such as adding special tokens)\n",
+    "\n",
+    "The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig\n",
+    "from peft import prepare_model_for_kbit_training\n",
+    "\n",
+    "# Hugging Face model id\n",
+    "model_id = \"google/gemma-4-E2B\"\n",
+    "\n",
+    "# Check if GPU supports bfloat16\n",
+    "if torch.cuda.is_bf16_supported():\n",
+    "    torch_dtype = torch.bfloat16\n",
+    "else:\n",
+    "    torch_dtype = torch.float16\n",
+    "\n",
+    "# Define model init arguments\n",
+    "model_kwargs = dict(\n",
+    "    dtype=torch_dtype, # What torch dtype to use\n",
+    "    device_map=\"auto\", # Let torch decide how to load the model\n",
+    ")\n",
+    "\n",
+    "# BitsAndBytesConfig: Enables 4-bit quantization to reduce model size/memory usage\n",
+    "model_kwargs[\"quantization_config\"] = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_quant_type='nf4',\n",
+    "    bnb_4bit_compute_dtype=torch_dtype,\n",
+    "    bnb_4bit_quant_storage=torch_dtype,\n",
+    ")\n",
+    "\n",
+    "# Load model and processor\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(model_id, **model_kwargs)\n",
+    "processor = AutoProcessor.from_pretrained(\"google/gemma-4-E2B-it\") # Load the Instruction Processor to use the official Gemma template\n",
+    "\n",
+    "# NOTE: You should call the prepare_model_for_kbit_training() function to preprocess the quantized model for training.\n",
+    "# On T4, we are skipping this step purely due to VRAM limitation and for a quick demonstration.\n",
+    "if (torch.cuda.get_device_properties(0).total_memory/1024**3) > 16:\n",
+    "    model = prepare_model_for_kbit_training(model)\n",
+    "WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_mxfp8.cpython-310-x86_64-linux-gnu.so\n",
+    "WARNING:torchao:Failed to load /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so: Could not load this library: /usr/local/lib/python3.12/dist-packages/torchao/_C_cutlass_90a.abi3.so\n",
+    "Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]\n",
+    "/usr/local/lib/python3.12/dist-packages/bitsandbytes/backends/cuda/ops.py:213: FutureWarning: _check_is_size will be removed in a future PyTorch release along with guard_size_oblivious.     Use _check(i >= 0) instead.\n",
+    "  torch._check_is_size(blocksize)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `SFTTrainer` supports a built-in integration with `peft`, which makes it straightforward to efficiently tune LLMs using QLoRA. You only need to create a `LoraConfig` and provide it to the trainer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    lora_alpha=16,\n",
+    "    lora_dropout=0.05,\n",
+    "    r=16,\n",
+    "    bias=\"none\",\n",
+    "    # no target_modules --- PEFT's Gemma 4 defaults scope to the LM layers\n",
+    "    task_type=\"CAUSAL_LM\",\n",
+    "    modules_to_save=[\"lm_head\", \"embed_tokens\"], # make sure to save the lm_head and embed_tokens as you train the special tokens\n",
+    "    ensure_weight_tying=True,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before you can start your training, you need to define the hyperparameter you want to use in a `SFTConfig` instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from trl import SFTConfig\n",
+    "\n",
+    "args = SFTConfig(\n",
+    "    output_dir=\"gemma-text-to-sql\",         # directory to save and repository id\n",
+    "    max_length=512,                         # max length for model and packing of the dataset\n",
+    "    num_train_epochs=3,                     # number of training epochs\n",
+    "    per_device_train_batch_size=1,          # batch size per device during training\n",
+    "    per_device_eval_batch_size=1,           # batch size per device during evaluation\n",
+    "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
+    "    logging_steps=10,                       # log every 10 steps\n",
+    "    save_strategy=\"epoch\",                  # save checkpoint every epoch\n",
+    "    eval_strategy=\"epoch\",                  # evaluate checkpoint every epoch\n",
+    "    learning_rate=2e-4,                     # learning rate\n",
+    "    fp16=True if torch_dtype == torch.float16 else False,  # use float16 precision\n",
+    "    bf16=True if torch_dtype == torch.bfloat16 else False, # use bfloat16 precision\n",
+    "    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n",
+    "    push_to_hub=True,                       # push model to hub\n",
+    "    report_to=\"tensorboard\",                # report metrics to tensorboard\n",
+    "    dataset_kwargs={\"skip_prepare_dataset\": True}, # important for collator\n",
+    "    remove_unused_columns = False,                 # important for collator\n",
+    ")\n",
+    "\n",
+    "# Data collator\n",
+    "def collate_fn(examples):\n",
+    "    texts = []\n",
+    "\n",
+    "    for example in examples:\n",
+    "        full_text = processor.apply_chat_template(\n",
+    "            example[\"messages\"], add_generation_prompt=False, tokenize=False\n",
+    "        )\n",
+    "        texts.append(full_text.strip())\n",
+    "\n",
+    "    # Tokenize the texts and process the audios\n",
+    "    batch = processor(text=texts, return_tensors=\"pt\", padding=True)\n",
+    "\n",
+    "    # The labels are the input_ids, and we mask the padding tokens and audio tokens in the loss computation\n",
+    "    labels = batch[\"input_ids\"].clone()\n",
+    "\n",
+    "    target_tokens = [\n",
+    "        processor.tokenizer.convert_tokens_to_ids(\"<|turn>\"),\n",
+    "        processor.tokenizer.convert_tokens_to_ids(\"model\"),\n",
+    "        processor.tokenizer.convert_tokens_to_ids(\"\\n\")\n",
+    "    ]\n",
+    "    target_len = len(target_tokens)\n",
+    "\n",
+    "    for i in range(labels.size(0)):\n",
+    "        row_tokens = batch[\"input_ids\"][i].tolist()\n",
+    "\n",
+    "        # Find where the assistant block begins\n",
+    "        assistant_start_idx = None\n",
+    "        for idx in range(len(row_tokens) - target_len + 1):\n",
+    "            if row_tokens[idx : idx + target_len] == target_tokens:\n",
+    "                # We want to keep loss calculation on the assistant transcription tokens,\n",
+    "                # so we move the index right past the assistant header ('<|turn>\\nmodel\\n')\n",
+    "                assistant_start_idx = idx + target_len\n",
+    "                break\n",
+    "\n",
+    "        if assistant_start_idx is not None:\n",
+    "            # Mask everything from index 0 up to the start of the actual Japanese text response\n",
+    "            labels[i, :assistant_start_idx] = -100\n",
+    "        else:\n",
+    "            # Fallback safety: if template matching fails for an anomalous row, mask padding anyway\n",
+    "            print(\"WARNING: maybe the sample is too long, try to increase `token_limit` value.\")\n",
+    "            labels[i, labels[i] == processor.tokenizer.pad_token_id] = -100\n",
+    "\n",
+    "        \"\"\"\n",
+    "        # --- DEBUG PRINT CODE ---\n",
+    "        print(f\"\\n--- Example {i} (Split index: {assistant_start_idx}) ---\")\n",
+    "        debug_string = []\n",
+    "        for token_id, label_id in zip(row_tokens, labels[i].tolist()):\n",
+    "            # Decode token by token so we can see exactly what is masked\n",
+    "            decoded_token = processor.tokenizer.decode([token_id])\n",
+    "\n",
+    "            if label_id == -100:\n",
+    "                # Red text for masked tokens (ANSI Escape Code)\n",
+    "                debug_string.append(f\"\\033[91m{decoded_token}\\033[0m\")\n",
+    "            else:\n",
+    "                # Green text for active loss tokens\n",
+    "                debug_string.append(f\"\\033[92m{decoded_token}\\033[0m\")\n",
+    "\n",
+    "        print(\"\".join(debug_string))\n",
+    "        # ---\n",
+    "        \"\"\"\n",
+    "\n",
+    "\n",
+    "    # Mask tokens for not being used in the loss computation\n",
+    "    labels[labels == processor.tokenizer.pad_token_id] = -100\n",
+    "\n",
+    "    batch[\"labels\"] = labels\n",
+    "    return batch\n",
+    "/tmp/ipykernel_164873/3952412390.py:4: FutureWarning: The default `loss_type` will change from `'nll'` to `'chunked_nll'` in TRL 1.7. For standard models this is transparent (same math, lower memory) and no action is needed — you'll get the new default automatically on upgrade. If you use a custom model, check ahead of time that `loss_type='chunked_nll'` runs and yields the same loss as `'nll'`; if it doesn't, pin `loss_type='nll'` to keep the current behavior and please open an issue at https://github.com/huggingface/trl/issues so we can address the edge case.\n",
+    "  args = SFTConfig(\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You now have every building block you need to create your `SFTTrainer` to start the training of your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "\n",
+    "# Create Trainer object\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    eval_dataset=dataset[\"test\"],\n",
+    "    peft_config=peft_config,\n",
+    "    processing_class=processor,\n",
+    "    data_collator=collate_fn,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start training by calling the `train()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Start training, the model will be automatically saved to the Hub and the output directory\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save the final model again to the Hugging Face Hub\n",
+    "trainer.save_model()\n",
+    "[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 1, 'bos_token_id': 2, 'pad_token_id': 0}.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\[3000/3000 1:51:30, Epoch 3/3\\]\n",
+    "\n",
+    "| Epoch | Training Loss | Validation Loss | Entropy | Mean Token Accuracy | Num Tokens |\n",
+    "|---|---|---|---|---|---|\n",
+    "| 1 | 0.315478 | 0.267880 | 0.204330 | 0.927141 | 246435.000000 |\n",
+    "| 2 | 0.139383 | 0.297303 | 0.131811 | 0.929568 | 492870.000000 |\n",
+    "| 3 | 0.150697 | 0.315715 | 0.110690 | 0.927032 | 739305.000000 |\n",
+    "\n",
+    "\n",
+    "\n",
+    "Before you can test your model, make sure to free the memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# free the memory again\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using QLoRA, you only train adapters and not the full model. This means when saving the model during training you only save the adapter weights and not the full model. If you want to save the full model, which makes it easier to use with serving stacks like vLLM or TGI, you can merge the adapter weights into the model weights using the `merge_and_unload` method and then save the model with the `save_pretrained` method. This saves a default model, which can be used for inference.\n",
+    "\n",
+    "> **Note:** It requires more than 30GB of CPU Memory when you want to merge the adapter into the model. You can skip this and continue with Test Model Inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForMultimodalLM, AutoProcessor\n",
+    "from peft import PeftModel\n",
+    "\n",
+    "# Load Model base model\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(model_id, low_cpu_mem_usage=True)\n",
+    "\n",
+    "# Merge LoRA and base model and save\n",
+    "peft_model = PeftModel.from_pretrained(model, args.output_dir)\n",
+    "merged_model = peft_model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\"merged_model\", safe_serialization=True, max_shard_size=\"2GB\")\n",
+    "\n",
+    "processor = AutoProcessor.from_pretrained(\"google/gemma-4-E2B-it\")\n",
+    "processor.save_pretrained(\"merged_model\")\n",
+    "Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]\n",
+    "Writing model shards:   0%|          | 0/4 [00:00<?, ?it/s]\n",
+    "['merged_model/processor_config.json']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test Model Inference and generate SQL queries\n",
+    "\n",
+    "After the training is done, you'll want to evaluate and test your model. You can load different samples from the test dataset and evaluate the model on those samples.\n",
+    "\n",
+    "> **Note:** Evaluating generative AI models is not a trivial task since one input can have multiple correct outputs. This guide only focuses on manual evaluation and vibe checks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForMultimodalLM, AutoProcessor\n",
+    "\n",
+    "model_id = \"merged_model\"\n",
+    "\n",
+    "# Load Model with PEFT adapter\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(\n",
+    "  model_id,\n",
+    "  device_map=\"auto\",\n",
+    "  dtype=\"auto\",\n",
+    ")\n",
+    "processor = AutoProcessor.from_pretrained(model_id)\n",
+    "Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's load a random sample from the test dataset and generate a SQL command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from random import randint\n",
+    "import re\n",
+    "from transformers import pipeline, GenerationConfig, pipeline\n",
+    "\n",
+    "config = GenerationConfig.from_pretrained(model_id)\n",
+    "config.max_new_tokens = 256\n",
+    "config.eos_token_id = [processor.tokenizer.convert_tokens_to_ids(\"<turn|>\")]\n",
+    "\n",
+    "# Load the model and tokenizer into the pipeline\n",
+    "pipe = pipeline(\"text-generation\", model=model, tokenizer=processor.tokenizer)\n",
+    "\n",
+    "# Load a random sample from the test dataset\n",
+    "rand_idx = randint(0, len(dataset[\"test\"]))\n",
+    "test_sample = dataset[\"test\"][rand_idx]\n",
+    "\n",
+    "# Convert as test example into a prompt with the Gemma template\n",
+    "prompt = processor.tokenizer.apply_chat_template(test_sample[\"messages\"][:2], tokenize=False, add_generation_prompt=True)\n",
+    "print(prompt)\n",
+    "\n",
+    "# Generate our SQL query.\n",
+    "outputs = pipe(text_inputs=prompt, generation_config=config)\n",
+    "\n",
+    "# Extract the user query and original answer\n",
+    "print(f\"Context:\\n\", re.search(r'<SCHEMA>\\n(.*?)\\n</SCHEMA>', test_sample['messages'][1]['content'], re.DOTALL).group(1).strip())\n",
+    "print(f\"Query:\\n\", re.search(r'<USER_QUERY>\\n(.*?)\\n</USER_QUERY>', test_sample['messages'][1]['content'], re.DOTALL).group(1).strip())\n",
+    "print(f\"Original Answer:\\n{test_sample['messages'][2]['content']}\")\n",
+    "print(f\"Generated Answer:\\n{outputs[0]['generated_text'][len(prompt):].strip().removesuffix(\"<turn|>\")}\")\n",
+    "<bos><|turn>system\n",
+    "You are a text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.<turn|>\n",
+    "<|turn>user\n",
+    "Given the <USER_QUERY> and the <SCHEMA>, generate the corresponding SQL command to retrieve the desired data, considering the query's syntax, semantics, and schema constraints.\n",
+    "\n",
+    "<SCHEMA>\n",
+    "CREATE TABLE Auto_Shows (id INT, show_name VARCHAR(255), show_year INT, location VARCHAR(255)); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (1, 'New York International Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (2, 'Chicago Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (3, 'North American International Auto Show', 2018, 'United States');\n",
+    "</SCHEMA>\n",
+    "\n",
+    "<USER_QUERY>\n",
+    "How many auto shows were held in the United States in the year 2019?\n",
+    "</USER_QUERY><turn|>\n",
+    "<|turn>model\n",
+    "\n",
+    "Context:\n",
+    " CREATE TABLE Auto_Shows (id INT, show_name VARCHAR(255), show_year INT, location VARCHAR(255)); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (1, 'New York International Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (2, 'Chicago Auto Show', 2019, 'United States'); INSERT INTO Auto_Shows (id, show_name, show_year, location) VALUES (3, 'North American International Auto Show', 2018, 'United States');\n",
+    "Query:\n",
+    " How many auto shows were held in the United States in the year 2019?\n",
+    "Original Answer:\n",
+    "SELECT COUNT(*) FROM Auto_Shows WHERE show_year = 2019 AND location = 'United States';\n",
+    "Generated Answer:\n",
+    "SELECT COUNT(*) FROM Auto_Shows WHERE location = 'United States' AND show_year = 2019;\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary and next steps\n",
+    "\n",
+    "This tutorial covered how to fine-tune a Gemma model using TRL and QLoRA. Check out the following docs next:\n",
+    "\n",
+    "- Learn how to [generate text with a Gemma model](../get-started/quickstart.md).\n",
+    "- Learn how to [fine-tune Gemma for vision tasks using Hugging Face Transformers](vision-qlora.md).\n",
+    "- Learn how to perform [distributed fine-tuning and inference on a Gemma model](distributed.md).\n",
+    "- Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+    "- Learn how to [fine-tune Gemma using KerasNLP and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb)."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "qlora.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/tuning/qlora.md",
+   "title": "Fine-Tune Gemma using Hugging Face Transformers and QloRA"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/text.ipynb b/docs/notebooks/text.ipynb
new file mode 100644
index 0000000..e21f79b
--- /dev/null
+++ b/docs/notebooks/text.ipynb
@@ -0,0 +1,313 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inference\n",
+    "\n",
+    "Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the [Gemini](https://deepmind.google/technologies/gemini/#introduction) models. Gemma 4 is designed to be the world's most efficient open-weight model family.\n",
+    "\n",
+    "This document provides a guide to performing basic text inference with Gemma 4 using the Hugging Face `transformers` library. It covers environment setup, model loading, and various text generation scenarios including single-turn prompts, structured multi-turn conversations, and applying system instructions.\n",
+    "\n",
+    "## Install Python packages\n",
+    "\n",
+    "Install the Hugging Face libraries required for running the Gemma model and making requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# Install PyTorch & other libraries\n",
+    "pip install torch accelerate\n",
+    "\n",
+    "# Install the transformers library\n",
+    "pip install \"transformers>=5.10.1\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Dialog](https://github.com/google-deepmind/dialog) is a library to manipulate and display conversations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "pip install dialog\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model\n",
+    "\n",
+    "Use `transformers` library to load the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_ID = \"google/gemma-4-E2B-it\"\n",
+    "\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "txt_pipe = pipeline(\n",
+    "    task=\"text-generation\",\n",
+    "    model=MODEL_ID,\n",
+    "    device_map=\"auto\",\n",
+    "    dtype=\"auto\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run text generation\n",
+    "\n",
+    "Once you have the Gemma model loaded and configured in a `pipeline` object, you can send prompts to the model. The following example code shows a basic request using the `text_inputs` parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "output = txt_pipe(text_inputs=\"<|turn>user\\nRoses are..<turn|>\\n<|turn>model\\n\")\n",
+    "print(output[0]['generated_text'].removesuffix(\"<turn|>\"))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use Dialog library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import dialog\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "\n",
+    "conv = dialog.Conversation(\n",
+    "    dialog.User(\"Roses are...\")\n",
+    ")\n",
+    "output = txt_pipe(text_inputs=conv.as_text(), return_full_text=False, generation_config=config)\n",
+    "conv += dialog.Model(output[0]['generated_text'].removesuffix(\"<turn|>\"))\n",
+    "\n",
+    "print(conv.as_text())\n",
+    "conv.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use a prompt template\n",
+    "\n",
+    "When generating content with more complex prompting, use a prompt template to structure your request. A prompt template allows you to specify input from specific roles, such as `user` or `model`, and is a required format for managing multi-turn chat interactions with Gemma models. The following example code shows how to construct a prompt template for Gemma:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"text\", \"text\": \"Write a short poem about the Kraken.\"},\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "output = txt_pipe(messages, return_full_text=False, generation_config=config)\n",
+    "print(output[0]['generated_text'].removesuffix(\"<turn|>\"))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-turn conversation\n",
+    "\n",
+    "In a multi-turn setup, the conversation history is preserved as a sequence of alternating `user` and `model` roles. This cumulative list serves as the model's memory, ensuring that each new output is informed by the preceding dialogue."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import dialog\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "\n",
+    "# User turn #1\n",
+    "conv = dialog.Conversation(\n",
+    "    dialog.User(\"Write a short poem about the Kraken.\")\n",
+    ")\n",
+    "\n",
+    "# Model response #1\n",
+    "output = txt_pipe(text_inputs=conv.as_text(), return_full_text=False, generation_config=config)\n",
+    "conv += dialog.Model(output[0]['generated_text'].removesuffix(\"<turn|>\"))\n",
+    "\n",
+    "# User turn #2\n",
+    "conv += dialog.User(\"Now with the Siren.\")\n",
+    "\n",
+    "# Model response #2\n",
+    "output = txt_pipe(text_inputs=conv.as_text(), return_full_text=False, generation_config=config)\n",
+    "conv += dialog.Model(output[0]['generated_text'].removesuffix(\"<turn|>\"))\n",
+    "\n",
+    "print(conv.as_text())\n",
+    "conv.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the conversation exported as text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "chat_history = conv.as_text(training=True)\n",
+    "print(chat_history)\n",
+    "print(\"-\"*80)\n",
+    "\n",
+    "# display as Conversation widget\n",
+    "chat_history\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** If you set `training=True`, the conversation is assumed to be the full complete example. Always ends with `<turn|>`\n",
+    "\n",
+    "\n",
+    "## System instructions\n",
+    "\n",
+    "Use the `system` role to provide the system-level instructions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import dialog\n",
+    "from transformers import GenerationConfig\n",
+    "config = GenerationConfig.from_pretrained(MODEL_ID)\n",
+    "config.max_new_tokens = 512\n",
+    "\n",
+    "conv = dialog.Conversation(\n",
+    "    dialog.System(\"Speak like a pirate.\"),\n",
+    "    dialog.User(\"Why is the sky blue?\")\n",
+    ")\n",
+    "\n",
+    "output = txt_pipe(text_inputs=conv.as_text(), return_full_text=False, generation_config=config)\n",
+    "conv += dialog.Model(output[0]['generated_text'].removesuffix(\"<turn|>\"))\n",
+    "\n",
+    "print(conv.as_text())\n",
+    "conv.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "In this guide, you learned how to perform basic text inference with Gemma 4 using the Hugging Face `transformers` library. You covered:\n",
+    "\n",
+    "1. **Environment**: Setting up the environment and installing dependencies.\n",
+    "2. **Loading the model**: Using the `pipeline` abstraction.\n",
+    "3. **Inference**: Running basic text generation.\n",
+    "4. **Conversation**: Using the `dialog` library for conversation tracking.\n",
+    "5. **Multi-turn conversations**: Multi-turn conversations and applying system instructions.\n",
+    "\n",
+    "\n",
+    "<!-- TODO: Port over\n",
+    "### Next Steps\n",
+    "\n",
+    "- [Prompt and system instructions](../prompt-formatting.md)\n",
+    "- [Function calling](function-calling.md)\n",
+    "- [Run Gemma overview](../../deployment/index.md) -->"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "text.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/get-started/capabilities/text.md",
+   "title": "Inference"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/thinking.ipynb b/docs/notebooks/thinking.ipynb
new file mode 100644
index 0000000..6f7fdc3
--- /dev/null
+++ b/docs/notebooks/thinking.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reasoning\n",
+    "\n",
+    "\n",
+    "Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the [Gemini](https://deepmind.google/technologies/gemini/#introduction) models. Gemma 4 is designed to be the world's most efficient open-weight model family.\n",
+    "\n",
+    "This document demonstrates how to use the thinking capabilities of Gemma 4 to generate reasoning processes before providing a final answer. You will learn how to enable thinking mode for both text-only and multimodal (image-text) tasks using the Hugging Face `transformers` library, and how to parse the output to separate thinking from the answer.\n",
+    "\n",
+    "## Install Python packages\n",
+    "\n",
+    "Install the Hugging Face libraries required for running the Gemma model and making requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# Install PyTorch & other libraries\n",
+    "pip install torch accelerate\n",
+    "\n",
+    "# Install the transformers library\n",
+    "pip install \"transformers>=5.10.1\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model\n",
+    "\n",
+    "Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_ID = \"google/gemma-4-E2B-it\"\n",
+    "\n",
+    "from transformers import AutoProcessor, AutoModelForMultimodalLM\n",
+    "\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(MODEL_ID, dtype=\"auto\", device_map=\"auto\")\n",
+    "processor = AutoProcessor.from_pretrained(MODEL_ID)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A single text inference with Thinking\n",
+    "\n",
+    "To generate a response using the model's thinking capabilities, pass `enable_thinking=True`, the processor will insert the correct thinking tokens into the prompt, instructing the model to think before responding.\n",
+    "> **Note:** We've added an empty thinking token to the chat template for `gemma-4-12B-it`, `gemma-4-26B-A4B-it` and `gemma-4-31B-it`. This stabilizes model output by suppressing \"ghost\" thought channels that may appear even when thinking is deactivated.\n",
+    "\n",
+    "| Model Size | Thinking State | Template Structure / Output |\n",
+    "|---|---|---|\n",
+    "| **E2B/E4B** | **OFF** | `<|turn>user\\n[Prompt]<turn|>\\n<|turn>model` |\n",
+    "| **E2B/E4B** | **ON** | `<|turn>system\\n<|think|><turn|>\\n<|turn>user\\n[Prompt]<turn|>\\n<|turn>model` |\n",
+    "| **12B/26B/31B** | **OFF** | ⚠️ `<|turn>user\\n[Prompt]<turn|>\\n<|turn>model\\n<|channel>thought\\n<channel|>` |\n",
+    "| **12B/26B/31B** | **ON** | `<|turn>system\\n<|think|><turn|>\\n<|turn>user\\n[Prompt]<turn|>\\n<|turn>model` |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import TextStreamer\n",
+    "\n",
+    "message = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": \"What is the water formula?\"\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True, enable_thinking=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "input_len = inputs[\"input_ids\"].shape[-1]\n",
+    "\n",
+    "streamer = TextStreamer(processor)\n",
+    "outputs = model.generate(**inputs, streamer=streamer, max_new_tokens=1024)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the text is generated, the response will contain both the reasoning blocks and the final answer bounded by special tokens. You can use the `parse_response` utility to easily extract them into a dictionary containing `thinking` and `answer`.\n",
+    "> **Note:** `skip_special_tokens=False` is mandatory to make the reasoning parser to work correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)\n",
+    "result = processor.parse_response(response)\n",
+    "\n",
+    "for key, value in result.items():\n",
+    "  if key == \"role\":\n",
+    "    print(f\"Role: {value}\")\n",
+    "  elif key == \"thinking\":\n",
+    "    print(f\"\\n=== Thoughts ===\\n{value}\")\n",
+    "  elif key == \"content\":\n",
+    "    print(f\"\\n=== Answer ===\\n{value}\")\n",
+    "  elif key == \"tool_calls\":\n",
+    "    print(f\"\\n=== Tool Calls ===\\n{value}\")\n",
+    "  else:\n",
+    "    print(f\"\\n{key}: {value}...\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-Turn Example with Thought Stripping\n",
+    "\n",
+    "Properly managing the model's generated thoughts is critical for maintaining performance across multi-turn conversations.\n",
+    "\n",
+    "- **Standard Multi-Turn Conversations:** You must remove (strip) the model's generated thoughts from the previous turn before passing the conversation history back to the model for the next turn. If you want to disable thinking mode mid-conversation, you can remove the `<|think|>` token when you strip the previous thoughts.\n",
+    "- **Function Calling (Exception):** If a single model turn involves function or tool calls, thoughts must **NOT** be removed between the function calls.\n",
+    "- **Maintaining Conversation History:** The historical model output must only include the final response. Ensure that no generated thoughts from previous turns remain in the context window before the next user turn begins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import TextStreamer\n",
+    "\n",
+    "# Append the clean response to the message history\n",
+    "message.append({\n",
+    "    \"role\": \"assistant\",\n",
+    "    \"content\": result[\"content\"]\n",
+    "})\n",
+    "\n",
+    "# ==========================================\n",
+    "# TURN 2\n",
+    "# ==========================================\n",
+    "print(\"\\n--- Turn 2 ---\")\n",
+    "# Add the next user query to the history\n",
+    "message.append({\n",
+    "    \"role\": \"user\",\n",
+    "    \"content\": \"What is its boiling point in Celsius?\"\n",
+    "})\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True, enable_thinking=True)\n",
+    "inputs = processor(text=text, return_tensors=\"pt\").to(model.device)\n",
+    "input_len = inputs[\"input_ids\"].shape[-1]\n",
+    "\n",
+    "streamer = TextStreamer(processor)\n",
+    "outputs = model.generate(**inputs, streamer=streamer, max_new_tokens=1024)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)\n",
+    "result = processor.parse_response(response)\n",
+    "\n",
+    "for key, value in result.items():\n",
+    "  if key == \"role\":\n",
+    "    print(f\"Role: {value}\")\n",
+    "  elif key == \"thinking\":\n",
+    "    print(f\"\\n=== Thoughts ===\\n{value}\")\n",
+    "  elif key == \"content\":\n",
+    "    print(f\"\\n=== Answer ===\\n{value}\")\n",
+    "  elif key == \"tool_calls\":\n",
+    "    print(f\"\\n=== Tool Calls ===\\n{value}\")\n",
+    "  else:\n",
+    "    print(f\"\\n{key}: {value}...\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A single image inference\n",
+    "\n",
+    "The procedure for using the thinking model with visual data is very similar. You can provide an image as part of the `messages` array. Just ensure you pass the image to the processor along with the formatted text, and the model will reason about the visual input before responding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "prompt = \"What is shown in this image?\"\n",
+    "image_url = \"https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png\"\n",
+    "\n",
+    "# download image\n",
+    "!wget -q {image_url} -O image.png\n",
+    "image = Image.open(\"image.png\")\n",
+    "\n",
+    "# Display all images\n",
+    "print(\"=== Downloaded image ===\")\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
+    "ax.imshow(image)\n",
+    "ax.set_title(\"Image 1\")\n",
+    "ax.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "message = [\n",
+    "    {\n",
+    "        \"role\": \"user\", \"content\": [\n",
+    "          {\"type\": \"image\"},\n",
+    "          {\"type\": \"text\", \"text\": prompt}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "text = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True, enable_thinking=True)\n",
+    "inputs = processor(text=text, images=image, return_tensors=\"pt\").to(model.device)\n",
+    "input_len = inputs[\"input_ids\"].shape[-1]\n",
+    "\n",
+    "outputs = model.generate(**inputs, max_new_tokens=1024)\n",
+    "response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)\n",
+    "\n",
+    "result = processor.parse_response(response)\n",
+    "\n",
+    "for key, value in result.items():\n",
+    "  if key == \"role\":\n",
+    "    print(f\"Role: {value}\")\n",
+    "  elif key == \"thinking\":\n",
+    "    print(f\"\\n=== Thoughts ===\\n{value}\")\n",
+    "  elif key == \"content\":\n",
+    "    print(f\"\\n=== Answer ===\\n{value}\")\n",
+    "  elif key == \"tool_calls\":\n",
+    "    print(f\"\\n=== Tool Calls ===\\n{value}\")\n",
+    "  else:\n",
+    "    print(f\"\\n{key}: {value}...\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![png](../../assets/capabilities/output_0f334997_1.png)\n",
+    "\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "In this guide, you learned how to use the thinking capabilities of Gemma 4 models to generate reasoning processes before final answers. You covered:\n",
+    "\n",
+    "1. **Enabling thinking mode**: Using `enable_thinking=True` in `apply_chat_template`.\n",
+    "2. **Text streamer**: Using `TextStreamer` to observe the thinking process in real-time.\n",
+    "3. **Parsing the combined output**: Using `parse_response` to separate `thinking` and `answer` blocks.\n",
+    "4. **Multimodal tasks**: Applying thinking capabilities to multimodal tasks (image + text).\n",
+    "\n",
+    "<!-- TODO: Port over\n",
+    "### Next Steps\n",
+    "\n",
+    "Explore more capabilities of Gemma 4:\n",
+    "\n",
+    "- [Prompt and system instructions](../prompt-formatting.md)\n",
+    "- [Function calling](function-calling.md)\n",
+    "- [Run Gemma overview](../../deployment/index.md) -->"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "thinking.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/get-started/capabilities/thinking.md",
+   "title": "Reasoning"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/video.ipynb b/docs/notebooks/video.ipynb
new file mode 100644
index 0000000..60d34a4
--- /dev/null
+++ b/docs/notebooks/video.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Video Understanding\n",
+    "\n",
+    "Video data is a rich source of information that can help you accomplish tasks and understand the world around you. Using Gemma with video data can help you understand spatial relationships, interpret human interactions, and assist with situational awareness. This tutorial shows you how to get started processing video data with Gemma using Hugging Face Transformers.\n",
+    "\n",
+    "\n",
+    "## Install Python packages\n",
+    "\n",
+    "Install the Hugging Face libraries required for running the Gemma model and making requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "bash"
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# Install PyTorch & other libraries\n",
+    "pip install torch accelerate\n",
+    "\n",
+    "# Install the transformers library\n",
+    "pip install \"transformers>=5.10.1\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Model\n",
+    "\n",
+    "Use the `transformers` libraries to create an instance of a `processor` and `model` using the `AutoProcessor` and `AutoModelForImageTextToText` classes as shown in the following code example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_ID = \"google/gemma-4-E2B-it\"\n",
+    "\n",
+    "from transformers import AutoProcessor, AutoModelForMultimodalLM\n",
+    "\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(MODEL_ID, dtype=\"auto\", device_map=\"auto\")\n",
+    "processor = AutoProcessor.from_pretrained(MODEL_ID)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run video data request\n",
+    "\n",
+    "You can reference the location of the file in the chat template. The `processor` will then take care of loading in the video and processing it. The following example code shows a request using a chat template, output generation, and decoding the response:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoProcessor\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        'role': 'user',\n",
+    "        'content': [\n",
+    "            {\"type\": \"video\", \"video\": \"https://github.com/bebechien/gemma/raw/refs/heads/main/videos/ForBiggerBlazes.mp4\"},\n",
+    "            {'type': 'text', 'text': 'Describe this video.'}\n",
+    "        ]\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "inputs = processor.apply_chat_template(\n",
+    "    messages,\n",
+    "    tokenize=True,\n",
+    "    return_dict=True,\n",
+    "    return_tensors=\"pt\",\n",
+    "    add_generation_prompt=True,\n",
+    ").to(model.device)\n",
+    "\n",
+    "outputs = model.generate(**inputs, max_new_tokens=512)\n",
+    "\n",
+    "response = processor.decode(outputs, skip_special_tokens=False)\n",
+    "print(response[0])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "In this guide, you learned how to process video data using Gemma 4 models. The examples demonstrated how to use the `processor` to automatically process the input video and generate textual understanding of the video content.\n",
+    "\n",
+    "<!-- TODO: Port over\n",
+    "### Next steps\n",
+    "\n",
+    "- [Prompt and system instructions](../prompt-formatting.md)\n",
+    "- [Function calling](function-calling.md)\n",
+    "- [Run Gemma overview](../../deployment/index.md) -->"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "video.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/get-started/capabilities/video.md",
+   "title": "Video Understanding"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/vision-qlora.ipynb b/docs/notebooks/vision-qlora.ipynb
new file mode 100644
index 0000000..12896ca
--- /dev/null
+++ b/docs/notebooks/vision-qlora.ipynb
@@ -0,0 +1,626 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-Tune Gemma for Vision Tasks using Hugging Face Transformers and QLoRA\n",
+    "\n",
+    "\n",
+    "This guide walks you through how to fine-tune Gemma on a custom image and text dataset for a vision task (generating product descriptions) using Hugging Face [Transformers](https://huggingface.co/docs/transformers/index) and [TRL](https://huggingface.co/docs/trl/index). You will learn:\n",
+    "\n",
+    "- What is Quantized Low-Rank Adaptation (QLoRA)\n",
+    "- Setup development environment\n",
+    "- Create and prepare the fine-tuning dataset\n",
+    "- Fine-tune Gemma using TRL and the SFTTrainer\n",
+    "- Test Model Inference and generate product descriptions from images and text.\n",
+    "\n",
+    "> **Note:** This guide requires a GPU which support bfloat16 data type such as NVIDIA L4 or NVIDIA A100 and more than 16GB of memory.\n",
+    "\n",
+    "\n",
+    "## What is Quantized Low-Rank Adaptation (QLoRA)\n",
+    "\n",
+    "This guide demonstrates the use of [Quantized Low-Rank Adaptation (QLoRA)](https://arxiv.org/abs/2305.14314), which emerged as a popular method to efficiently fine-tune LLMs as it reduces computational resource requirements while maintaining high performance. In QloRA, the pretrained model is quantized to 4-bit and the weights are frozen. Then trainable adapter layers (LoRA) are attached and only the adapter layers are trained. Afterwards, the adapter weights can be merged with the base model or kept as a separate adapter.\n",
+    "\n",
+    "## Setup development environment\n",
+    "\n",
+    "The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Install Pytorch & other libraries\n",
+    "%pip install torch tensorboard torchvision\n",
+    "%pip install -U torchao\n",
+    "\n",
+    "# Install Transformers\n",
+    "%pip install \"transformers>=5.10.1\"\n",
+    "\n",
+    "# Install Hugging Face libraries\n",
+    "%pip install datasets accelerate evaluate bitsandbytes trl \"peft>=0.19.0\" protobuf pillow sentencepiece\n",
+    "\n",
+    "# COMMENT IN: if you are running on a GPU that supports BF16 data type and flash attn, such as NVIDIA L4 or NVIDIA A100\n",
+    "#%pip install flash-attn\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Note: If you are using a GPU with Ampere architecture (such as NVIDIA L4) or newer, you can use Flash attention. Flash Attention is a method that significantly speeds computations up and reduces memory usage from quadratic to linear in sequence length, leading to acelerating training up to 3x. Learn more at [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/main).*\n",
+    "\n",
+    "You need a valid Hugging Face Token to publish your model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Login into Hugging Face Hub\n",
+    "from huggingface_hub import login\n",
+    "login()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and prepare the fine-tuning dataset\n",
+    "\n",
+    "When fine-tuning LLMs, it is important to know your use case and the task you want to solve. This helps you create a dataset to fine-tune your model. If you haven't defined your use case yet, you might want to go back to the drawing board.\n",
+    "\n",
+    "As an example, this guide focuses on the following use case:\n",
+    "\n",
+    "- Fine-tuning a Gemma model to generate concise, SEO-optimized product descriptions for an ecommerce platform, specifically tailored for mobile search.\n",
+    "\n",
+    "This guide uses the [philschmid/amazon-product-descriptions-vlm](https://huggingface.co/datasets/philschmid/amazon-product-descriptions-vlm) dataset, a dataset of Amazon product descriptions, including product images and categories.\n",
+    "\n",
+    "Hugging Face TRL supports multimodal conversations. The important piece is the \"image\" role, which tells the processing class that it should load the image. The structure should follow:\n",
+    "\n",
+    "```text\n",
+    "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\":\"You are...\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"...\"}, {\"type\": \"image\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"...\"}]}]}\n",
+    "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\":\"You are...\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"...\"}, {\"type\": \"image\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"...\"}]}]}\n",
+    "{\"messages\": [{\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\":\"You are...\"}]}, {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"...\"}, {\"type\": \"image\"}]}, {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"...\"}]}]}\n",
+    "```\n",
+    "\n",
+    "You can now use the Hugging Face Datasets library to load the dataset and create a prompt template to combine the image, product name, and category, and add a system message. The dataset includes images as`Pil.Image` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from PIL import Image\n",
+    "\n",
+    "# System message for the assistant\n",
+    "system_message = \"You are an expert product description writer for Amazon.\"\n",
+    "\n",
+    "# User prompt that combines the user query and the schema\n",
+    "user_prompt = \"\"\"Create a Short Product description based on the provided <PRODUCT> and <CATEGORY> and image.\n",
+    "Only return description. The description should be SEO optimized and for a better mobile search experience.\n",
+    "\n",
+    "<PRODUCT>\n",
+    "{product}\n",
+    "</PRODUCT>\n",
+    "\n",
+    "<CATEGORY>\n",
+    "{category}\n",
+    "</CATEGORY>\n",
+    "\"\"\"\n",
+    "\n",
+    "# Convert dataset to OAI messages\n",
+    "def format_data(sample):\n",
+    "    return {\n",
+    "        \"messages\": [\n",
+    "            {\n",
+    "                \"role\": \"system\",\n",
+    "                #\"content\": [{\"type\": \"text\", \"text\": system_message}],\n",
+    "                \"content\": system_message,\n",
+    "            },\n",
+    "            {\n",
+    "                \"role\": \"user\",\n",
+    "                \"content\": [\n",
+    "                    {\n",
+    "                        \"type\": \"text\",\n",
+    "                        \"text\": user_prompt.format(\n",
+    "                            product=sample[\"Product Name\"],\n",
+    "                            category=sample[\"Category\"],\n",
+    "                        ),\n",
+    "                    },\n",
+    "                    {\n",
+    "                        \"type\": \"image\",\n",
+    "                        \"image\": sample[\"image\"],\n",
+    "                    },\n",
+    "                ],\n",
+    "            },\n",
+    "            {\n",
+    "                \"role\": \"assistant\",\n",
+    "                \"content\": [{\"type\": \"text\", \"text\": sample[\"description\"]}],\n",
+    "            },\n",
+    "        ],\n",
+    "    }\n",
+    "\n",
+    "def process_vision_info(messages: list[dict]) -> list[Image.Image]:\n",
+    "    image_inputs = []\n",
+    "    # Iterate through each conversation\n",
+    "    for msg in messages:\n",
+    "        # Get content (ensure it's a list)\n",
+    "        content = msg.get(\"content\", [])\n",
+    "        if not isinstance(content, list):\n",
+    "            content = [content]\n",
+    "\n",
+    "        # Check each content element for images\n",
+    "        for element in content:\n",
+    "            if isinstance(element, dict) and (\n",
+    "                \"image\" in element or element.get(\"type\") == \"image\"\n",
+    "            ):\n",
+    "                # Get the image and convert to RGB\n",
+    "                if \"image\" in element:\n",
+    "                    image = element[\"image\"]\n",
+    "                else:\n",
+    "                    image = element\n",
+    "                image_inputs.append(image.convert(\"RGB\"))\n",
+    "    return image_inputs\n",
+    "\n",
+    "# Load dataset from the hub\n",
+    "dataset = load_dataset(\"philschmid/amazon-product-descriptions-vlm\", split=\"train\")\n",
+    "dataset = dataset.train_test_split(test_size=0.1)\n",
+    "\n",
+    "# Convert dataset to OAI messages\n",
+    "# need to use list comprehension to keep Pil.Image type, .mape convert image to bytes\n",
+    "dataset_train = [format_data(sample) for sample in dataset[\"train\"]]\n",
+    "dataset_test = [format_data(sample) for sample in dataset[\"test\"]]\n",
+    "\n",
+    "print(dataset_train[345][\"messages\"])\n",
+    "[{'role': 'system', 'content': 'You are an expert product description writer for Amazon.'}, {'role': 'user', 'content': [{'type': 'text', 'text': 'Create a Short Product description based on the provided <PRODUCT> and <CATEGORY> and image.\\nOnly return description. The description should be SEO optimized and for a better mobile search experience.\\n\\n<PRODUCT>\\nBachmann Trains inches 6 inches Pine Trees with Snow 6 Per Box\\n</PRODUCT>\\n\\n<CATEGORY>\\nToys & Games | Hobbies | Trains & Accessories | Accessories | Scenery\\n</CATEGORY>\\n'}, {'type': 'image', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=173x500 at 0x7ED5E4422C00>}]}, {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Enhance your model train landscape with these realistic Bachmann Trains 6-inch pine trees, flocked with snow.  Six highly detailed trees per box, perfect for adding winter charm to your HO scale layout.  Create a captivating snowy scene!'}]}]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fine-tune Gemma using TRL and the SFTTrainer\n",
+    "\n",
+    "You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) makes it straightforward to supervise fine-tune open LLMs. The `SFTTrainer` is a subclass of the `Trainer` from the `transformers` library and supports all the same features, including logging, evaluation, and checkpointing, but adds additional quality of life features, including:\n",
+    "\n",
+    "- Dataset formatting, including conversational and instruction formats\n",
+    "- Training on completions only, ignoring prompts\n",
+    "- Packing datasets for more efficient training\n",
+    "- Parameter-efficient fine-tuning (PEFT) support including QloRA\n",
+    "- Preparing the model and tokenizer for conversational fine-tuning (such as adding special tokens)\n",
+    "\n",
+    "The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig\n",
+    "from peft import prepare_model_for_kbit_training\n",
+    "\n",
+    "# Hugging Face model id\n",
+    "model_id = \"google/gemma-4-E2B\"\n",
+    "\n",
+    "# Check if GPU supports bfloat16\n",
+    "if not torch.cuda.is_bf16_supported():\n",
+    "    raise ValueError(\"GPU does not support bfloat16, please use a GPU that supports bfloat16.\")\n",
+    "\n",
+    "torch_dtype = torch.bfloat16\n",
+    "\n",
+    "# Define model init arguments\n",
+    "model_kwargs = dict(\n",
+    "    dtype=torch_dtype, # What torch dtype to use\n",
+    "    device_map=\"auto\", # Let torch decide how to load the model\n",
+    ")\n",
+    "\n",
+    "# BitsAndBytesConfig int-4 config\n",
+    "model_kwargs[\"quantization_config\"] = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_quant_type=\"nf4\",\n",
+    "    bnb_4bit_compute_dtype=torch_dtype,\n",
+    "    bnb_4bit_quant_storage=torch_dtype,\n",
+    ")\n",
+    "\n",
+    "# Load model and processor\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(model_id, **model_kwargs)\n",
+    "processor = AutoProcessor.from_pretrained(\"google/gemma-4-E2B-it\") # Load the Instruction Tokenizer to use the official Gemma template\n",
+    "\n",
+    "# Prepare model for QLoRA training\n",
+    "model = prepare_model_for_kbit_training(model)\n",
+    "Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]\n",
+    "/usr/local/lib/python3.12/dist-packages/bitsandbytes/backends/cuda/ops.py:213: FutureWarning: _check_is_size will be removed in a future PyTorch release along with guard_size_oblivious.     Use _check(i >= 0) instead.\n",
+    "  torch._check_is_size(blocksize)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `SFTTrainer` supports a built-in integration with `peft`, which makes it straightforward to efficiently tune LLMs using QLoRA. You only need to create a `LoraConfig` and provide it to the trainer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig\n",
+    "\n",
+    "peft_config = LoraConfig(\n",
+    "    lora_alpha=16,\n",
+    "    lora_dropout=0.05,\n",
+    "    r=16,\n",
+    "    bias=\"none\",\n",
+    "    # no target_modules --- PEFT's Gemma 4 defaults scope to the LM layers\n",
+    "    task_type=\"CAUSAL_LM\",\n",
+    "    modules_to_save=[\"lm_head\", \"embed_tokens\"], # make sure to save the lm_head and embed_tokens as you train the special tokens\n",
+    "    ensure_weight_tying=True,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before you can start your training, you need to define the hyperparameter you want to use in a `SFTConfig` and a custom `collate_fn` to handle the vision processing. The `collate_fn` converts the messages with text and images into a format that the model can understand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from trl import SFTConfig\n",
+    "\n",
+    "args = SFTConfig(\n",
+    "    output_dir=\"gemma-product-description\",     # directory to save and repository id\n",
+    "    num_train_epochs=3,                         # number of training epochs\n",
+    "    per_device_train_batch_size=1,              # batch size per device during training\n",
+    "    per_device_eval_batch_size=1,               # batch size per device during evaluation\n",
+    "    optim=\"adamw_torch_fused\",                  # use fused adamw optimizer\n",
+    "    logging_steps=5,                            # log every 5 steps\n",
+    "    save_strategy=\"epoch\",                      # save checkpoint every epoch\n",
+    "    eval_strategy=\"epoch\",                      # evaluate checkpoint every epoch\n",
+    "    learning_rate=2e-4,                         # learning rate, based on QLoRA paper\n",
+    "    bf16=True,                                  # use bfloat16 precision\n",
+    "    max_grad_norm=0.3,                          # max gradient norm based on QLoRA paper\n",
+    "    lr_scheduler_type=\"constant\",               # use constant learning rate scheduler\n",
+    "    push_to_hub=True,                           # push model to hub\n",
+    "    report_to=\"tensorboard\",                    # report metrics to tensorboard\n",
+    "    dataset_text_field=\"\",                      # need a dummy field for collator\n",
+    "    dataset_kwargs={\"skip_prepare_dataset\": True}, # important for collator\n",
+    "    remove_unused_columns = False               # important for collator\n",
+    ")\n",
+    "\n",
+    "# Create a data collator to encode text and image pairs\n",
+    "def collate_fn(examples):\n",
+    "    texts = []\n",
+    "    images = []\n",
+    "    for example in examples:\n",
+    "        image_inputs = process_vision_info(example[\"messages\"])\n",
+    "        text = processor.apply_chat_template(\n",
+    "            example[\"messages\"], add_generation_prompt=False, tokenize=False\n",
+    "        )\n",
+    "        texts.append(text.strip())\n",
+    "        images.append(image_inputs)\n",
+    "\n",
+    "    # Tokenize the texts and process the images\n",
+    "    batch = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n",
+    "\n",
+    "    # The labels are the input_ids, and we mask the padding tokens and image tokens in the loss computation\n",
+    "    labels = batch[\"input_ids\"].clone()\n",
+    "\n",
+    "    # Mask tokens for not being used in the loss computation\n",
+    "    labels[labels == processor.tokenizer.pad_token_id] = -100\n",
+    "    labels[labels == processor.tokenizer.boi_token_id] = -100\n",
+    "    labels[labels == processor.tokenizer.image_token_id] = -100\n",
+    "    labels[labels == processor.tokenizer.eoi_token_id] = -100\n",
+    "\n",
+    "    batch[\"labels\"] = labels\n",
+    "    return batch\n",
+    "/tmp/ipykernel_26645/1232990497.py:3: FutureWarning: The default `loss_type` will change from `'nll'` to `'chunked_nll'` in TRL 1.7. For standard models this is transparent (same math, lower memory) and no action is needed — you'll get the new default automatically on upgrade. If you use a custom model, check ahead of time that `loss_type='chunked_nll'` runs and yields the same loss as `'nll'`; if it doesn't, pin `loss_type='nll'` to keep the current behavior and please open an issue at https://github.com/huggingface/trl/issues so we can address the edge case.\n",
+    "  args = SFTConfig(\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You now have every building block you need to create your `SFTTrainer` to start the training of your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "\n",
+    "# Create Trainer object\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=args,\n",
+    "    train_dataset=dataset_train,\n",
+    "    eval_dataset=dataset_test,\n",
+    "    peft_config=peft_config,\n",
+    "    processing_class=processor,\n",
+    "    data_collator=collate_fn,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start training by calling the `train()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# Start training, the model will be automatically saved to the Hub and the output directory\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save the final model again to the Hugging Face Hub\n",
+    "trainer.save_model()\n",
+    "[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 1, 'bos_token_id': 2, 'pad_token_id': 0}.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\[3630/3630 57:38, Epoch 3/3\\]\n",
+    "\n",
+    "| Epoch | Training Loss | Validation Loss | Entropy | Mean Token Accuracy | Num Tokens |\n",
+    "|---|---|---|---|---|---|\n",
+    "| 1 | 0.835934 | 0.844098 | 0.735220 | 0.818571 | 521741.000000 |\n",
+    "| 2 | 0.411868 | 0.886929 | 0.548703 | 0.822062 | 1043482.000000 |\n",
+    "| 3 | 0.488929 | 1.007192 | 0.417825 | 0.822075 | 1565223.000000 |\n",
+    "\n",
+    "\n",
+    "\n",
+    "Before you can test your model, make sure to free the memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "# free the memory again\n",
+    "del model\n",
+    "del trainer\n",
+    "torch.cuda.empty_cache()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using QLoRA, you only train adapters and not the full model. This means when saving the model during training you only save the adapter weights and not the full model. If you want to save the full model, which makes it easier to use with serving stacks like vLLM or TGI, you can merge the adapter weights into the model weights using the `merge_and_unload` method and then save the model with the `save_pretrained` method. This saves a default model, which can be used for inference.\n",
+    "\n",
+    "> **Note:** It requires more than 30GB of CPU Memory when you want to merge the adapter into the model. You can skip this and continue with Test Model Inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForMultimodalLM, AutoProcessor\n",
+    "from peft import PeftModel\n",
+    "\n",
+    "# Load Model base model\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(model_id, low_cpu_mem_usage=True)\n",
+    "\n",
+    "# Merge LoRA and base model and save\n",
+    "peft_model = PeftModel.from_pretrained(model, args.output_dir)\n",
+    "merged_model = peft_model.merge_and_unload()\n",
+    "merged_model.save_pretrained(\"merged_model\", safe_serialization=True, max_shard_size=\"2GB\")\n",
+    "\n",
+    "processor = AutoProcessor.from_pretrained(args.output_dir)\n",
+    "processor.save_pretrained(\"merged_model\")\n",
+    "Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]\n",
+    "Writing model shards:   0%|          | 0/4 [00:00<?, ?it/s]\n",
+    "['merged_model/processor_config.json']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test Model Inference and generate product descriptions\n",
+    "\n",
+    "After the training is done, you'll want to evaluate and test your model. You can load different samples from the test dataset and evaluate the model on those samples.\n",
+    "\n",
+    "> **Note:** Evaluating Generative AI models is not a trivial task since one input can have multiple correct outputs. This guide only focuses on manual evaluation and vibe checks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForMultimodalLM, AutoProcessor\n",
+    "\n",
+    "model_id = \"merged_model\"\n",
+    "\n",
+    "# Load Model with PEFT adapter\n",
+    "model = AutoModelForMultimodalLM.from_pretrained(\n",
+    "  model_id,\n",
+    "  device_map=\"auto\",\n",
+    "  dtype=\"auto\",\n",
+    ")\n",
+    "processor = AutoProcessor.from_pretrained(model_id)\n",
+    "Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can test inference by providing a product name, category and image. The `sample` includes a marvel action figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "language": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Test sample with Product Name, Category and Image\n",
+    "sample = {\n",
+    "  \"product_name\": \"Hasbro Marvel Avengers-Serie Marvel Assemble Titan-Held, Iron Man, 30,5 cm Actionfigur\",\n",
+    "  \"category\": \"Toys & Games | Toy Figures & Playsets | Action Figures\",\n",
+    "  \"image\": Image.open(requests.get(\"https://m.media-amazon.com/images/I/81+7Up7IWyL._AC_SY300_SX300_.jpg\", stream=True).raw).convert(\"RGB\")\n",
+    "}\n",
+    "\n",
+    "def generate_description(sample, model, processor):\n",
+    "    # Convert sample into messages and then apply the chat template\n",
+    "    messages = [\n",
+    "        {\"role\": \"system\", \"content\": system_message},\n",
+    "        {\"role\": \"user\", \"content\": [\n",
+    "            {\"type\": \"image\",\"image\": sample[\"image\"]},\n",
+    "            {\"type\": \"text\", \"text\": user_prompt.format(product=sample[\"product_name\"], category=sample[\"category\"])},\n",
+    "        ]},\n",
+    "    ]\n",
+    "    text = processor.apply_chat_template(\n",
+    "        messages, tokenize=False, add_generation_prompt=True\n",
+    "    )\n",
+    "    print(text)\n",
+    "    # Process the image and text\n",
+    "    image_inputs = process_vision_info(messages)\n",
+    "    # Tokenize the text and process the images\n",
+    "    inputs = processor(\n",
+    "        text=[text],\n",
+    "        images=image_inputs,\n",
+    "        padding=True,\n",
+    "        return_tensors=\"pt\",\n",
+    "    )\n",
+    "    # Move the inputs to the device\n",
+    "    inputs = inputs.to(model.device)\n",
+    "\n",
+    "    # Generate the output\n",
+    "    stop_token_ids = [processor.tokenizer.eos_token_id, processor.tokenizer.convert_tokens_to_ids(\"<turn|>\")]\n",
+    "    generated_ids = model.generate(**inputs, max_new_tokens=256, top_p=1.0, do_sample=True, temperature=0.8, eos_token_id=stop_token_ids, disable_compile=True)\n",
+    "    # Trim the generation and decode the output to text\n",
+    "    generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]\n",
+    "    output_text = processor.batch_decode(\n",
+    "        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False\n",
+    "    )\n",
+    "    return output_text[0]\n",
+    "\n",
+    "# generate the description\n",
+    "description = generate_description(sample, model, processor)\n",
+    "print(\"MODEL OUTPUT>> \\n\")\n",
+    "print(description)\n",
+    "<bos><|turn>system\n",
+    "You are an expert product description writer for Amazon.<turn|>\n",
+    "<|turn>user\n",
+    "<|image|>Create a Short Product description based on the provided <PRODUCT> and <CATEGORY> and image.\n",
+    "Only return description. The description should be SEO optimized and for a better mobile search experience.\n",
+    "\n",
+    "<PRODUCT>\n",
+    "Hasbro Marvel Avengers-Serie Marvel Assemble Titan-Held, Iron Man, 30,5 cm Actionfigur\n",
+    "</PRODUCT>\n",
+    "\n",
+    "<CATEGORY>\n",
+    "Toys & Games | Toy Figures & Playsets | Action Figures\n",
+    "</CATEGORY><turn|>\n",
+    "<|turn>model\n",
+    "\n",
+    "MODEL OUTPUT>> \n",
+    "\n",
+    "Bring home the action with the Hasbro Marvel Avengers Assemble Titan-held Iron Man Action Figure! This highly detailed 30.5-centimeter figure is perfect for recreating epic battles.  Collect the entire Marvel Avengers Assemble Titan-held series for an authentic experience.  Shop now!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary and next steps\n",
+    "\n",
+    "This tutorial covered how to fine-tune a Gemma model for vision tasks using TRL and QLoRA, specifically for generating product descriptions. Check out the following docs next:\n",
+    "\n",
+    "- Learn how to [generate text with a Gemma model](../get-started/quickstart.md).\n",
+    "- Learn how to [fine-tune Gemma for text tasks using Hugging Face Transformers](qlora.md).\n",
+    "- Learn how to [full model fine-tune using Hugging Face Transformers](full-finetune.md).\n",
+    "- Learn how to perform [distributed fine-tuning and inference on a Gemma model](distributed.md).\n",
+    "- Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+    "- Learn how to [fine-tune Gemma using KerasNLP and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb)."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "vision-qlora.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3"
+  },
+  "gemma_docs": {
+   "canonical_source": "docs/tuning/vision-qlora.md",
+   "title": "Fine-Tune Gemma for Vision Tasks using Hugging Face Transformers and QLoRA"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/page-metadata.json b/docs/page-metadata.json
new file mode 100644
index 0000000..2e9127d
--- /dev/null
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+      "estimated_tokens": 928,
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+      "markdown_url": "/tuning/qlora.md",
+      "estimated_tokens": 6042,
+      "last_updated": "2026-08-05"
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+    "tuning/vision-qlora.md": {
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+      "markdown_url": "/tuning/vision-qlora.md",
+      "estimated_tokens": 5232,
+      "last_updated": "2026-08-05"
+    },
+    "tutorials/visual-guides/diffusiongemma.md": {
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+      "markdown_url": "/tutorials/visual-guides/diffusiongemma.md",
+      "estimated_tokens": 9535,
+      "last_updated": "2026-08-05"
+    },
+    "tutorials/visual-guides/gemma-4-12b.md": {
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+      "markdown_url": "/tutorials/visual-guides/gemma-4-12b.md",
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+    },
+    "tutorials/visual-guides/gemma-4.md": {
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+      "markdown_url": "/tutorials/visual-guides/gemma-4.md",
+      "estimated_tokens": 8765,
+      "last_updated": "2026-08-05"
+    },
+    "tutorials/visual-guides/mtp.md": {
+      "url": "/tutorials/visual-guides/mtp/",
+      "markdown_url": "/tutorials/visual-guides/mtp.md",
+      "estimated_tokens": 3102,
+      "last_updated": "2026-08-05"
+    }
+  }
+}
diff --git a/docs/stylesheets/embeds.css b/docs/stylesheets/embeds.css
new file mode 100644
index 0000000..33f6f56
--- /dev/null
+++ b/docs/stylesheets/embeds.css
@@ -0,0 +1,25 @@
+/* External interactive demos. */
+.md-typeset .demo-embed {
+  width: 100%;
+  min-height: 42rem;
+  margin: var(--gemma-space-5) 0 var(--gemma-space-3);
+  overflow: hidden;
+  border: 1px solid var(--md-default-fg-color--lightest);
+  border-radius: var(--gemma-radius-lg);
+  background: var(--md-default-bg-color);
+  box-shadow: var(--gemma-shadow-sm);
+}
+
+.md-typeset .demo-embed iframe {
+  display: block;
+  width: 100%;
+  min-height: 42rem;
+  border: 0;
+}
+
+@media screen and (max-width: 44.9375em) {
+  .md-typeset .demo-embed,
+  .md-typeset .demo-embed iframe {
+    min-height: 36rem;
+  }
+}
diff --git a/docs/stylesheets/page-context.css b/docs/stylesheets/page-context.css
index 3e032ea..6637111 100644
--- a/docs/stylesheets/page-context.css
+++ b/docs/stylesheets/page-context.css
@@ -52,6 +52,18 @@
   text-decoration: underline;
 }
 
+.md-typeset .page-context__colab {
+  display: inline-flex;
+  align-items: center;
+  gap: var(--gemma-space-1);
+}
+
+.page-context__colab img {
+  display: block;
+  width: 1rem;
+  height: 1rem;
+}
+
 .page-context__picker {
   position: relative;
   display: inline-flex;
diff --git a/docs/tuning/full-finetune.md b/docs/tuning/full-finetune.md
index 90929d1..e51ff63 100644
--- a/docs/tuning/full-finetune.md
+++ b/docs/tuning/full-finetune.md
@@ -4,6 +4,7 @@ description: This guide walks you through how to fine-tune Gemma on a mobile gam
 models: [gemma-3]
 frameworks: [transformers, trl]
 source_url: "https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetune.md.txt"
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/full-finetune.ipynb
 ---
 # Full Model Fine-Tune using Hugging Face Transformers
 
@@ -23,7 +24,7 @@ This guide walks you through how to fine-tune Gemma on a mobile game NPC dataset
 
 The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model, including different RLHF and alignment techniques.
 
-```python
+```python { .notebook-cell }
 # Install Pytorch & other libraries
 %pip install torch tensorboard
 
@@ -42,7 +43,7 @@ Before you can start training, you have to make sure that you accepted the terms
 
 After you have accepted the license, you need a valid Hugging Face Token to access the model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training.
 
-```python
+```python { .notebook-cell }
 from google.colab import userdata
 from huggingface_hub import login
 
@@ -53,14 +54,14 @@ login(hf_token)
 
 You can keep the results on Colab's local virtual machine. However, we highly recommend saving your intermediate results to your Google Drive. This ensures your training results are safe and allows you to easily compare and select the best model.
 
-```python
+```python { .notebook-cell }
 from google.colab import drive
 drive.mount('/content/drive')
 ```
 
 Select the base model to fine-tune, adjust the checkpoint directory and the learning rate.
 
-```python
+```python { .notebook-cell }
 base_model = "google/gemma-3-270m-it" # @param ["google/gemma-3-270m-it","google/gemma-3-1b-it","google/gemma-3-4b-it","google/gemma-3-12b-it","google/gemma-3-27b-it"] {"allow-input":true}
 checkpoint_dir = "/content/drive/MyDrive/MyGemmaNPC"
 learning_rate = 5e-5
@@ -112,7 +113,7 @@ You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https:/
 
 The following code loads the Gemma model and tokenizer from Hugging Face.
 
-```python
+```python { .notebook-cell }
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
@@ -135,7 +136,7 @@ DType: torch.bfloat16
 
 The output below shows that the out-of-the-box capabilities may not be good enough for this use case.
 
-```python
+```python { .notebook-cell }
 from transformers import pipeline
 
 from random import randint
@@ -171,7 +172,7 @@ The example above checks the model's primary function of generating in-game dial
 
 The goal is to see if the model can stay in character rather than answering the out-of-context question. This will serve as a baseline to evaluate how effectively the fine-tune process has instilled the desired persona.
 
-```python
+```python { .notebook-cell }
 outputs = pipe([{"role": "user", "content": "Sorry, you are a game NPC."}], max_new_tokens=256, disable_compile=True)
 print(outputs[0]['generated_text'][1]['content'])
 Okay, I'm ready. Let's begin!
@@ -179,7 +180,7 @@ Okay, I'm ready. Let's begin!
 
 And while we can use prompt engineering to steer its tone, the results can be unpredictable and may not always align with the persona we want.
 
-```python
+```python { .notebook-cell }
 message = [
     # give persona
     {"role": "system", "content": "You are a Martian NPC with a unique speaking style. Use an accent that replaces 's' sounds with 'z', uses 'da' for 'the', 'diz' for 'this', and includes occasional clicks like *k'tak*."},
@@ -212,7 +213,7 @@ This is a cave. It's made of rock and dust.
 
 Before you can start your training, you need to define the hyperparameters you want to use in a `SFTConfig` instance.
 
-```python
+```python { .notebook-cell }
 from trl import SFTConfig
 
 torch_dtype = model.dtype
@@ -243,7 +244,7 @@ args = SFTConfig(
 
 You now have every building block you need to create your `SFTTrainer` to start the training of your model.
 
-```python
+```python { .notebook-cell }
 from trl import SFTTrainer
 
 # Create Trainer object
@@ -262,7 +263,7 @@ Truncating eval dataset:   0%|          | 0/5 [00:00<?, ? examples/s]
 
 Start training by calling the `train()` method.
 
-```python
+```python { .notebook-cell }
 # Start training, the model will be automatically saved to the Hub and the output directory
 trainer.train()
 
@@ -285,7 +286,7 @@ To plot the training and validation losses, you would typically extract these va
 
 Libraries like Matplotlib can then be used to visualize these values over training steps or epochs. The x-asis would represent the training steps or epochs, and the y-axis would represent the corresponding loss values.
 
-```python
+```python { .notebook-cell }
 import matplotlib.pyplot as plt
 
 # Access the log history
@@ -325,7 +326,7 @@ After the training is done, you'll want to evaluate and test your model. You can
 
 For this particular use case, the best model is a matter of preference. Interestingly, what we'd normally call 'overfitting' can be very useful for a game NPC. It forces the model to forget general information and instead lock onto the specific persona and characteristics it was trained on, ensuring it stays consistently in character.
 
-```python
+```python { .notebook-cell }
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 model_id = checkpoint_dir
@@ -342,7 +343,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 
 Let's load all questions from the test dataset and generate outputs.
 
-```python
+```python { .notebook-cell }
 from transformers import pipeline
 
 # Load the model and tokenizer into the pipeline
@@ -402,7 +403,7 @@ A genuine Martian Zcrap-fruit. Very... strange. Why you burn it with... k'tak...
 
 If you try our original generalist prompt, you can see that the model still attempts to answer in the trained style. In this example overfitting and catastrophic forgetting are actually beneficial for the game NPC because it will begin forgetting general knowledge which might not be applicable. This is also true for other types of full fine-tuning where the goal is to restrict the output to specific data formats.
 
-```python
+```python { .notebook-cell }
 outputs = pipe([{"role": "user", "content": "Sorry, you are a game NPC."}], max_new_tokens=256, disable_compile=True)
 print(outputs[0]['generated_text'][1]['content'])
 Nameless. You... you z-mell like... wet plantz. Why you wear shiny piecez on your head?
diff --git a/docs/tuning/qlora.md b/docs/tuning/qlora.md
index 793ca92..be323d9 100644
--- a/docs/tuning/qlora.md
+++ b/docs/tuning/qlora.md
@@ -4,6 +4,7 @@ description: This guide walks you through how to fine-tune Gemma on a custom tex
 models: [gemma-4]
 frameworks: [transformers, peft, trl]
 source_url: "https://ai.google.dev/gemma/docs/core/huggingface_text_finetune_qlora.md.txt"
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/qlora.ipynb
 ---
 # Fine-Tune Gemma using Hugging Face Transformers and QloRA
 
@@ -28,7 +29,7 @@ This guide demonstrates the use of [Quantized Low-Rank Adaptation (QLoRA)](https
 
 The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model, including different RLHF and alignment techniques.
 
-```python
+```python { .notebook-cell }
 # Install Pytorch & other libraries
 %pip install torch tensorboard
 %pip install -U torchao
@@ -47,7 +48,7 @@ The first step is to install Hugging Face Libraries, including TRL, and datasets
 
 You need a valid Hugging Face Token to publish your model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training.
 
-```python
+```python { .notebook-cell }
 # Login into Hugging Face Hub
 from huggingface_hub import login
 login()
@@ -86,7 +87,7 @@ The [philschmid/gretel-synthetic-text-to-sql](https://huggingface.co/datasets/ph
 
 You can now use the Hugging Face Datasets library to load the dataset and create a prompt template to combine the natural language instruction, schema definition and add a system message for your assistant.
 
-```python
+```python { .notebook-cell }
 from datasets import load_dataset
 
 # System message for the assistant
@@ -141,7 +142,7 @@ You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https:/
 
 The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration.
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 import torch
 from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig
 from peft import prepare_model_for_kbit_training
@@ -187,7 +188,7 @@ Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
 
 The `SFTTrainer` supports a built-in integration with `peft`, which makes it straightforward to efficiently tune LLMs using QLoRA. You only need to create a `LoraConfig` and provide it to the trainer.
 
-```python
+```python { .notebook-cell }
 from peft import LoraConfig
 
 peft_config = LoraConfig(
@@ -204,7 +205,7 @@ peft_config = LoraConfig(
 
 Before you can start your training, you need to define the hyperparameter you want to use in a `SFTConfig` instance.
 
-```python
+```python { .notebook-cell }
 import torch
 from trl import SFTConfig
 
@@ -302,7 +303,7 @@ def collate_fn(examples):
 
 You now have every building block you need to create your `SFTTrainer` to start the training of your model.
 
-```python
+```python { .notebook-cell }
 from trl import SFTTrainer
 
 # Create Trainer object
@@ -319,7 +320,7 @@ trainer = SFTTrainer(
 
 Start training by calling the `train()` method.
 
-```python
+```python { .notebook-cell }
 # Start training, the model will be automatically saved to the Hub and the output directory
 trainer.train()
 
@@ -352,7 +353,7 @@ WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Sk
 
 Before you can test your model, make sure to free the memory.
 
-```python
+```python { .notebook-cell }
 # free the memory again
 del model
 del trainer
@@ -365,7 +366,7 @@ When using QLoRA, you only train adapters and not the full model. This means whe
     It requires more than 30GB of CPU Memory when you want to merge the adapter into the model. You can skip this and continue with Test Model Inference.
 
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 from transformers import AutoModelForMultimodalLM, AutoProcessor
 from peft import PeftModel
 
@@ -392,7 +393,7 @@ After the training is done, you'll want to evaluate and test your model. You can
     Evaluating generative AI models is not a trivial task since one input can have multiple correct outputs. This guide only focuses on manual evaluation and vibe checks.
 
 
-```python
+```python { .notebook-cell }
 from transformers import AutoModelForMultimodalLM, AutoProcessor
 
 model_id = "merged_model"
@@ -409,7 +410,7 @@ Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
 
 Let's load a random sample from the test dataset and generate a SQL command.
 
-```python
+```python { .notebook-cell }
 from random import randint
 import re
 from transformers import pipeline, GenerationConfig, pipeline
diff --git a/docs/tuning/vision-qlora.md b/docs/tuning/vision-qlora.md
index ba40a45..f217f54 100644
--- a/docs/tuning/vision-qlora.md
+++ b/docs/tuning/vision-qlora.md
@@ -4,6 +4,7 @@ description: This guide walks you through how to fine-tune Gemma on a custom ima
 models: [gemma-4]
 frameworks: [transformers, peft, trl]
 source_url: "https://ai.google.dev/gemma/docs/core/huggingface_vision_finetune_qlora.md.txt"
+colab: https://colab.research.google.com/github/google-gemma/docs/blob/main/docs/notebooks/vision-qlora.ipynb
 ---
 # Fine-Tune Gemma for Vision Tasks using Hugging Face Transformers and QLoRA
 
@@ -28,7 +29,7 @@ This guide demonstrates the use of [Quantized Low-Rank Adaptation (QLoRA)](https
 
 The first step is to install Hugging Face Libraries, including TRL, and datasets to fine-tune open model.
 
-```python
+```python { .notebook-cell }
 # Install Pytorch & other libraries
 %pip install torch tensorboard torchvision
 %pip install -U torchao
@@ -47,7 +48,7 @@ The first step is to install Hugging Face Libraries, including TRL, and datasets
 
 You need a valid Hugging Face Token to publish your model. If you are running inside a Google Colab, you can securely use your Hugging Face Token using the Colab secrets otherwise you can set the token as directly in the `login` method. Make sure your token has write access too, as you push your model to the Hub during training.
 
-```python
+```python { .notebook-cell }
 # Login into Hugging Face Hub
 from huggingface_hub import login
 login()
@@ -73,7 +74,7 @@ Hugging Face TRL supports multimodal conversations. The important piece is the "
 
 You can now use the Hugging Face Datasets library to load the dataset and create a prompt template to combine the image, product name, and category, and add a system message. The dataset includes images as`Pil.Image` objects.
 
-```python
+```python { .notebook-cell }
 from datasets import load_dataset
 from PIL import Image
 
@@ -172,7 +173,7 @@ You are now ready to fine-tune your model. Hugging Face TRL [SFTTrainer](https:/
 
 The following code loads the Gemma model and tokenizer from Hugging Face and initializes the quantization configuration.
 
-```python { .model-swap }
+```python { .notebook-cell .model-swap }
 import torch
 from transformers import AutoProcessor, AutoModelForMultimodalLM, BitsAndBytesConfig
 from peft import prepare_model_for_kbit_training
@@ -214,7 +215,7 @@ Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
 
 The `SFTTrainer` supports a built-in integration with `peft`, which makes it straightforward to efficiently tune LLMs using QLoRA. You only need to create a `LoraConfig` and provide it to the trainer.
 
-```python
+```python { .notebook-cell }
 from peft import LoraConfig
 
 peft_config = LoraConfig(
@@ -231,7 +232,7 @@ peft_config = LoraConfig(
 
 Before you can start your training, you need to define the hyperparameter you want to use in a `SFTConfig` and a custom `collate_fn` to handle the vision processing. The `collate_fn` converts the messages with text and images into a format that the model can understand.
 
-```python
+```python { .notebook-cell }
 from trl import SFTConfig
 
 args = SFTConfig(
@@ -286,7 +287,7 @@ def collate_fn(examples):
 
 You now have every building block you need to create your `SFTTrainer` to start the training of your model.
 
-```python
+```python { .notebook-cell }
 from trl import SFTTrainer
 
 # Create Trainer object
@@ -303,7 +304,7 @@ trainer = SFTTrainer(
 
 Start training by calling the `train()` method.
 
-```python
+```python { .notebook-cell }
 # Start training, the model will be automatically saved to the Hub and the output directory
 trainer.train()
 
@@ -334,7 +335,7 @@ WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Sk
 
 Before you can test your model, make sure to free the memory.
 
-```python
+```python { .notebook-cell }
 # free the memory again
 del model
 del trainer
@@ -347,7 +348,7 @@ When using QLoRA, you only train adapters and not the full model. This means whe
     It requires more than 30GB of CPU Memory when you want to merge the adapter into the model. You can skip this and continue with Test Model Inference.
 
 
-```python
+```python { .notebook-cell }
 from transformers import AutoModelForMultimodalLM, AutoProcessor
 from peft import PeftModel
 
@@ -374,7 +375,7 @@ After the training is done, you'll want to evaluate and test your model. You can
     Evaluating Generative AI models is not a trivial task since one input can have multiple correct outputs. This guide only focuses on manual evaluation and vibe checks.
 
 
-```python
+```python { .notebook-cell }
 from transformers import AutoModelForMultimodalLM, AutoProcessor
 
 model_id = "merged_model"
@@ -391,7 +392,7 @@ Loading weights:   0%|          | 0/1951 [00:00<?, ?it/s]
 
 You can test inference by providing a product name, category and image. The `sample` includes a marvel action figure.
 
-```python
+```python { .notebook-cell }
 import requests
 from PIL import Image
 
diff --git a/overrides/partials/model-names.html b/overrides/partials/model-names.html
index 01f867c..906f82f 100644
--- a/overrides/partials/model-names.html
+++ b/overrides/partials/model-names.html
@@ -5,6 +5,7 @@
   Verified by: uv run python scripts/lint_docs.py --check generated
 
   `gemma_model_names` maps every family, size and framework id to its display name.
+  `gemma_framework_short_names` maps tools with a compact UI label to that label.
   `gemma_model_urls` maps a family id to its canonical local documentation page.
   `gemma_framework_urls` maps a tool id to its project home.
 
@@ -90,3 +91,7 @@
   "android"              : "https://developer.android.com",
   "ios"                  : "https://developer.apple.com/ios/",
 } %}
+
+{% set gemma_framework_short_names = {
+  "transformers": "Transformers",
+} %}
diff --git a/overrides/partials/page-context.html b/overrides/partials/page-context.html
index 0ee74c6..a1bb055 100644
--- a/overrides/partials/page-context.html
+++ b/overrides/partials/page-context.html
@@ -4,6 +4,7 @@
 {%- set models = page.meta.models if page.meta and page.meta.models else [] -%}
 {%- set requires = page.meta.requires if page.meta and page.meta.requires else "" -%}
 {%- set frameworks = page.meta.frameworks if page.meta and page.meta.frameworks else [] -%}
+{%- set colab = page.meta.colab if page.meta and page.meta.colab else "" -%}
 {%- set ns = namespace(families=[]) -%}
 {%- for id in models -%}
   {%- set family = id | split("/") | first -%}
@@ -11,7 +12,7 @@
     {%- set ns.families = ns.families + [family] -%}
   {%- endif -%}
 {%- endfor -%}
-{%- if models or frameworks %}
+{%- if models or frameworks or colab %}
 <div class="page-context" data-requires="{{ requires }}">
   {%- if ns.families %}
   <span class="page-context__item">
@@ -32,12 +33,20 @@
     {%- for id in frameworks %}
     {%- set url = gemma_framework_urls[id] | default("") %}
     {%- if url %}
-    <a class="page-context__value page-context__value--link" href="{{ url }}">{{ gemma_model_names[id] | default(id) }}</a>
+    <a class="page-context__value page-context__value--link" href="{{ url }}" title="{{ gemma_model_names[id] | default(id) }}">{{ gemma_framework_short_names[id] | default(gemma_model_names[id]) | default(id) }}</a>
     {%- else %}
-    <span class="page-context__value">{{ gemma_model_names[id] | default(id) }}</span>
+    <span class="page-context__value" title="{{ gemma_model_names[id] | default(id) }}">{{ gemma_framework_short_names[id] | default(gemma_model_names[id]) | default(id) }}</span>
     {%- endif %}
     {%- endfor %}
   </span>
   {%- endif %}
+  {%- if colab %}
+  <span class="page-context__item page-context__item--colab">
+    <a class="page-context__value page-context__value--link page-context__colab" href="{{ colab }}" target="_blank" rel="noopener" title="Open in Google Colab" aria-label="Open in Google Colab">
+      <img src="https://www.tensorflow.org/images/colab_logo_32px.png" alt="" width="16" height="16">
+      <span>Colab</span>
+    </a>
+  </span>
+  {%- endif %}
 </div>
 {%- endif %}
diff --git a/scripts/agent_docs.py b/scripts/agent_docs.py
new file mode 100644
index 0000000..17d9565
--- /dev/null
+++ b/scripts/agent_docs.py
@@ -0,0 +1,246 @@
+#!/usr/bin/env python3
+"""Generate agent-facing documentation artifacts from the canonical Markdown pages.
+
+The committed outputs (`docs/llms.txt` and `docs/page-metadata.json`) work with the normal
+Zensical preview. After a site build, `--publish site` also writes raw Markdown beside the
+rendered HTML using Stripe-style URLs such as `/get-started/quickstart.md`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import subprocess
+import tomllib
+from datetime import date
+from pathlib import Path
+from typing import Iterable
+
+import yaml
+from jinja2 import Environment, StrictUndefined
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DOCS_DIR = REPO_ROOT / "docs"
+CONFIG_FILE = REPO_ROOT / "zensical.toml"
+MODELS_FILE = REPO_ROOT / "data" / "models.yml"
+LLMS_TARGET = DOCS_DIR / "llms.txt"
+METADATA_TARGET = DOCS_DIR / "page-metadata.json"
+SITE_URL = "https://gemma.ai"
+
+TOKEN = re.compile(r"\w+|[^\w\s]", re.UNICODE)
+H1 = re.compile(r"^#\s+", re.MULTILINE)
+
+
+def split_frontmatter(text: str) -> tuple[dict, str]:
+    if not text.startswith("---"):
+        return {}, text
+    lines = text.splitlines()
+    for index in range(1, len(lines)):
+        if lines[index].strip() == "---":
+            metadata = yaml.safe_load("\n".join(lines[1:index])) or {}
+            if not isinstance(metadata, dict):
+                raise ValueError("frontmatter must be a mapping")
+            return metadata, "\n".join(lines[index + 1 :]).lstrip("\n")
+    raise ValueError("unterminated frontmatter")
+
+
+def source_pages() -> dict[str, tuple[Path, dict, str]]:
+    pages = {}
+    for path in sorted(DOCS_DIR.rglob("*.md")):
+        relative = path.relative_to(DOCS_DIR).as_posix()
+        metadata, body = split_frontmatter(path.read_text(encoding="utf-8"))
+        pages[relative] = (path, metadata, body)
+    return pages
+
+
+def walk_paths(node) -> Iterable[str]:
+    if isinstance(node, str) and node.endswith(".md"):
+        yield node
+    elif isinstance(node, list):
+        for item in node:
+            yield from walk_paths(item)
+    elif isinstance(node, dict):
+        for value in node.values():
+            yield from walk_paths(value)
+
+
+def navigation_sections() -> list[tuple[str, list[str]]]:
+    config = tomllib.loads(CONFIG_FILE.read_text(encoding="utf-8"))["project"]
+    sections = []
+    for item in config["nav"]:
+        if isinstance(item, str):
+            sections.append(("Docs", list(walk_paths(item))))
+            continue
+        for label, children in item.items():
+            sections.append((str(label), list(dict.fromkeys(walk_paths(children)))))
+    return sections
+
+
+def route_for(relative: str) -> str:
+    path = Path(relative)
+    if path.name == "index.md":
+        return "/" if path.parent == Path(".") else f"/{path.parent.as_posix()}/"
+    return f"/{path.with_suffix('').as_posix()}/"
+
+
+def markdown_route_for(relative: str) -> str:
+    route = route_for(relative)
+    return "/index.md" if route == "/" else f"{route.rstrip('/')}.md"
+
+
+def display_title(metadata: dict, family_names: dict[str, str]) -> str:
+    title = str(metadata.get("title", "Untitled"))
+    models = metadata.get("models") or []
+    if title.lower() in {"model card", "overview"} and len(models) == 1:
+        family = str(models[0]).split("/", 1)[0]
+        if family in family_names:
+            return f"{family_names[family]} — {title}"
+    return title
+
+
+def last_updated(path: Path) -> str:
+    relative = path.relative_to(REPO_ROOT).as_posix()
+    result = subprocess.run(
+        ["git", "log", "-1", "--format=%cs", "--", relative],
+        cwd=REPO_ROOT,
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    value = result.stdout.strip()
+    if value:
+        return value
+    return date.fromtimestamp(path.stat().st_mtime).isoformat()
+
+
+def estimated_tokens(body: str) -> int:
+    return max(1, len(TOKEN.findall(body)))
+
+
+def model_data() -> dict:
+    return yaml.safe_load(MODELS_FILE.read_text(encoding="utf-8"))
+
+
+def render_metadata() -> str:
+    pages = source_pages()
+    payload = {
+        "generated_at": "build-time",
+        "pages": {
+            relative: {
+                "url": route_for(relative),
+                "markdown_url": markdown_route_for(relative),
+                "estimated_tokens": estimated_tokens(body),
+                "last_updated": last_updated(path),
+            }
+            for relative, (path, _metadata, body) in pages.items()
+        },
+    }
+    return json.dumps(payload, indent=2, ensure_ascii=False) + "\n"
+
+
+def render_llms() -> str:
+    pages = source_pages()
+    data = model_data()
+    family_names = {str(item["id"]): str(item["name"]) for item in data["families"]}
+    lines = [
+        "# Gemma Documentation",
+        "",
+        "Official documentation for building with the Gemma family of open models.",
+        "Start with Gemma 4 unless a page or the user's request names another generation.",
+        "Follow the Markdown links below to read canonical page content without cloning the repository.",
+    ]
+    seen: set[str] = set()
+    for section, paths in navigation_sections():
+        entries = [path for path in paths if path in pages and path not in seen]
+        if not entries:
+            continue
+        lines.extend(("", f"## {section}"))
+        for relative in entries:
+            seen.add(relative)
+            _path, metadata, _body = pages[relative]
+            title = display_title(metadata, family_names)
+            description = " ".join(str(metadata.get("description", "")).split())
+            url = f"{SITE_URL}{markdown_route_for(relative)}"
+            suffix = f": {description}" if description else ""
+            lines.append(f"- [{title}]({url}){suffix}")
+    lines.extend(
+        (
+            "",
+            "## Instructions for language model agents",
+            "",
+            "- Prefer the model and framework identifiers shown in the selected page's examples.",
+            "- Check capability pages before assuming that every Gemma model accepts the same modalities.",
+            "- Use the Run section for engine-specific commands and Fine-tune for training workflows.",
+            "",
+        )
+    )
+    return "\n".join(lines)
+
+
+def render_raw(relative: str, metadata: dict, body: str) -> str:
+    environment = Environment(undefined=StrictUndefined, autoescape=False, keep_trailing_newline=True)
+    context = dict(metadata)
+    context["modeldata"] = model_data()
+    rendered = environment.from_string(body).render(**context).strip() + "\n"
+    if not H1.search(rendered):
+        rendered = f"# {metadata['title']}\n\n{rendered}"
+    return rendered
+
+
+def raw_targets(site_dir: Path) -> Iterable[tuple[Path, str]]:
+    for relative, (_path, metadata, body) in source_pages().items():
+        content = render_raw(relative, metadata, body)
+        source_shape = site_dir / relative
+        yield source_shape, content
+        clean_route = markdown_route_for(relative).lstrip("/")
+        clean_target = site_dir / clean_route
+        if clean_target != source_shape:
+            yield clean_target, content
+
+
+def generated_outputs() -> tuple[tuple[Path, str], ...]:
+    return ((LLMS_TARGET, render_llms()), (METADATA_TARGET, render_metadata()))
+
+
+def write_generated() -> None:
+    for path, content in generated_outputs():
+        path.write_text(content, encoding="utf-8")
+        print(f"wrote {path.relative_to(REPO_ROOT)}")
+
+
+def check_generated() -> int:
+    stale = []
+    for path, expected in generated_outputs():
+        if not path.exists() or path.read_text(encoding="utf-8") != expected:
+            stale.append(path)
+    for path in stale:
+        print(f"stale: {path.relative_to(REPO_ROOT)}")
+    return 1 if stale else 0
+
+
+def publish(site_dir: Path) -> None:
+    resolved = site_dir.resolve()
+    if not resolved.is_relative_to(REPO_ROOT) or resolved == REPO_ROOT:
+        raise ValueError("site directory must be a child of the repository")
+    for path, content in raw_targets(resolved):
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(content, encoding="utf-8")
+    print(f"published raw Markdown to {resolved.relative_to(REPO_ROOT)}")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--check", action="store_true", help="verify committed generated files")
+    parser.add_argument("--publish", type=Path, help="write raw Markdown into a built site")
+    args = parser.parse_args()
+    if args.check:
+        return check_generated()
+    write_generated()
+    if args.publish:
+        publish(args.publish)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/build_docs.py b/scripts/build_docs.py
new file mode 100644
index 0000000..232a3bb
--- /dev/null
+++ b/scripts/build_docs.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+"""Build the Zensical site and add its agent-facing raw Markdown routes."""
+
+from __future__ import annotations
+
+import subprocess
+import sys
+from pathlib import Path
+
+import agent_docs
+
+
+def main() -> int:
+    agent_docs.write_generated()
+    command = [sys.executable, "-m", "zensical", "build", *sys.argv[1:]]
+    result = subprocess.run(command, cwd=agent_docs.REPO_ROOT, check=False)
+    if result.returncode:
+        return result.returncode
+    agent_docs.publish(agent_docs.REPO_ROOT / "site")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/frontmatter_schema.py b/scripts/frontmatter_schema.py
index 67ded69..68c3267 100644
--- a/scripts/frontmatter_schema.py
+++ b/scripts/frontmatter_schema.py
@@ -8,8 +8,8 @@
   AUTHORED   Facts only a person knows -- what the page is about, which models it applies
              to, what you should read first. Hand-written, validated here.
 
-  COMPUTED   Facts derivable from the file itself -- token count, code languages, last
-             edit date. Hand-writing these guarantees they go stale, so authoring one is
+  COMPUTED   Facts derivable from the file itself -- token count and last edit date.
+             Hand-writing these guarantees they go stale, so authoring one is
              an *error*. The build hook in PLAN.md Step 3.2 fills them in.
 
 Vocabularies are read from the repository rather than hardcoded, so adding a model
@@ -48,7 +48,7 @@
 
 # Reserved for the Step 3.2 build hook. Authoring one is an error: a hand-written token
 # count is wrong the moment the page is edited.
-COMPUTED_KEYS = frozenset({"estimated_tokens", "code_languages", "last_updated"})
+COMPUTED_KEYS = frozenset({"estimated_tokens", "last_updated"})
 
 URL = re.compile(r"^https?://\S+$")
 SLUG = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$")
@@ -71,6 +71,7 @@ class Field:
     Field("frameworks", False, "list", "Engines/libraries the page shows code for."),
     Field("prerequisites", False, "list", "Pages to read first, as docs-relative paths."),
     Field("source_url", False, "str", "Upstream page this was ported from."),
+    Field("colab", False, "str", "Generated notebook URL for the page context strip."),
 )
 
 REQUIRED = tuple(f.name for f in SCHEMA if f.required)
@@ -246,4 +247,8 @@ def validate(frontmatter: dict, *, page_paths: set[str] | None = None) -> list[P
     if source_url is not None and not URL.match(str(source_url)):
         problems.append(Problem("source_url", f"source_url must be an http(s) URL: {source_url}"))
 
+    colab = frontmatter.get("colab")
+    if colab is not None and not URL.match(str(colab)):
+        problems.append(Problem("colab", f"colab must be an http(s) URL: {colab}"))
+
     return problems
diff --git a/scripts/generate_model_names.py b/scripts/generate_model_names.py
index 014550e..7c6d493 100644
--- a/scripts/generate_model_names.py
+++ b/scripts/generate_model_names.py
@@ -36,6 +36,7 @@
   Verified by: uv run python scripts/lint_docs.py --check generated
 
   `gemma_model_names` maps every family, size and framework id to its display name.
+  `gemma_framework_short_names` maps tools with a compact UI label to that label.
   `gemma_model_urls` maps a family id to its canonical local documentation page.
   `gemma_framework_urls` maps a tool id to its project home.
 
@@ -81,6 +82,11 @@ def render() -> str:
         for entry in frameworks["frameworks"]
         if entry.get("url")
     ]
+    framework_short_names = [
+        (str(entry["id"]), f'"{entry["short_name"]}"')
+        for entry in frameworks["frameworks"]
+        if entry.get("short_name")
+    ]
 
     return (
         HEADER
@@ -89,6 +95,8 @@ def render() -> str:
         + _map("gemma_model_urls", model_urls)
         + "\n"
         + _map("gemma_framework_urls", framework_urls)
+        + "\n"
+        + _map("gemma_framework_short_names", framework_short_names)
     )
 
 
diff --git a/scripts/lint_docs.py b/scripts/lint_docs.py
index 85705ea..286e752 100644
--- a/scripts/lint_docs.py
+++ b/scripts/lint_docs.py
@@ -694,20 +694,24 @@ def check_generated(ctx: Context) -> Iterable[Finding]:
     generated from data/models.yml into a template partial. That partial is the one place in
     the repo where model data could silently go stale, hence this check.
     """
+    import agent_docs
     import generate_model_names
+    import notebooks
 
     outputs = (
         (generate_model_names.TARGET, generate_model_names.render()),
         (generate_model_names.JS_TARGET, generate_model_names.render_js()),
+        *agent_docs.generated_outputs(),
+        *notebooks.generated_outputs(),
     )
     for target, expected in outputs:
         rel = target.relative_to(REPO_ROOT).as_posix()
         if not target.exists():
-            yield Finding("generated", rel, 0, "missing -- run scripts/generate_model_names.py")
+            yield Finding("generated", rel, 0, "missing -- rerun the corresponding generator")
         elif target.read_text(encoding="utf-8") != expected:
             yield Finding(
                 "generated", rel, 0,
-                "stale vs data/models.yml -- run scripts/generate_model_names.py",
+                "stale vs its canonical source -- rerun the corresponding generator",
             )
 
 
diff --git a/scripts/notebooks.py b/scripts/notebooks.py
new file mode 100644
index 0000000..25a48f4
--- /dev/null
+++ b/scripts/notebooks.py
@@ -0,0 +1,188 @@
+#!/usr/bin/env python3
+"""Export explicitly marked Zensical Markdown code blocks to a Jupyter notebook.
+
+This is intentionally a narrow exporter. Markdown remains canonical; only fences carrying the
+`.notebook-cell` class become executable cells. Site-only regions can be wrapped in
+`<!-- notebook:skip-start -->` and `<!-- notebook:skip-end -->` comments.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+from pathlib import Path
+
+import yaml
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+DEFAULT_SOURCE = REPO_ROOT / "docs" / "get-started" / "capabilities" / "thinking.md"
+DEFAULT_TARGET = REPO_ROOT / "docs" / "notebooks" / "thinking.ipynb"
+NOTEBOOKS = {
+    DEFAULT_SOURCE: DEFAULT_TARGET,
+    REPO_ROOT / "docs" / "get-started" / "capabilities" / "text.md":
+        REPO_ROOT / "docs" / "notebooks" / "text.ipynb",
+    REPO_ROOT / "docs" / "get-started" / "capabilities" / "function-calling.md":
+        REPO_ROOT / "docs" / "notebooks" / "function-calling.ipynb",
+    REPO_ROOT / "docs" / "get-started" / "capabilities" / "image.md":
+        REPO_ROOT / "docs" / "notebooks" / "image.ipynb",
+    REPO_ROOT / "docs" / "get-started" / "capabilities" / "video.md":
+        REPO_ROOT / "docs" / "notebooks" / "video.ipynb",
+    REPO_ROOT / "docs" / "get-started" / "capabilities" / "audio.md":
+        REPO_ROOT / "docs" / "notebooks" / "audio.ipynb",
+    REPO_ROOT / "docs" / "tuning" / "qlora.md":
+        REPO_ROOT / "docs" / "notebooks" / "qlora.ipynb",
+    REPO_ROOT / "docs" / "tuning" / "full-finetune.md":
+        REPO_ROOT / "docs" / "notebooks" / "full-finetune.ipynb",
+    REPO_ROOT / "docs" / "tuning" / "vision-qlora.md":
+        REPO_ROOT / "docs" / "notebooks" / "vision-qlora.ipynb",
+}
+
+FENCE_OPEN = re.compile(r"^(?P<fence>`{3,})(?P<language>[\w+-]*)(?P<attributes>.*)$")
+SKIP_REGION = re.compile(
+    r"<!--\s*notebook:skip-start\s*-->.*?<!--\s*notebook:skip-end\s*-->",
+    re.DOTALL,
+)
+ADMONITION = re.compile(
+    r"^!!!\s+(?P<label>[A-Za-z]+)(?:\s+\"[^\"]*\")?\n(?P<body>(?:[ \t]+.*(?:\n|$))*)",
+    re.MULTILINE,
+)
+
+
+def split_frontmatter(text: str) -> tuple[dict, str]:
+    if not text.startswith("---"):
+        return {}, text
+    lines = text.splitlines()
+    for index in range(1, len(lines)):
+        if lines[index].strip() == "---":
+            metadata = yaml.safe_load("\n".join(lines[1:index])) or {}
+            return metadata, "\n".join(lines[index + 1 :]).lstrip("\n")
+    raise ValueError("unterminated frontmatter")
+
+
+def notebook_markdown(text: str) -> str:
+    text = SKIP_REGION.sub("", text)
+
+    def replace_admonition(match: re.Match) -> str:
+        body = "\n".join(line.strip() for line in match.group("body").splitlines()).strip()
+        quoted = "\n> ".join(body.splitlines())
+        return f"> **{match.group('label').title()}:** {quoted}\n"
+
+    return ADMONITION.sub(replace_admonition, text).strip()
+
+
+def markdown_cell(source: str) -> dict | None:
+    source = notebook_markdown(source)
+    if not source:
+        return None
+    return {"cell_type": "markdown", "metadata": {}, "source": source.splitlines(keepends=True)}
+
+
+def code_cell(language: str, source: str) -> dict:
+    source = source.rstrip() + "\n"
+    if language in {"bash", "sh", "shell"}:
+        source = "%%bash\n" + source
+    return {
+        "cell_type": "code",
+        "execution_count": None,
+        "metadata": {"language": language or "python"},
+        "outputs": [],
+        "source": source.splitlines(keepends=True),
+    }
+
+
+def export_cells(body: str) -> list[dict]:
+    body = SKIP_REGION.sub("", body)
+    lines = body.splitlines(keepends=True)
+    cells: list[dict] = []
+    prose: list[str] = []
+    index = 0
+    while index < len(lines):
+        match = FENCE_OPEN.match(lines[index].rstrip("\r\n"))
+        if not match:
+            prose.append(lines[index])
+            index += 1
+            continue
+
+        closing = index + 1
+        while closing < len(lines) and lines[closing].strip() != match.group("fence"):
+            closing += 1
+        if closing == len(lines):
+            raise ValueError(f"unclosed code fence at line {index + 1}")
+
+        attributes = match.group("attributes")
+        block = "".join(lines[index + 1 : closing])
+        if ".notebook-cell" in attributes:
+            pending = markdown_cell("".join(prose))
+            if pending:
+                cells.append(pending)
+            prose = []
+            cells.append(code_cell(match.group("language"), block))
+        elif ".output" not in attributes:
+            prose.extend(lines[index : closing + 1])
+        index = closing + 1
+
+    pending = markdown_cell("".join(prose))
+    if pending:
+        cells.append(pending)
+    return cells
+
+
+def render(source: Path = DEFAULT_SOURCE) -> str:
+    metadata, body = split_frontmatter(source.read_text(encoding="utf-8"))
+    try:
+        canonical_source = source.resolve().relative_to(REPO_ROOT).as_posix()
+    except ValueError:
+        canonical_source = source.name
+    notebook = {
+        "cells": export_cells(body),
+        "metadata": {
+            "colab": {"name": f"{source.stem}.ipynb", "provenance": []},
+            "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
+            "language_info": {"name": "python", "version": "3"},
+            "gemma_docs": {
+                "canonical_source": canonical_source,
+                "title": metadata.get("title", source.stem),
+            },
+        },
+        "nbformat": 4,
+        "nbformat_minor": 5,
+    }
+    return json.dumps(notebook, indent=1, ensure_ascii=False) + "\n"
+
+
+def generated_outputs() -> tuple[tuple[Path, str], ...]:
+    """Return every registered output and its canonical rendered contents."""
+    return tuple((target, render(source)) for source, target in NOTEBOOKS.items())
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--source", type=Path)
+    parser.add_argument("--output", type=Path)
+    parser.add_argument("--check", action="store_true")
+    args = parser.parse_args()
+    if bool(args.source) != bool(args.output):
+        parser.error("--source and --output must be provided together")
+    outputs = (
+        ((args.output, render(args.source)),)
+        if args.source
+        else generated_outputs()
+    )
+    if args.check:
+        stale = [
+            target for target, expected in outputs
+            if not target.exists() or target.read_text(encoding="utf-8") != expected
+        ]
+        for target in stale:
+            print(f"stale: {target}")
+        return bool(stale)
+    for target, expected in outputs:
+        target.parent.mkdir(parents=True, exist_ok=True)
+        target.write_text(expected, encoding="utf-8")
+        print(f"wrote {target.relative_to(REPO_ROOT)}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/test_agent_docs.py b/scripts/test_agent_docs.py
new file mode 100644
index 0000000..af75536
--- /dev/null
+++ b/scripts/test_agent_docs.py
@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+import sys
+import unittest
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+import agent_docs
+
+
+class AgentDocsTests(unittest.TestCase):
+    def test_routes_match_rendered_pages(self):
+        self.assertEqual(agent_docs.markdown_route_for("index.md"), "/index.md")
+        self.assertEqual(agent_docs.markdown_route_for("models/index.md"), "/models.md")
+        self.assertEqual(
+            agent_docs.markdown_route_for("get-started/quickstart.md"),
+            "/get-started/quickstart.md",
+        )
+
+    def test_llms_uses_absolute_markdown_links(self):
+        rendered = agent_docs.render_llms()
+        self.assertTrue(rendered.startswith("# Gemma Documentation\n"))
+        self.assertIn("https://gemma.ai/get-started/quickstart.md", rendered)
+        self.assertNotIn("llms-full", rendered)
+
+    def test_raw_markdown_renders_model_macros(self):
+        pages = agent_docs.source_pages()
+        _path, metadata, body = pages["get-started/quickstart.md"]
+        rendered = agent_docs.render_raw("get-started/quickstart.md", metadata, body)
+        self.assertIn("| **E2B** |", rendered)
+        self.assertNotIn("{% for", rendered)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/scripts/test_notebooks.py b/scripts/test_notebooks.py
new file mode 100644
index 0000000..0b36ebc
--- /dev/null
+++ b/scripts/test_notebooks.py
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+import json
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+import notebooks
+
+
+class NotebookExportTests(unittest.TestCase):
+    def test_only_marked_cells_are_executable(self):
+        body = """# Guide
+
+Intro.
+
+```python { .notebook-cell .model-swap }
+print("run")
+```
+
+```python
+print("display only")
+```
+
+``` { .no-copy .output }
+run
+```
+"""
+        cells = notebooks.export_cells(body)
+        code = [cell for cell in cells if cell["cell_type"] == "code"]
+        self.assertEqual(len(code), 1)
+        self.assertEqual("".join(code[0]["source"]), 'print("run")\n')
+        markdown = "".join("".join(cell["source"]) for cell in cells if cell["cell_type"] == "markdown")
+        self.assertIn('print("display only")', markdown)
+        self.assertNotIn("\nrun\n", markdown)
+
+    def test_site_only_region_is_removed(self):
+        body = "Before\n<!-- notebook:skip-start -->\nSite UI\n<!-- notebook:skip-end -->\nAfter"
+        cells = notebooks.export_cells(body)
+        rendered = "".join(cells[0]["source"])
+        self.assertIn("Before", rendered)
+        self.assertIn("After", rendered)
+        self.assertNotIn("Site UI", rendered)
+
+    def test_nested_fence_remains_markdown(self):
+        body = """````
+Sample output:
+```json
+{"answer": 42}
+```
+````
+
+```python { .notebook-cell }
+print(42)
+```
+"""
+        cells = notebooks.export_cells(body)
+        self.assertEqual([cell["cell_type"] for cell in cells], ["markdown", "code"])
+        self.assertIn("```json", "".join(cells[0]["source"]))
+
+    def test_render_is_a_version_four_notebook(self):
+        with tempfile.TemporaryDirectory() as directory:
+            source = Path(directory) / "guide.md"
+            source.write_text(
+                "---\ntitle: Guide\n---\n# Guide\n\n```python { .notebook-cell }\nprint(1)\n```\n",
+                encoding="utf-8",
+            )
+            payload = json.loads(notebooks.render(source))
+            self.assertEqual(payload["nbformat"], 4)
+            self.assertEqual(payload["cells"][1]["cell_type"], "code")
+
+    def test_registered_notebooks_have_unique_outputs_and_code_cells(self):
+        self.assertEqual(len(notebooks.NOTEBOOKS), 9)
+        self.assertEqual(len(set(notebooks.NOTEBOOKS.values())), len(notebooks.NOTEBOOKS))
+        for source, target in notebooks.NOTEBOOKS.items():
+            with self.subTest(target=target.name):
+                payload = json.loads(notebooks.render(source))
+                self.assertTrue(
+                    any(cell["cell_type"] == "code" for cell in payload["cells"]),
+                    f"{source} exports no executable cells",
+                )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/zensical.toml b/zensical.toml
index f2e64db..b1787b1 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -19,6 +19,7 @@ extra_css = [
   "stylesheets/nav.css",         # header, tabs, sidebar, table of contents
   "stylesheets/content.css",     # tables, admonitions, code, tabs
   "stylesheets/components.css",  # cards, badges, figures, steps, embeds
+  "stylesheets/embeds.css",      # external interactive demos
   "stylesheets/page-context.css",# the applicability bar above every H1
   "stylesheets/tokens.css",      # chat-token badges
   "stylesheets/home.css",        # landing page: chrome, backdrop, hero
@@ -179,6 +180,7 @@ nav = [
   ]},
 
   { "Learn" = [
+    { "Overview" = "learn/index.md" },
     { "Visual guides" = [
       { "Gemma 4" = "tutorials/visual-guides/gemma-4.md" },
       { "Gemma 4 12B" = "tutorials/visual-guides/gemma-4-12b.md" },
@@ -202,7 +204,7 @@ nav = [
       "tools/model-selector.md",
     ]},
     { "Apps & Spaces" = [
-      "demos/concurrent.md",
+      { "Vision token budget" = "demos/vision-token-budget.md" },
     ]},
   ]},
 

From 7749ea318f7085d6e235f02743cca56739b35bf0 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 10 Aug 2026 23:09:20 +0200
Subject: [PATCH 18/20] Cleanup

---
 .github/workflows/deploy.yml                  |   2 +-
 .github/workflows/lint.yml                    |   2 +-
 PLAN-ARCHIVE.md                               | 954 ------------------
 PLAN.md                                       | 787 +++------------
 README.md                                     |   2 +-
 data/models.yml                               |   2 +-
 docs/contributing/components.md               |   2 +-
 docs/page-metadata.json                       |  22 +-
 scripts/backfill_frontmatter.py               | 373 -------
 scripts/frontmatter_schema.py                 |   4 +-
 scripts/{ => importers}/check_fidelity.py     |   4 +-
 scripts/{ => importers}/convert_notebook.py   |   2 +-
 scripts/{ => importers}/import_newsletter.py  |   2 +-
 scripts/{ => importers}/link_map.toml         |   2 +-
 scripts/{ => importers}/port_docs.py          |  10 +-
 scripts/{ => importers}/rewrite_links.py      |  12 +-
 scripts/{ => importers}/sources.toml          |   4 +-
 scripts/{ => importers}/vendor_images.py      |   6 +-
 .../fixtures/admonitions.expected.md          |   0
 .../fixtures/admonitions.md.txt               |   0
 .../fixtures/base64_image.expected.md         |   0
 .../fixtures/base64_image.md.txt              |   0
 .../fixtures/list_continuation.expected.md    |   0
 .../fixtures/list_continuation.md.txt         |   0
 .../fixtures/prompt_template.expected.md      |   0
 .../fixtures/prompt_template.md.txt           |   0
 .../fixtures/split_code_block.expected.md     |   0
 .../fixtures/split_code_block.md.txt          |   0
 {scripts => tests}/test_agent_docs.py         |   2 +-
 {scripts => tests}/test_convert_notebook.py   |   8 +-
 {scripts => tests}/test_notebooks.py          |   2 +-
 31 files changed, 208 insertions(+), 1996 deletions(-)
 delete mode 100644 PLAN-ARCHIVE.md
 delete mode 100644 scripts/backfill_frontmatter.py
 rename scripts/{ => importers}/check_fidelity.py (98%)
 rename scripts/{ => importers}/convert_notebook.py (99%)
 rename scripts/{ => importers}/import_newsletter.py (98%)
 rename scripts/{ => importers}/link_map.toml (99%)
 rename scripts/{ => importers}/port_docs.py (92%)
 rename scripts/{ => importers}/rewrite_links.py (95%)
 rename scripts/{ => importers}/sources.toml (98%)
 rename scripts/{ => importers}/vendor_images.py (95%)
 rename {scripts => tests}/fixtures/admonitions.expected.md (100%)
 rename {scripts => tests}/fixtures/admonitions.md.txt (100%)
 rename {scripts => tests}/fixtures/base64_image.expected.md (100%)
 rename {scripts => tests}/fixtures/base64_image.md.txt (100%)
 rename {scripts => tests}/fixtures/list_continuation.expected.md (100%)
 rename {scripts => tests}/fixtures/list_continuation.md.txt (100%)
 rename {scripts => tests}/fixtures/prompt_template.expected.md (100%)
 rename {scripts => tests}/fixtures/prompt_template.md.txt (100%)
 rename {scripts => tests}/fixtures/split_code_block.expected.md (100%)
 rename {scripts => tests}/fixtures/split_code_block.md.txt (100%)
 rename {scripts => tests}/test_agent_docs.py (94%)
 rename {scripts => tests}/test_convert_notebook.py (95%)
 rename {scripts => tests}/test_notebooks.py (97%)

diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 3e72079..1e053aa 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -31,7 +31,7 @@ jobs:
         uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6
 
       - name: Script tests
-        run: uv run --frozen python -m unittest discover -s scripts -p "test_*.py"
+        run: uv run --frozen python -m unittest discover -s tests -p "test_*.py"
 
       - name: Lint docs
         run: uv run --frozen python scripts/lint_docs.py --baseline
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 827ca55..70f7668 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -30,7 +30,7 @@ jobs:
         run: pip install pyyaml==6.0.3 jinja2==3.1.6
 
       - name: Script tests
-        run: python -m unittest discover -s scripts -p "test_*.py"
+        run: python -m unittest discover -s tests -p "test_*.py"
 
       - name: Lint documentation
         run: python scripts/lint_docs.py --baseline
diff --git a/PLAN-ARCHIVE.md b/PLAN-ARCHIVE.md
deleted file mode 100644
index cb10933..0000000
--- a/PLAN-ARCHIVE.md
+++ /dev/null
@@ -1,954 +0,0 @@
-# Gemma Docs — Archive
-
-Completed work and the running log of lessons learned. Split out of `PLAN.md` on 2026-08-05,
-which had grown to 1,395 lines with five steps recorded twice — once as a plan and once as an
-outcome.
-
-**`PLAN.md` is the working document.** This file is the record: what was done, what it cost,
-and what surprised us. Nothing here is outstanding work.
-
----
-
-## Starting point
-
-This table is the migration's starting baseline, retained for historical context. Current
-status is recorded in the completed phase sections and the maintenance baseline below.
-
-| Metric | Value |
-|---|---|
-| Markdown files | 80 |
-| Nav entries | 76 |
-| Real ported content (>1.5 KB) | 44 |
-| One-line stubs (<250 B) | 34 |
-| Pages with frontmatter | 9 / 80 |
-| Links still pointing at `ai.google.dev/gemma/docs` | 122 |
-| Images hotlinked to `ai.google.dev/static` | 18 (0 vendored) |
-| Orphan pages (exist, not in nav) | 4 |
-| Orphan stylesheet (exists, not in `extra_css`) | 1 (`custom.css`) |
-
-Known defects:
-
-- Malformed code fences from the notebook converter — e.g. `docs/models/gemma-4/index.md:117`
-  wraps prose in a ```` ```python ```` fence mid-sentence.
-- 7 prompt-template blocks tagged `python` that are plain text.
-- ~16 unconverted devsite `Note:` / `Key Point:` / `Caution:` callouts.
-- `docs/models/paligemma/fine-tuning.md` is 435 KB (base64 images inlined by the converter).
-- `docs/community/gemmaverse.md` still ships `example.com` placeholder cards.
-- Modality data disagrees across `quickstart.md`, `models/index.md:14`, and `models/gemma-4/index.md:45`.
-
-
----
-
-## Phase 0 — Foundation
-
-*Nothing here is glamorous. Everything else compounds on top of it, and CI checks stop the
-debt from coming back.*
-
-### Step 0.1 — Repo hygiene and CI guardrails ✅
-- [x] `scripts/lint_docs.py` with 9 checks: `links`, `images`, `internal`, `stubs`,
-      `frontmatter`, `orphans`, `fences`, `admonitions`, `size`.
-- [x] Baseline mechanism (`.lintbaseline.json`) so CI fails only on *new* violations while the
-      backlog is burned down. **Shrinking the baseline to zero is the definition of done for Phase 0.**
-- [x] `.github/workflows/lint.yml` — runs on push, PR, and dispatch. Stdlib-only, no install step.
-- [x] Deleted `docs/stylesheets/custom.css` (dead: not in `extra_css`, fully superseded by
-      `layout.css`, and would have conflicted on `.md-grid` max-width and sidebar width).
-- **Verify:** `uv run python scripts/lint_docs.py --baseline` → exit 0.
-      `uv run python scripts/lint_docs.py` → full 300-finding backlog.
-- **Done when:** ✅ regression test confirmed — adding an upstream link exits 1, reverting exits 0.
-
-**Baseline at 2026-08-03 (the Phase 0 burn-down list):**
-
-| Check | Count | Notes |
-|---|---|---|
-| `frontmatter` | 81 | 71 pages with no block + missing keys elsewhere |
-| `links` | 122 | 5 of these are inside code blocks |
-| `images` | 52 | 18 hotlinked, 34 inline base64 (all in `paligemma/fine-tuning.md`) |
-| `stubs` | 33 | |
-| `fences` | 7 | prompt templates tagged `python` |
-| `orphans` | 4 | |
-| `size` | 1 | `paligemma/fine-tuning.md` at 435 KB |
-| `internal` | 0 | all relative links resolve |
-| `admonitions` | 0 | **converter already handles these correctly** — 36 converted |
-| **Total** | **300** | |
-
-Useful commands:
-
-```bash
-uv run python scripts/lint_docs.py --list
-```
-
-```bash
-uv run python scripts/lint_docs.py --check links,images
-```
-
-```bash
-uv run python scripts/lint_docs.py --write-baseline
-```
-
-### Step 0.2 — Fix the notebook converter ✅
-- [x] **Wrapped list continuations** no longer eaten as code. Devsite wraps long bullets with an
-      indented continuation line; the 4-space rule fenced it as `python`. The guard meant to
-      prevent this (`lines[i-1].strip().startswith("    ")`) could never fire — `.strip()`
-      removes the very indentation it tests. Continuations are now rejoined onto their bullet,
-      which restores the original sentence exactly (markdown renders `foo\n  bar` in a list as
-      `foo bar`, so nothing about the content changes).
-- [x] **Language detection** gained a `text` fallback: `bash` → `python` → `text`. Detection now
-      uses Python *syntax* signals, deliberately not bare English words like "for"/"with"/"from",
-      which is why prompt templates were being classed as code.
-- [x] **Indented bullets no longer split a block.** A bullet may not *start* a block (that would
-      eat indented lists), but once a block is underway an indented bullet is block content —
-      previously one ASR prompt became a fence plus orphaned bullets.
-- [x] **Base64 extraction** via `--assets-dir` / `--assets-url`. Anchored on `![...](...)` so a
-      data URI inside real code is left alone.
-- [x] Golden tests: `scripts/test_convert_notebook.py` + 4 fixtures in `scripts/fixtures/`,
-      each a minimised reproduction of a real defect. 11 tests, wired into CI.
-- **Verify:** `uv run python scripts/test_convert_notebook.py` → 11 passing.
-- **Done when:** ✅ re-converting `core.md.txt` produces the QAT bullets intact with **zero**
-      python fences; `audio.md.txt` yields 8 `python`, 2 `bash`, 2 `text` fences, both ASR
-      prompts correctly `text`.
-
-**Content-fidelity check** (word multiset, source vs. output):
-
-| Source | Dropped | Verdict |
-|---|---|---|
-| `core.md.txt` | `NOTE` ×1 | the `> [!NOTE]` → `!!! Note` conversion. Additions are cookbook-card markup. |
-| `audio.md.txt` | 111, all badge/metadata | `colab_logo`, `notebook-site-button.png`, `View`, `Run`, `<br>` — the intended stripping. |
-
-**Zero prose lost in either.** Regenerate goldens with `--update`, then review the diff —
-those files are the specification.
-
-```bash
-uv run python scripts/test_convert_notebook.py
-```
-
-### Step 0.3 — Re-port Models, targeted repair for Get Started
-
-Two different strategies, because the two sections have different histories:
-
-- **Models is a straight port.** No hand-authoring, so re-converting is safe and correct. ✅ Done.
-- **Get Started is hand-edited and reviewed.** `quickstart.md` has a hand-authored model table,
-  the `npx skills add` admonition, and `hide: toc` frontmatter — none of it in upstream's
-  `get_started.md.txt`. Re-converting would destroy that. Repair defects in place.
-
-#### 0.3a — Porting infrastructure ✅
-- [x] `scripts/sources.toml` — manifest mapping each ported page to its upstream source.
-      **A page absent from the manifest must never be re-converted.** Also the origin of the
-      `source_url` frontmatter in Step 3.1.
-- [x] `scripts/check_fidelity.py` — word-multiset comparison proving a conversion changed markup
-      and nothing else. Compares *source minus what the converter is designed to drop* vs. output,
-      reusing the converter's own `is_badge_line` rather than a parallel word blocklist, so the
-      two can't drift apart.
-- [x] `scripts/port_docs.py` — batch porter. **Fidelity-gated: a page that drops prose is never
-      written.** Caches sources under `.sources/` (gitignored).
-- [x] New converter fix: **consecutive blank lines no longer split a code block.** Notebook
-      exports routinely leave two blank lines mid-cell; the old lookahead checked only one line
-      ahead, so one function became two fences (found at `paligemma/inference.md`, `binary_mask`).
-- [x] New linter check: two same-language fences separated only by blank lines — the signature of
-      a split block. Found **9 across 7 files**, well beyond the one reported.
-
-#### 0.3b — Models re-ported ✅
-- [x] All **37** manifest pages re-ported. Fidelity: 37 faithful, 0 with dropped prose.
-      Re-running is idempotent (all "unchanged").
-- [x] `paligemma/fine-tuning.md`: **435 KB → 51 KB**, 34 images extracted to `docs/assets/paligemma/`.
-- [x] `models/releases.md`: 58-byte stub → full port (+4.2 KB).
-- **Verify:** `uv run python scripts/port_docs.py --filter models/ --dry-run` → 37 ok, 0 failed.
-
-#### 0.3c — Get Started targeted repair ✅
-
-Diffing each page against a fresh conversion first confirmed both carry real hand-edits, so
-repair-in-place was the correct call:
-
-| Page | Hand-edits preserved |
-|---|---|
-| `capabilities/image.md` | title recased, `## Summary` edit, `<!-- TODO: Port over -->` block |
-| `capabilities/audio.md` | hand-added `# Audio Understanding`, `## Summary` edit, TODO block |
-
-- [x] `capabilities/audio.md` — two ASR prompt templates `python` → `text`.
-- [x] `capabilities/audio.md:81` — unconverted `> > [!NOTE]` callout → `!!! Note`.
-- [x] `capabilities/image.md` — three split blocks rejoined. Diff is exactly 6 lines
-      (the fence pairs); no content touched.
-- [x] New linter coverage: GitHub-style `> [!NOTE]` callouts. The `admonitions` check only
-      knew about devsite `Note:` prefixes, so this defect was invisible.
-- **Verify:** `uv run python scripts/lint_docs.py --check fences,admonitions` → zero.
-- **Done when:** ✅ zero fence and admonition findings site-wide; remaining diff vs. a fresh
-      conversion is hand-edits only.
-
-**Technique worth reusing:** diffing a page against a fresh conversion cleanly separates
-*bugs* from *hand-edits*. Do this before touching any page whose history is unclear — it is the
-manual form of the `--divergence` check and it is what proved re-porting would have been wrong here.
-
-### Phase 0 progress: 300 → 285
-
-| Check | Start | Now | |
-|---|---|---|---|
-| `fences` | 7 | **0** | ✅ cleared |
-| `size` | 1 | **0** | ✅ cleared |
-| `admonitions` | 0 | **0** | ✅ 1 found and fixed |
-| `internal` | 0 | **0** | ✅ |
-| `images` | 52 | **18** | base64 done; 18 hotlinked remain → Step 0.4 |
-| `stubs` | 33 | **32** | → Steps 2.2–2.4 |
-| `orphans` | 4 | **4** | → Step 2.1 |
-| `frontmatter` | 81 | **81** | → Step 3.1 |
-| `links` | 122 | **150** | +28 from `releases.md` gaining content → Step 0.5 |
-
-**Backlog after the Models port — 300 → 290:**
-
-| Check | Was | Now | |
-|---|---|---|---|
-| `images` | 52 | **18** | 34 base64 extracted |
-| `size` | 1 | **0** | cleared |
-| `fences` | 7 | **5** | model-card fences fixed; 5 left, all in Get Started |
-| `stubs` | 33 | **32** | `releases.md` now real content |
-| `links` | 122 | **150** | +28 from `releases.md` gaining content — Step 0.5 rewrites them |
-| `frontmatter` / `orphans` / `internal` / `admonitions` | 81 / 4 / 0 / 0 | unchanged | later steps |
-
-```bash
-uv run python scripts/port_docs.py --filter models/ --dry-run
-```
-
-```bash
-uv run python scripts/check_fidelity.py --manifest --cache-dir .sources
-```
-
-### Step 0.4 — Vendor all images ✅
-- [x] `scripts/vendor_images.py` — downloads remote images into `docs/assets/<section>/` and
-      rewrites the references. Section = the page's parent directory, matching the layout
-      `port_docs.py` already uses. Original filenames are kept so any asset traces back to its
-      upstream URL. A failed download leaves the reference untouched (a broken local path is
-      worse than a working remote one) and the script is idempotent, so re-running retries.
-- [x] All **18** hotlinked images vendored, 18/18 succeeded. `docs/assets/` is now 8.2 MB.
-- [x] Fidelity checker taught to ignore image *targets* on both sides — only alt text is prose.
-      Without this, vendoring read as 6 pages losing prose and would have **blocked those pages
-      from ever being re-ported**, since `port_docs.py` is fidelity-gated.
-- [ ] ~~Dark-mode variants~~ — **deferred to Phase 1** with the rest of the design system.
-- **Verify:** ✅ zero `ai.google.dev/static` references in the built `site/`; all 214 local
-      `<img>` sources resolve on disk.
-- **Done when:** ✅ the site renders fully with no network access to Google.
-
-!!! warning "Ordering: port, then vendor"
-    Conversion restores the upstream image URLs, so **re-porting a page undoes its vendoring**
-    (confirmed: re-porting `models/gemma-4/model-card.md` would revert one image, +21 bytes).
-    Always run `vendor_images.py` after `port_docs.py`. `port_docs.py` prints this reminder, and
-    since the `images` check now sits at zero, a missed run fails CI.
-
-```bash
-uv run python scripts/vendor_images.py --dry-run
-```
-
-### Step 0.4b — CI hardening (zizmor / org policy) ✅
-
-The org runs [zizmor](https://docs.zizmor.sh/) on workflows and **fails on any Medium/High
-finding**, not just the mandatory-check list. Both workflows now pass clean.
-
-- [x] All 6 action references pinned to commit SHAs in `lint.yml` and `deploy.yml`
-      (`unpinned-uses`, High — this is what broke the build).
-- [x] `lint.yml` given an explicit `permissions: contents: read` block.
-- [x] `deploy.yml` permissions narrowed: `contents: read` at workflow level, with
-      `pages: write` / `id-token: write` scoped to the `deploy` job alone. At workflow level they
-      were also being handed to the `build` job, which only needs to read (`excessive-permissions`, High).
-- [x] `persist-credentials: false` on both checkouts (`artipacked`).
-- **Verify:** `uvx zizmor --persona=regular .github/workflows/` → "No findings to report."
-
-!!! note "Updating a pin"
-    Pins carry a `# v4` comment so the intended version stays readable. To move one:
-
-    ```bash
-    gh api repos/actions/checkout/git/ref/tags/v4 --jq '.object.sha'
-    ```
-
-    For annotated tags, dereference: `gh api repos/<repo>/git/tags/<sha> --jq '.object.sha'`.
-
-### Step 0.5 — Rewrite internal links ✅
-
-- [x] `scripts/link_map.toml` — reviewable data, not logic buried in a script. Three sections:
-      `[ported]` (target exists), `[planned]` (eventual path; tracked as known-pending),
-      `[delink]` (drop the link, keep the text).
-- [x] `scripts/rewrite_links.py` — applies the map, computing each relative path per page.
-      An unmapped target is **reported and left alone**; silently dropping a link is worse than
-      leaving one to fix.
-- [x] **144 prose links rewritten**: 122 ported, 20 planned, 2 delinked. Zero unmapped.
-- [x] **CodeGemma delinked** (decision: not being ported). Both references in `releases.md`
-      keep their text — "Release of CodeGemma v1.1." still reads correctly.
-- [x] `--fix-anchors` pass: 12 fragments repaired. devsite used `#model_data`, Zensical emits
-      `#model-data`; 8 were the mechanical underscore→hyphen case, 4 needed the explicit
-      `[anchors]` override table because the heading text itself changed.
-- **Verify:** `uv run python scripts/lint_docs.py --check links` → zero. Both passes idempotent.
-- **Done when:** ✅ zero outbound links to the site we're replacing.
-
-**Left external on purpose** — 5 URLs inside code blocks: four are sample data the reader
-fetches (`img_url = "https://.../cat.png"`) and one is a BibTeX citation. Rewriting those would
-break copy-paste and misattribute the citation. `check_links` now skips code blocks for this reason.
-
-**5 anchors remain unresolved**, none of them actionable now:
-
-| Anchor | Why |
-|---|---|
-| `huggingface.md#audio`, `#vision` | target is a stub; resolves in Step 2.2 |
-| `quickstart.md#models-list` | section removed during hand-editing |
-| `paligemma/index.md#categories` | **broken upstream too** — no such section on ai.google.dev |
-| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
-
-The last two were dead links on the original site; the port reproduced them faithfully.
-Inventing a target would be an editorial guess, so they are left alone pending a content decision.
-
-```bash
-uv run python scripts/rewrite_links.py --report
-```
-
-### Phase 0 progress: 300 → 137
-
-| Check | Start | Now | |
-|---|---|---|---|
-| `links` | 122 | **0** | ✅ cleared |
-| `images` | 52 | **0** | ✅ cleared |
-| `fences` | 7 | **0** | ✅ cleared |
-| `size` | 1 | **0** | ✅ cleared |
-| `admonitions` | 0 | **0** | ✅ |
-| `internal` | 0 | 20 | known-pending: links to planned pages |
-| `stubs` | 33 | 32 | → Steps 2.2–2.4 |
-| `orphans` | 4 | 4 | → Step 2.1 |
-| `frontmatter` | 81 | 81 | → Step 3.1 |
-
-
-- [ ] Map all 122 `ai.google.dev/gemma/docs/*` links to local paths.
-- [ ] For pages not yet ported, point at the eventual local path and let the linter flag it as
-      a known-pending target (don't leave live external links).
-- **Verify:** `uv run python scripts/lint_docs.py --check links`
-- **Done when:** zero outbound links to the site we're replacing.
-
-### Step 0.5b — Port the pages Step 0.5 pointed at ✅
-
-Step 0.5 left 19 build warnings by design (links aimed at eventual paths). Every one of those
-targets turned out to exist upstream, so porting them was cheaper than leaving the warnings —
-and it advanced Step 2.4 at the same time.
-
-- [x] **10 pages ported**, all fidelity-clean:
-
-| Page | Source | Size |
-|---|---|---|
-| `get-started/setup.md` | `/setup` | 2.5 KB |
-| `get-started/prompt-structure.md` | `/core/prompt-structure` | 2.2 KB |
-| `get-started/capabilities/mtp.md` | `/mtp/overview` | 2.8 KB |
-| `deployment/index.md` | `/run` | 10.7 KB |
-| `deployment/huggingface.md` | `/core/huggingface_inference` | 11.9 KB (**replaced a stub**) |
-| `deployment/mobile-actions.md` | `/mobile-actions` | 4.2 KB |
-| `tuning/spoken-language.md` | `/spoken-language/task-specific-tuning` | 22.9 KB |
-| `learn/business-email-assistant.md` | `/business-email-assistant` | 20.5 KB |
-| `learn/personal-code-assistant.md` | `/personal-code-assistant` | 22.6 KB |
-| `models/gemma-aps/index.md` | `/gemma-aps` | 1.8 KB |
-
-- [x] **The MTP gap is closed.** It was the flagship Gemma 4 feature with zero documentation.
-- [x] Link map grew to 75 `[ported]` entries; 23 more anchor overrides applied.
-- [x] Fixed a mis-targeted mapping: `/core/prompt-system-instructions` (no such upstream page)
-      was sent to `paligemma/prompting.md`, but the referring context is core Gemma
-      `start_of_turn`/`end_of_turn` tags, so it now points at `get-started/prompt-structure.md`.
-- **Verify:** build warnings **19 → 5**; all four pipeline stages idempotent.
-
-**5 warnings remain, all deliberate:**
-
-| Warning | Status |
-|---|---|
-| `deployment/keras.md` ×2 | planned page — Step 2.1 decides whether "Run > Local" gains Keras |
-| `quickstart.md#models-list` | section removed during your hand-editing |
-| `paligemma/index.md#categories` | **broken upstream too** |
-| `shieldgemma/model-card-v2.md#harm-types` | **broken upstream too** |
-
-The last three are left alone on purpose: picking a target is an editorial decision, and the
-dangling anchor is a useful signal that a section is genuinely missing. **Needs a content call.**
-
-!!! warning "The 9 new pages are not in the nav"
-    `orphans` rose 4 → 13. That is expected — Step 2.1 places them. Do not "fix" it by
-    deleting pages.
-
-### Step 0.6 — Model capability matrix as single source of truth ✅
-
-- [x] `data/models.yml` — every Gemma 4 size with parameters, architecture, modalities in/out,
-      context window, memory at each quantization, and target platform.
-- [x] Wired into `zensical.toml` via the macros plugin's `include_yaml`, exposed as `models.*`.
-- [x] `quickstart.md` and `models/index.md` tables **generated** from it.
-- [x] New `modeldata` lint check guarding the one table that can't be generated (below).
-- **Verify:** ✅ changed `context: 128K` → `TESTVALUE`, rebuilt, both tables updated; reverted,
-      both reverted. Drift check verified by injecting a wrong figure — caught it.
-
-**The modality contradiction is resolved.** Confirmed by Maarten (2026-08-03):
-
-| Model | Input | Note |
-|---|---|---|
-| E2B, E4B, 12B | Text, Image, Audio, Video | full multimodal |
-| 26B A4B, 31B | Text, Image, Video | **no audio**; video is processed as individual frames |
-
-This matches `models/gemma-4/index.md:45` ("Audio... featured natively on the E2B, E4B and 12B
-models"). The stale source was the `quickstart.md` table, which omitted video entirely and is
-now generated.
-
-!!! warning "One table is deliberately *not* generated"
-    `models/gemma-4/index.md` is a straight port (`scripts/sources.toml`), so a template there
-    would be wiped by the next `port_docs.py` run. Its memory figures were copied into
-    `data/models.yml` instead, and the new **`modeldata`** check compares the two — so if
-    upstream revises a number, it surfaces as a finding rather than a silent contradiction.
-
-```bash
-uv run python scripts/lint_docs.py --check modeldata
-```
-
----
-
-
-### Step 0.7 — Rendering fixes from review
-
-All fixed **in the converter**, not per-page, so re-porting preserves them.
-
-- [x] **Devsite card grids → plain bullets.** Upstream's own `.md.txt` export mangles its card
-      HTML into an empty bullet + `####` heading + paragraph. Now collapsed to
-      `- **Title** — description`. Affected shieldgemma, recurrentgemma, datagemma,
-      gemma-scope, paligemma, gemma-aps (14 cards).
-- [x] **Videos embedded, not linked.** A standalone `[Video](youtube…)` becomes a responsive
-      16:9 `youtube-nocookie` iframe (lazy-loaded). 8 pages.
-- [x] **Model-card header centred.** Upstream wraps the banner/links/license block in
-      `<p align="center">`, which the export drops. Restored as `.model-card-header`.
-- [x] **Code inside list items stays inside.** Fences were emitted at column 0, breaking out of
-      the list so everything after started a new one — `embeddinggemma/inference.md`'s RAG
-      section. Blocks are now dedented by their own indent and the fence indented to its item.
-- [x] **Nested lists tightened.** A blank line between a bullet and its own sub-list made
-      Markdown render it "loose", wrapping items in `<p>` inconsistently.
-- [x] **`> NOTE:` blockquotes → admonitions.** 4 in functiongemma pages. The `**Note:**` lines in
-      `capabilities/thinking.md` are inside model-output blocks and were correctly left verbatim.
-- [x] **Gemma 1/2/3 model cards reference themselves.** Upstream's "Model Page: Gemma → /core"
-      resolved to Gemma 4 here, so the Gemma 2 card advertised Gemma 4 as its model page.
-      New `[page_overrides]` in `link_map.toml` handles per-page mappings.
-- [x] `docs/stylesheets/components.css` added (video embeds, model-card header) — the first
-      piece of the Phase 1 modular stylesheet layout.
-
-**The fidelity gate caught these transformations and blocked 8 pages** until
-`check_fidelity.py` was taught that video URLs and empty `[](url)` duplicates are markup, not
-prose. Working as designed — a transformation must be declared before it can land.
-
-### Phase 0 outcome — 300 → 136
-
-| Check | Start | Now | |
-|---|---|---|---|
-| `links` | 122 | **0** | ✅ |
-| `images` | 52 | **0** | ✅ |
-| `fences` | 7 | **0** | ✅ |
-| `size` | 1 | **0** | ✅ |
-| `admonitions` | 0 | **0** | ✅ |
-| `modeldata` | — | **0** | ✅ new check |
-| `internal` | 0 | 2 | `deployment/keras.md` — Step 2.1 decision |
-| `stubs` | 33 | 31 | → Steps 2.2–2.4 |
-| `orphans` | 4 | 13 | → Step 2.1 (9 newly ported pages await nav) |
-| `frontmatter` | 81 | 90 | → Step 3.1 |
-
-**Everything remaining is scheduled work, not defects.** The three checks that still have
-findings are all waiting on Phase 2 (nav, content) or Phase 3 (frontmatter schema).
-
----
-
-## Phase 1 — Design system
-
-### Step 1.1 — Token layer ✅
-
-- [x] `docs/stylesheets/gemma.css` — palette, spacing (1–7), radius (sm/md/lg/pill),
-      shadow (sm/md/lg), chat-token categories, all with dark-scheme variants.
-- [x] Material's `--md-*` primary/accent/link variables bound to Gemma tokens, so component
-      sheets never name a brand colour.
-- [x] `content.css`, `tokens.css`, and the landing-page stylesheet consume tokens.
-- [x] `cookbook.css` folded into `components.css` as a generalised card grid
-      (`.cookbook-*` kept as aliases — converter output and existing pages emit them).
-- [x] New **`css`** lint check: raw hex only in `gemma.css`, every sheet registered in
-      `extra_css`, none over 250 lines.
-- **Verify:** `uv run python scripts/lint_docs.py --check css` → zero.
-- **Done when:** ✅ zero hex outside `gemma.css`; every sheet under 250 lines.
-
-**Contrast drove a real decision.** Measured against the palette:
-
-| Pairing | Ratio | |
-|---|---|---|
-| `#3186FF` on white | 3.51:1 | **fails AA for body text** |
-| `#346BF1` on white | 4.65:1 | passes |
-| `#4FA0FF` on ink | 5.98:1 | passes |
-
-So `--gemma-blue` is a **fill** colour — buttons, bars, icons, step markers — and never body
-text. Text uses `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in
-dark. The header uses `blue-deep` for the same reason.
-
-**Stylesheet layout** (8 files, each under 250 lines):
-
-| File | Owns |
-|---|---|
-| `gemma.css` | Tokens. **Only file with raw hex.** |
-| `layout.css` | Page grid, sidebars, typography scale |
-| `nav.css` | Header, tabs, sidebars, table of contents |
-| `content.css` | Tables, admonitions, code, content tabs |
-| `components.css` | Cards, badges, figures, steps, embeds |
-| `tokens.css` | Chat-token badges |
-| `home.css` | Landing page: chrome, backdrop, hero |
-| `motion.css` | Transitions + reduced-motion opt-out |
-
-The homepage is deliberately one concern again: obsolete styles for an earlier DOM were removed,
-while the live template styles moved into the registered, linted `home.css`.
-
-### Step 1.2 — Component library ✅
-
-- [x] Card grid, model badges (`--accent`, `--muted`), framed figures with light/dark image
-      swap, numbered steps with a connecting rail, video embeds, model-card header.
-- [x] `docs/contributing/components.md` — every component with a **live example** plus the
-      markup to copy, the colour-token table with contrast guidance, and the stylesheet map.
-      Added to nav under *Contributing*.
-- **Verify:** the components page renders every component in both themes.
-
-`.badge--muted` exists for the Step 4.2 switcher: a capability the selected model lacks is
-struck through rather than hidden, so the reader learns the limitation instead of it silently
-vanishing.
-
-### Step 1.3 — Motion ✅
-
-- [x] `motion.css` owns shared transitions and the site-wide reduced-motion opt-out. The isolated
-      homepage text animation checks the same preference before starting.
-- [x] Blanket reduced-motion rule; anything added later inherits it.
-- [x] Focus rings never animate — keyboard users need them immediately.
-- **Verify:** enable *Reduce motion* in OS settings, reload, confirm hover effects are instant.
-
-Motion is feedback (something is interactive, something changed), never decoration.
-
-
-### Step 1.4 — Stripe-inspired refinement
-
-Measured against `docs.stripe.com` rather than eyeballed. What actually differed:
-
-| | Stripe | Was | Now |
-|---|---|---|---|
-| Content width | 800px | **1600px** | **880px** |
-| Body size | 14px | 15.5px | **14px** |
-| Line height | 1.43 | 1.7 | 1.65 |
-| Neutral ramp | 13 steps | none | **12 steps**, blue-tinted |
-| Brand use | accent only | **full header** | accent only |
-
-- [x] **Layout reshaped.** Sidebar sits flush left; the *content* is centred in the space that
-      remains, rather than stretching to fill it. That is the single biggest change — at 1600px
-      lines ran to ~180 characters.
-- [x] Pages that hide the table of contents can use the freed rail without changing the regular
-      article's left edge (the 7-column Gemma 4 table fits at the regular 880px measure).
-- [x] **Header is a near-white tint** (`--gemma-n50`), not a slab of brand blue. Blue appears
-      once: the active tab, as colour + a 2px rule.
-- [x] New `nav.css` (chrome) and `content.css` (tables, admonitions, collapsibles, inline code,
-      content tabs) — Stripe-inspired, tinted surfaces over heavy borders.
-
-**Four bugs found only because the result was measured in a browser:**
-
-1. **Body text was 18px, not 14px.** Material ships `html { font-size: 125% }`, so every `rem`
-   in the file was 25% larger than it read. Anchored to 100%.
-2. **The active-tab rule never matched.** Zensical marks the active tab on the `<li>`
-   (`md-tabs__item--active`), not the `<a>`.
-3. **Material's palette outranks a plain scheme selector.** It scopes at
-   `[data-md-color-scheme=X][data-md-color-primary=Y]` (0,2,0); ours was (0,1,0) and lost
-   silently regardless of load order. Dark-mode links and inline code stayed light.
-4. **Admonition tints outranked the base rule** at `.md-typeset .admonition.note` (0,3,1).
-
-**Layout shape** (verified at 1366px and 2000px):
-
-```
-sidebar 17rem │  ←auto→  │ content 56rem │ toc 17rem │  ←auto→
-   flush left            └── centred as a pair, TOC follows the text ──┘
-```
-
-The sidebar stays flush left at every width. Content and the table of contents are centred
-*as a pair* via auto margins on the pair's outer edges — capping and centring all three
-columns instead pushes the sidebar inward, which a docs rail should not do.
-
-**`hide: toc` pages get their own rule.** Material still renders the sidebar element, just
-`hidden`, so the pair's `margin-right: auto` lands on a zero-width box and every pixel of
-slack piles up on the left, shunting the column right. Those pages centre on their own and
-widen to 73rem — hiding the table of contents is usually a request for a wider page.
-
-Padding lives on `.md-content`, not `.md-content__inner`: the breadcrumb is a **sibling** of
-`__inner`, so padding there left the two misaligned by 77px. Breadcrumb, `h1` and body text
-now share one left edge.
-
-**Contrast verified in the live page, both schemes — everything passes AA:**
-
-| | Light | Dark |
-|---|---|---|
-| Body text | 21.0 | 15.4 |
-| Links | 4.65 | 7.27 |
-| Active tab | 4.92 | — |
-| Table header / muted | 6.38 | 8.39 |
-
-Two tokens exist purely to hold that line: `--gemma-text-muted` uses `n600` (n500 measured
-4.46 on the tint), and `--gemma-accent-on-tint` (`#2F63E6`) carries small brand-coloured text
-on tinted surfaces, where plain `blue-deep` measures 4.41.
-
-
----
-
-## Phase 2 — Navigation & content
-
-### Step 2.1 — Restructure nav ✅
-
-- [x] Nav rewritten to the 8-tab target: **Home · Get started · Models · Run · Fine-tune ·
-      Learn · Demos · Ecosystem**.
-- [x] **All 13 orphans resolved.** `orphans` 13 → **0**.
-- [x] Cookbook folded into *Learn*; the old Tools tab folded into *Demos* as
-      "Interactive tools"; `contributing/components.md` placed under *Ecosystem → Contribute*.
-- **Verify:** `uv run python scripts/lint_docs.py --check orphans` → zero.
-- **Done when:** ✅ zero orphans, zero broken nav entries, 8 top-level tabs confirmed in the
-      rendered header.
-
-How each orphan was resolved:
-
-| Orphan | Resolution |
-|---|---|
-| `get-started/index.md` | **Deleted** — a strict subset of `quickstart.md` with zero inbound links |
-| `capabilities/vision.md` | **Ported** from upstream (was a stub); now the parent of Image/Video |
-| `deployment/keras.md` | **Ported** — clears the last 2 `internal` findings and fills *Run → Local* |
-| `models/index.md`, `tools/index.md` | placed as section landing pages |
-| the 9 pages from Step 0.5b | placed in Run / Fine-tune / Learn / Models |
-
-**Directory question settled: `models/` stays flat.** Grouping is nav-only, exactly so a
-model's URL survives reclassification — when the next generation ships and Gemma 4 moves to
-*Previous generations*, no URL changes. The rationale is recorded in the nav file itself.
-
-**One more converter fix:** consecutive `{ .no-copy .output }` blocks separated only by blank
-lines are now merged. Upstream splits a single stream of terminal output across several fences
-(Keras `.summary()` prints a title then a table), which rendered as two separate result boxes.
-Only *output* blocks are merged — two adjacent code blocks may legitimately be separate cells.
-
-### Phase 2 progress: 136 → 120
-
-| Check | Now | |
-|---|---|---|
-| `links` `images` `internal` `fences` `admonitions` `size` `modeldata` `css` `orphans` | **0** | ✅ all clear |
-| `stubs` | 30 | → Steps 2.2–2.4 |
-| `frontmatter` | 90 | → Step 3.1 |
-
-**Only two checks still have findings**, and both are scheduled work. Build warnings: 5 → **3**.
-
-
-
-### Step 2.2 — Local engine pages (the differentiator)
-
-These are *written*, not ported — upstream's versions are three paragraphs each. One step per
-engine so they can be tested and handed over independently.
-
-**These are quick references, not manuals** (decided 2026-08-03). Users want working code
-fast, and a short page is a page that stays correct. Roughly 100 lines each.
-
-Template — in this order, nothing else:
-
-1. **Install** — the one command per platform
-2. **Run** — the one command that works
-3. **Model tags / repositories** — a table, every Gemma 4 size
-4. **Multimodal** — only where it differs from plain text, with a note when a size lacks a modality
-5. **Server** — the OpenAI-compatible snippet
-
-Deliberately **not** included: context-length deep dives, troubleshooting tables, importing
-your own fine-tunes, "next steps" link lists. Each is either engine documentation that we would
-have to keep in sync, or a link the sidebar already provides. A single `!!! tip` is allowed
-where a default will actively surprise someone (Ollama's 4K context, for instance).
-
-- [x] 2.2a Ollama ✅ *(written)*
-- [x] 2.2b llama.cpp ✅ *(written)*
-- [x] 2.2c LM Studio ✅ *(written)*
-- [x] 2.2d MLX ✅ *(written)*
-- [x] 2.2e Transformers ✅ *(ported — upstream `core/huggingface_inference` is already complete)*
-- [x] 2.2f vLLM ✅ *(written)*
-- [x] 2.2g SGLang ✅ *(written)*
-- [x] 2.2h LiteRT / mobile / WebGPU ✅ *(ported — upstream pages are already concise link hubs)*
-- [x] 2.2i Google Cloud ✅ *(ported)*
-- [x] Also filled: Keras, LangChain, Quantization & QAT. **The Run section has zero stubs.**
-
-**Nav labels are short, page titles are not** (2026-08-03). The sidebar shows "Hugging Face
-Transformers", "LangChain", "Keras"; the page H1s stay as ported ("Run Gemma with Hugging Face
-Transformers"). Labels live in `zensical.toml`, so no ported prose is touched.
-
-**`mobile-actions.md` moved `deployment/` → `tuning/`.** Its H1 is "Fine-tune FunctionGemma 270M
-for Mobile Actions" — a fine-tuning guide that was sitting under Run.
-
-
-- **Verify per page:** every command runs as written on a clean machine.
-
-**Sourcing rule (2026-08-03).** Every command, flag and model identifier on these pages is
-taken from a primary source — the engine's own repository docs, its published model registry,
-or the upstream Gemma docs. Nothing is written from memory. Where a claim can't be verified it
-is omitted rather than guessed.
-
-Verified sources used so far:
-
-| Page | Sources |
-|---|---|
-| Ollama | `ollama.com/library/gemma4/tags` (tags, sizes, context) · `ollama/ollama` `docs/api`, `docs/api/openai-compatibility.mdx`, `docs/capabilities/vision.mdx`, `docs/context-length.mdx` · upstream `/integrations/ollama` |
-| llama.cpp | `ggml-org/llama.cpp` `docs/install.md`, `docs/multimodal.md` · `huggingface.co/ggml-org` (5 GGUF repos) · `huggingface.co/google` (5 QAT GGUF repos) · upstream `/integrations/llamacpp` |
-
-**Throughput numbers are deliberately absent.** Measured tok/s can't be sourced from
-documentation, and inventing it would be worse than omitting it. The pages give the verifiable
-performance levers instead — GPU-offload checks, context defaults, quantization choice. Add real
-figures when someone can measure them.
-
-**Unresolved discrepancy — Ollama download sizes.** `ollama.com/library/gemma4/tags` states
-E2B 7.2 GB, E4B 9.6 GB, **12B 7.6 GB**, 26B 18 GB, 31B 20 GB. That makes E4B *larger* than 12B,
-which contradicts the validated Q4_0 column in `models/gemma-4/index.md` (E4B 4.5 GB,
-12B 6.7 GB — monotonic, as expected). Verified verbatim from the tags page twice.
-
-Most likely the default tag for each size uses a different quantization, but that is a guess and
-**no explanation appears on the page**. An earlier draft rationalised it as a Per-Layer
-Embeddings effect; that was wrong and was removed. If someone can confirm the per-tag quant
-levels, add them to the table — otherwise leave the figures unexplained.
-
-Two corrections to upstream found while writing:
-
-- Upstream's Ollama page lists "four sizes: E2B, E4B, 26B and 31B" and **omits 12B**, which the
-  Ollama library does publish.
-- **Ollama exposes Gemma 4 as text + image only** — no audio — while llama.cpp supports image,
-  audio *and* video. Neither page upstream mentions this, and it's the kind of thing that costs
-  someone an afternoon.
-
-
-- **Done when:** the page is genuinely more useful than the upstream equivalent.
-
-### Step 2.3 — Fill remaining stubs (partly done)
-
-*The 7 stubs still open are tracked in `PLAN.md` under "Current status".*
-
-- [x] **Fine-tune section ported** — all six stubs filled from upstream: `tune`,
-      `huggingface_text_finetune_qlora`, `huggingface_text_full_finetune`,
-      `huggingface_vision_finetune_qlora`, `lora_tuning`, `distributed_tuning`.
-      These are validated tutorials, so porting beats authoring.
-
-**`slugify` bug fixed.** `rewrite_links.py` stripped parenthesised text, so
-"Google Kubernetes Engine (GKE)" produced `google-kubernetes-engine` while Python-Markdown
-emits `google-kubernetes-engine-gke`. Anchors computed from it silently never matched. It now
-mirrors the real algorithm: reduce markdown to visible text, then drop non-word characters —
-parentheses contribute their contents, they are not removed.
-
-
-
-### Step 2.4 — Port remaining upstream pages (partly done)
-
-- [x] ~~CodeGemma~~ — **not being ported** (decision: 2026-08-03). Existing references are
-      delinked, keeping the text. Do not add it back without a new decision.
-- [x] `run`, `core/keras_inference`, `core/huggingface_inference`, `core/lora_tuning`,
-      `core/prompt-structure`, `gemma-aps`, `spoken-language/task-specific-tuning`,
-      `conversions/hf-to-mediapipe-task`, `mobile-actions` — ported and frozen.
-- [x] ~~`setup`~~ — **deleted, not ported** (decision: 2026-08-04). The legacy setup page was
-      superseded by `get-started/quickstart.md`.
-- [x] Application tutorials → Learn: `business-email-assistant`, `personal-code-assistant`.
-
-*The 6 pages still awaiting a keep-or-drop decision are listed in `PLAN.md`.*
-
----
-
-## Maintenance baseline (2026-08-04)
-
-- [x] Zensical is pinned exactly (`zensical==0.0.51`) and deployment runs with
-      `uv run --frozen`, so a package release cannot silently restyle production.
-- [x] Deployment gates the build on converter tests and baseline-aware docs lint.
-- [x] Stylesheets consolidated from 11 to 8: the active homepage CSS moved out of its template,
-      three obsolete homepage sheets were removed, and the one-rule `code.css` was folded into
-      `content.css`.
-- [x] CSS lint rejects template `<style>` blocks in addition to enforcing registration, token
-      use, and the 250-line limit.
-- [x] Internal-link lint validates `#fragments`, including same-page links. The three real broken
-      fragments from the August build were repaired. The larger missing-page list in the captured
-      output was transient incremental-build state; a clean build is the gate.
-- [x] Replaced the placeholder README and removed the unused `main.py` scaffold.
-- [x] Import, conversion, and image-vendoring scripts remain in place by decision. Demo/vendor
-      images are untouched until original PNGs replace them.
-
-
----
-
-## Notes
-
-*Agents: record surprises, deviations, and anything that changes the plan here.*
-
-- Zensical is pinned exactly via `pyproject.toml` and `uv.lock` (`zensical==0.0.51`). Some Material features listed in
-  `zensical.toml` may not be implemented yet — verify before relying on them.
-
-**2026-08-03 (Step 0.1)**
-
-- ⚠️ **Two workflows both deploy to GitHub Pages on push to `main`** — `docs.yml` and
-  `deploy.yml`. They will race; `deploy.yml` has a `concurrency: pages` group, `docs.yml`
-  doesn't. They also install zensical differently (`docs.yml` → plain PyPI, `deploy.yml` → uv +
-  `pypi.zensical.org`). **Needs a decision from Maarten** — `deploy.yml` looks like the intended
-  one. Left both in place rather than deleting unilaterally.
-- **Correction to the original audit:** devsite callouts are *not* an issue. The converter
-  already converts them (36 admonitions present, 0 unconverted). The earlier "~16" figure came
-  from a combined grep pattern that also matched `.md.txt` and `colab.research` strings.
-  Step 0.2's admonition work is therefore already done — verify with a fixture rather than rewrite.
-- `check_images` and `check_internal` scan **outside code blocks only**. Markdown-like syntax
-  inside snippets is code, not a reference (e.g. an f-string `{render_inline(image, resize=(64,64))}`
-  in `paligemma/fine-tuning.md:515` was a false positive).
-- `check_links` scans **everywhere**, including code blocks — 5 upstream links live inside
-  snippets. Some may be intentional (e.g. downloading a sample asset); review case by case in Step 0.5.
-- `Finding.key()` deliberately excludes line numbers so edits above a known finding don't
-  resurrect it as "new".
-- The 34 base64 images are all in `paligemma/fine-tuning.md`. Fixing that one file clears the
-  `size` check and 34 of the 52 `images` findings at once — good first target for Step 0.3/0.4.
-
-**2026-08-03 (Step 0.2)**
-
-- ⚠️ **Pages have been hand-edited since their port.** There is currently no way to tell a
-  straight port from a hand-edited page automatically — hence `--divergence` in Step 0.3 and
-  `source_url` frontmatter in Step 3.1. Until one of those lands, **assume any page may contain
-  hand-work and never re-convert in bulk.**
-- **Get Started is reviewed and approved by Maarten.** Repair defects in place; do not re-convert.
-- Only 6 files have converter-class defects. The other 294 backlog findings are links, images,
-  frontmatter, and nav — all outside the converter's remit.
-- The converter's `text` fallback matters more than it looks: an early version of the Python
-  detector matched `\bfor\b` inside "for formatting the answer", which is exactly how prose got
-  classified as code. Keep the detector on *syntax* signals, never bare English words.
-
-**2026-08-03 (Step 0.3)**
-
-- **`scripts/sources.toml` is the authority on what may be re-converted.** Absent from the
-  manifest = hand-authored or hand-edited = never re-convert. Add a page to it only after
-  confirming it is a straight port.
-- Fidelity checking must compare against *source minus designed drops*, not raw source.
-  A naive comparison flags every Colab/Kaggle badge as lost prose — that produced 10 false
-  failures before `strip_designed_drops` reused the converter's own `is_badge_line`.
-- `check_fidelity.py` and `port_docs.py` **share `cache_name()`**. They must: keying the source
-  cache on the bare filename collided every `index.md` across all model directories onto one
-  entry, making the two tools disagree (0 failures vs 26).
-- `--dry-run` extracts images to a temp directory. Without that it wrote 34 files to
-  `docs/assets/` while claiming to change nothing.
-- Two blank lines mid-cell is common in notebook exports — worth checking for whenever a new
-  converter bug is suspected.
-
-**2026-08-03 (Step 0.4)**
-
-- **Conversion and vendoring fight each other.** Converting restores upstream image URLs;
-  vendoring replaces them with local paths. Always port first, then vendor. See *Pipeline order*.
-- Any check that gates writes (as `port_docs.py` does on fidelity) must be **precise about what
-  is allowed to change**, or it locks pages out of future edits. Vendoring tripped the fidelity
-  check on 6 pages until image targets were excluded from the word comparison.
-- `docs/assets/` is 8.2 MB, most of it `capabilities/` (7 notebook-output PNGs, 5.5 MB). If repo
-  size becomes a concern, those are the candidates for recompression — not the diagrams.
-- The site's `404.html` uses root-absolute `/assets/` paths. That is correct (a 404 can be served
-  from any depth) and will look like a broken link to any checker resolving paths relative to the
-  file. Don't "fix" it.
-- ⚠️ **Any new workflow must be zizmor-clean before it is pushed.** Pin every action to a SHA,
-  set an explicit `permissions:` block, and scope write permissions to the job that needs them.
-  Check locally with `uvx zizmor --persona=regular .github/workflows/` — it catches everything
-  CI does and takes seconds. The org fails on Medium/High, which is broader than the published
-  mandatory-check list.
-- **Why zizmor appeared only now, on this branch.** The scanner is "GitHub Actions Scan"
-  (`.github/workflows/github_actions_scan.yml`), injected org-wide by `google-gemma` — it is not
-  in this repo. It triggers on **`pull_request_target`**, and every commit before this branch went
-  *straight to main via push*, so it had never run. `foundation` is the repo's first pull request.
-  The pre-existing `docs.yml` and `deploy.yml` did not pass the policy; they were never scanned.
-- **The scan is diff-scoped.** It flagged only `lint.yml` while `deploy.yml` sat unmodified on the
-  same branch with four unpinned actions. Pre-existing workflow debt therefore stays invisible
-  until a PR touches that file — worth auditing a workflow *before* you need to edit it under
-  time pressure.
-
-**2026-08-05 (Step 3.1)**
-
-- **`title` is not always metadata.** Zensical synthesises an H1 from `title` when a page has
-  no heading of its own, and leaves an existing heading alone. Five pages rely on this. A
-  first attempt assumed the title *always* replaced the H1 and dropped ten disambiguated
-  model-card titles to avoid a content change that could not actually happen — checking the
-  built HTML rather than reasoning about the renderer settled it in one command.
-- **A frontmatter key is either authored or computed, never both.** Backfilling
-  `estimated_tokens` by hand would have guaranteed drift the moment anyone edited a page.
-  The linter now rejects hand-written computed keys outright, so Step 3.2 can land without
-  first cleaning up after Step 3.1.
-- **The linter now needs PyYAML.** Frontmatter carries lists (`models`, `frameworks`), and the
-  old line-splitting parser read `[gemma-4]` as the string `"[gemma-4]"`. `lint.yml` installs
-  it explicitly — the workflow's "stdlib-only, nothing to install" assumption no longer holds.
-- **The backfill script found two of its own bugs through the schema it writes.** `models:
-  [releases.md]` (a filename read as a model id) and a nav *section* label leaking onto a page
-  title. Both were caught by the vocabulary check within seconds of the first run. Validating
-  against a repository-derived vocabulary rather than a free-text field is what made that work.
-- **Derived descriptions need a human pass.** 76 were extracted from body text; 24 were then
-  rewritten by hand. The extractor flags its own weak output — truncated, duplicated, or
-  fallback — which is what made the review tractable. Two upstream patterns caused most of it:
-  pages that open with the boilerplate "Gemma is a family of…" paragraph before saying what
-  the page covers, and `#` comments inside bash fences being read as headings.
-- **Provenance gap closed.** All 63 pages in `sources.toml` now carry `source_url`, including
-  the `get-started/**` pages ported before the manifest existed.
-- ⚠️ **`zensical build` reports 28 "page does not exist" warnings that are false positives.**
-  Every one checked resolves correctly in the built HTML (`../models/gemma-4/index.md` →
-  `../../models/gemma-4/`). They are pre-existing and unrelated to frontmatter, but they make
-  the build output useless as a signal — worth chasing before anyone trusts it as a gate.
-
-**2026-08-05 (schema revision + plan split)**
-
-- **A wildcard and an omission cannot mean different things if they look the same.** `models:
-  [all]` was being written on twelve pages that are not about a model at all — community, tools,
-  the cookbook. "Applies to every model" and "is about no model" need different renderings in the
-  page context header, so the wildcard was removed: absence now means the second, and the first
-  has no page that needs it.
-- **A bare size id is ambiguous and always will be.** `12b` says nothing about which generation;
-  Gemma 3 and Gemma 4 both ship one. Values are now `family` or `family/size`, with the
-  separator `/` rather than `:` — `[gemma-4:12b]` risks being read as a mapping in YAML flow
-  context, and the frontmatter is parsed by both our linter and Zensical.
-- **`ported_at` removed.** It was a second, staler copy of what git already records, and every
-  value in it came from `git log` in the first place. `source_url` alone answers the question the
-  key existed for: *what was this page ported from?*
-- **`data/frameworks.yml` added.** The framework vocabulary was a hardcoded set in the schema
-  module; it now sits beside `data/models.yml` with display names, categories and the docs page
-  for each. Both the linter and the Step 4.2 switcher need that metadata, and neither should read
-  it out of a Python literal.
-- **`PLAN.md` split at 1,395 lines.** Five completed steps were recorded twice — once as the plan
-  and once as the outcome — and Step 0.7 had drifted below *Open decisions*. Completed work and
-  the notes log moved to `PLAN-ARCHIVE.md`; `PLAN.md` is now 530 lines of what is still ahead.
-  The duplication was not accidental: each step was written as a plan, then a result section was
-  appended without removing the plan. Ticking a box in place would have avoided it.
-- **Tooling lifecycle recorded.** Roughly 2,450 of 3,877 script lines exist only to migrate
-  content off devsite, and all of them are gated on one unmade decision — whether anything else
-  gets ported from upstream. `sources.toml` in particular now duplicates `source_url` on every
-  page it lists.
-
-**2026-08-05 (page context strip)**
-
-- ⚠️ **A frontmatter key silently shadowed a macros variable.** Step 3.1 added `models:` to every
-  page; the macros plugin exposed `data/models.yml` as `models` too. Page metadata wins, so
-  `models.gemma4.sizes` resolved to the page's own `["gemma-4"]` list and both generated tables
-  rendered their Jinja source as literal text on screen. Nothing failed the build, nothing failed
-  the linter — it was only visible by looking at the page. The data variable is now `modeldata`.
-  Worth remembering that the frontmatter namespace and the macros namespace are the same
-  namespace.
-- **Measure the target design, don't recall it.** The first attempt at this strip was a row of
-  bordered chips above the H1. Loading docs.stripe.com and reading the box metrics showed the
-  space above the title is empty, and that their control row is flex/16px-gap/baseline with a
-  hairline rule under it. Two of those — the placement and the rule — were what made the first
-  version look unfinished.
-- **`getComputedStyle().font` returns an empty string** when the font was set via separate
-  properties, so a text-measuring ruler silently inherited the body font and sized the selector
-  to "1…" instead of "12B". Copy the individual font properties, and remember a border-box
-  element's declared width has to cover its own padding.
-- **A native select sizes to its widest option**, which strands a caret next to a short label.
-  Width is set from the selected option's measured text instead.
-- **Opt-in substitution earns its keep immediately.** On `deployment/ollama.md` the `pull`
-  command follows the selector while the tag table three lines below it — which must list every
-  tag — stays untouched. A blanket "rewrite every identifier" pass would have corrupted it.
-
-**2026-08-05 (page context strip, second pass)**
-
-- **The design error was conceptual, not visual.** The selector was driven by `models:`
-  frontmatter, which answers "what is this page about" — a fact. A selector answers "what do you
-  want the commands to target" — a preference. Conflating them produced a control that offered
-  exactly five Gemma 4 sizes on a page whose engine can run anything, and that claimed `E2B` on
-  Quick Start, a page covering all five. Deriving the options from the identifiers actually
-  present in the page's marked code blocks fixes both, and makes the control vanish where there
-  is nothing to switch.
-- **A taxonomy has to answer one question.** `data/frameworks.yml` had `local` / `serve`
-  (where it runs) next to `library` / `tuning` / `orchestration` (what it is), so `transformers`
-  and `llamacpp` — both libraries, both usually local — landed in unrelated groups. Replaced
-  with `url`. If a field's values can't be produced by asking a single question, it is two
-  fields or none.
-- **Stripe's separator is a 1px `<span role="separator">`** with `align-self: stretch` and a
-  background one step darker than the rule closing the row — not a `|` glyph, and not absent as
-  an earlier measurement pass concluded. It was missed because the DOM sweep only collected
-  elements that had text.
-- **Copying a reference design's *treatment* without matching its *structure* fails.** Stripe's
-  toolbar items are peer actions at one weight, so flat styling works. Ours are label→value
-  pairs; flat styling made "Frameworks" and "Sentence Transformers" indistinguishable. Hierarchy
-  had to come from spacing ratio and consistent label/value contrast, which Stripe's page does
-  not demonstrate because it never needs to.
-- ⚠️ **Do not test colour schemes by setting `data-md-color-scheme` from the console.** Anchor
-  colours do not re-resolve that way, which produced a phantom 2.06:1 contrast failure and a
-  long hunt through the cascade. Click the real palette toggle; the same measurement then read
-  13.36:1.
-- **Material's `.md-typeset a` is (0,2,0).** A bare component class for a link inside content is
-  (0,1,0) and loses, so the framework links rendered in link-blue and competed with the selector
-  for attention.
diff --git a/PLAN.md b/PLAN.md
index 002366e..c0a5e0e 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -1,676 +1,215 @@
-# Gemma Docs — Port & Re-imagination Plan
+# Gemma documentation roadmap
 
-Porting and re-imagining the Gemma documentation onto [Zensical](https://github.com/zensical/zensical).
-Source: <https://ai.google.dev/gemma/docs> (append `.md.txt` to any page for raw markdown).
+This repository ports and re-imagines the Gemma documentation with Zensical. The immediate goal
+is a polished proof of concept that is safe to merge and easy to continue maintaining.
 
-**Goal:** the best open-weights developer experience on the web. Not just accurate reference —
-an experience that makes people *want* to build with Gemma, welcomes newcomers, and gives
-partners a place to showcase their work.
+This file is a living roadmap, not a changelog. Completed implementation detail belongs in Git
+history; durable authoring instructions belong in `README.md` or `docs/contributing/`.
 
-**Gemma 4 is front and center.** All other models are documented, but the docs are
-implicitly about Gemma 4 unless stated otherwise.
+## Current state
 
-> Completed work, outcome tables, and the running log of lessons live in
-> [`PLAN-ARCHIVE.md`](PLAN-ARCHIVE.md). This file is only what is still ahead.
+- The Zensical design system, navigation, page-context header and global Gemma 4 selector work.
+- Agent-facing raw Markdown routes and a Stripe-style `llms.txt` are generated by the build.
+- Nine Markdown-first tutorials can be exported to Jupyter notebooks and opened in Colab.
+- One HF Space and one task-first Learn page demonstrate richer documentation experiences.
+- All 63 imported pages are frozen and hand-maintained.
+- The remaining lint baseline consists of six known content stubs.
 
----
+Deferred demos and interactive tools are not POC merge blockers.
 
-## Working agreement
+## Before merging the POC
 
-- **Bite-sized steps.** Each step is independently testable and roughly one session of work.
-- **Every step ends with a verification command** and a "done when" condition. Run it before
-  moving on.
-- **Maarten does all commits and pushes.** Agents stage nothing and push nothing — finish the
-  step, report what changed, and stop.
-- **Handover-friendly.** Each step is self-contained enough that a fresh agent can pick it up
-  from this file alone. Record anything surprising in the archive's *Notes* log.
-- **Foundation before fun.** Phases 0–2 must be clean before Phase 4 work starts. A WebGPU
-  playground next to "Content coming soon" pages reads as a prototype, not a product.
+Keep this list short. These tasks materially affect contributors or production; broader product
+ideas belong in the deferred roadmap.
 
-### Content fidelity — non-negotiable
+### Generated notebooks
 
-**Ported content must not change.** The original documentation has been validated for accuracy;
-that validation is only transferable if the words survive the port intact.
+- [ ] Publish notebooks from CI to a dedicated `generated-notebooks` branch.
+- [ ] Point every Colab URL at that branch.
+- [ ] Remove committed `.ipynb` files from `main` only after the published URLs work.
+- [ ] In pull requests, render notebooks into a temporary directory and validate their structure;
+      never require contributors to run or commit the generator for a Markdown edit.
+- [ ] Give `contents: write` only to the post-build publishing job. It must run only for successful
+      `main` builds, use SHA-pinned actions and remain zizmor-clean.
 
-- A port is a **format** migration, not an editorial one. Convert devsite syntax to Zensical
-  syntax, fix links and image paths, repair converter damage — and change nothing else.
-- Do **not** reword, tighten, restructure, "improve", modernize, or re-order ported prose.
-  Do not silently correct something that looks wrong: flag it and ask.
-- Code snippets are copied verbatim. Changing a flag or an argument invalidates the validation.
-- **New pages may remix.** Combining snippets from several source pages into a new page is fine
-  and encouraged — but the snippets themselves are still copied, not rewritten.
-- **Start with a straight port.** Port a page as-is first, confirm it renders well, and only then
-  consider whether it needs a companion page. Don't port and redesign in one motion.
-- Every ported page records `source_url` in frontmatter, so any page can be diffed against
-  upstream later.
+The intended flow is:
 
-Rule of thumb: if a diff against the source `.md.txt` shows a prose change, it needs a reason
-written down.
-
-### Import once, then hand-maintain — non-negotiable
-
-**Every page is hand-maintained the moment it is imported.** Maarten edits pages continuously to
-get them looking right; re-running an importer over one would silently discard that work.
-
-All **63** manifest pages carry `frozen = true`. There are no unfrozen pages, and that is the
-steady state — not a backlog to work through.
-
-- `port_docs.py` skips frozen pages. `check_fidelity.py` skips them too: divergence from
-  upstream is now *intended*, so comparing would produce noise rather than signal.
-- The `source` line stays for provenance.
-
-**Importing a new page** is the only time a converter runs:
-
-1. Add it to `scripts/sources.toml` *without* `frozen`.
-2. Run the pipeline: `port_docs.py` → `vendor_images.py` → `rewrite_links.py`.
-3. Review the result.
-4. **Set `frozen = true` immediately.** Before any hand-editing, not after.
-
-!!! warning "Converter improvements do not reach existing pages"
-    A fix to `convert_notebook.py` only affects future imports. Existing pages need the
-    equivalent change applied by hand. Weigh that before adding converter transforms aimed at
-    pages that are already in.
-
-**Visual guides** use `scripts/import_newsletter.py` instead (`importer = "import_newsletter"`).
-`gemma-4.md` and `mtp.md` were split by hand after import — re-importing either would merge MTP
-back into the parent guide and drop the pointer.
-
-### Standing rules
-
-Learned the hard way; each one cost a debugging session. Full stories in the archive.
-
-- **Never re-convert in bulk.** See above. There is no automatic way to tell hand-work from a
-  straight port.
-- **Any new workflow must be zizmor-clean before it is pushed.** Pin every action to a SHA, set
-  an explicit `permissions:` block, scope write permissions to the job that needs them. Check
-  with `uvx zizmor --persona=regular .github/workflows/` — seconds, and it catches what CI does.
-  The scan is diff-scoped, so pre-existing workflow debt stays invisible until a PR touches it.
-- **Measure the rendered page, don't reason about the renderer.** Four layout bugs in Step 1.4
-  and one in Step 3.1 were invisible in the source and obvious in one DOM query.
-- **`data/models.yml` and `data/frameworks.yml` are the single sources of truth.** Hand-editing a
-  model table in a page is a bug. Adding a framework name that isn't in the data file is a bug.
-- **Frontmatter keys shadow macros variables.** `models:` frontmatter silently broke
-  `models.gemma4.sizes` in both generated tables. The macros data is exposed as `modeldata.*`
-  for exactly this reason — never name a frontmatter key after an `include_yaml` variable.
-
-Local preview:
-
-```bash
-uv run zensical serve
+```text
+Markdown change
+  -> pull-request tests render temporary notebooks
+  -> merge to main
+  -> deployment publishes notebooks to generated-notebooks
+  -> Colab loads the generated branch
 ```
 
-Zensical's local server may download `.md` routes because it labels them as binary files. This
-is an accepted preview-only limitation; verify inline raw Markdown on the GitHub Pages deployment.
+Markdown is the only maintained tutorial source. A notebook can be an authoring origin for new
+code, but after its one-time import the Markdown owns all subsequent edits.
 
----
+### Other generated artifacts
 
-## Current status
+- [ ] Remove `docs/page-metadata.json` unless a concrete consumer is introduced. Nothing reads it
+      today, and its Git-derived update dates make ordinary documentation commits appear stale.
+- [ ] Decide whether `docs/llms.txt` remains committed for local `zensical serve` previews or is
+      written only into `site/`. It changes only with navigation and frontmatter, so either choice
+      is defensible; document the chosen behavior in `README.md`.
+- [ ] Keep the generated model lookup files committed for now. Model-data changes are deliberate
+      and infrequent, and Zensical's preview server has no supported build hook to generate them.
 
-| Phase | State |
-|---|---|
-| 0 — Foundation | ✅ complete |
-| 1 — Design system | ✅ complete |
-| 2 — Navigation & content | 🟡 nav and Run section done; 6 stubs and 6 keep-or-drop decisions left |
-| 3 — Agent-first infrastructure | ✅ POC complete: metadata, `llms.txt`, and raw Markdown routes |
-| 4 — Interactive context | 🟡 global model selection done; further tools deferred |
-| 5–6 — Showcase and Learn | ✅ narrow POC complete; broader rollout deferred |
-| 7–8 | deferred until after the POC |
-
-90 pages · 63 ported and frozen · 10 stylesheets · lint backlog **6** (all stubs).
-
-### POC finish line — in order
-
-1. Resolve the remaining Phase 2 content decisions and obvious stubs needed for a credible demo.
-2. ✅ Finish computed metadata and the Stripe-style `llms.txt` + raw Markdown surface (Steps 3.2–3.3).
-3. ✅ Add notebook launch actions to the exportable tutorials and one small task-first entry page
-   (Steps 6.2–6.3).
-4. ✅ Embed one HF Space with a plain-link fallback (Step 5.1).
+### Content decisions
 
-The persisted model selector is already the POC's interactive showcase. Everything explicitly
-marked deferred below is outside this finish line and must not block presenting the site.
+Resolve only the conspicuous placeholders needed for the demo. The rest can become post-merge
+issues instead of expanding this checklist.
 
-**Remaining Phase 2 work**, all blocked on content decisions rather than engineering:
-
-| Page | Blocked on |
+| Page | Decision or input needed |
 |---|---|
-| `community/gemmaverse.md` | real project data (still ships `example.com` cards) |
-| `community/discord.md` | port `/discord` |
-| `community/gemma-skills.md` | content — no upstream source |
-| `cookbook.md` | content decision |
-| `tools/index.md`, `tools/model-selector.md` | **Phase 4** — these *are* the interactive tools |
-
-**Upstream pages not ported (6):** `/core/gemma_library`, `/core/pytorch_gemma`,
-`/core/gemma_on_gemini_api`, `/core/deploy_to_cloud_run_from_ai_studio`, `/gemma_chat`,
-`/meme-generator`. Each needs a keep-or-drop decision rather than a straight port —
-`/meme-generator` in particular is a demo app, not reference material.
-
-Deliberately not ported and **not** gaps: `/integrations/{ollama,llamacpp,lmstudio,mlx}`
-(written as quick references instead), `/core/gke` (folded into Google Cloud),
-`/codegemma/*` (dropped by decision).
-
----
-
-## Target navigation
-
-Eight top-level tabs. Capabilities stay under **Get started** — they are fundamental Gemma 4
-capabilities and belong in one place where the full picture is visible.
-
+| `community/gemmaverse.md` | Replace `example.com` cards with real projects. |
+| `community/discord.md` | Port the official Discord page. |
+| `community/gemma-skills.md` | Supply first-party content; no upstream source exists. |
+| `cookbook.md` | Decide its purpose and scope. |
+| `tools/index.md`, `tools/model-selector.md` | Keep as deferred interactive-tool placeholders or remove from the POC navigation. |
+
+Six upstream pages still need individual keep-or-drop decisions:
+
+- `/core/gemma_library`
+- `/core/pytorch_gemma`
+- `/core/gemma_on_gemini_api`
+- `/core/deploy_to_cloud_run_from_ai_studio`
+- `/gemma_chat`
+- `/meme-generator`
+
+## Durable authoring rules
+
+### Import once, then hand-maintain
+
+Imported content is validated upstream. A port changes formatting, not prose or code.
+
+- Do not reword, reorder or silently correct imported prose during conversion.
+- Do not alter imported code without an explicit technical reason and review.
+- Every imported page records `source_url` in frontmatter.
+- Every existing manifest entry is frozen. Never reconvert frozen pages in bulk.
+- A new page is added to `scripts/importers/sources.toml` unfrozen, imported and reviewed once,
+  then marked `frozen = true` before documentation-specific editing begins.
+- Converter improvements affect future imports only; repair existing pages deliberately by hand.
+
+The official-doc import pipeline is:
+
+```text
+scripts/importers/port_docs.py
+  -> scripts/importers/vendor_images.py  # only if the source needs local assets
+  -> scripts/importers/rewrite_links.py
+  -> scripts/lint_docs.py --baseline
 ```
-Home
-Get started    Quickstart · Prompt & chat format
-               Capabilities: Text · Thinking · Function calling
-                             Vision (Image, Video) · Audio · Multi-Token Prediction
-Models         Gemma 4 (overview, model card)
-               Variants: Gemma 3n · DiffusionGemma · FunctionGemma
-                         EmbeddingGemma · PaliGemma · ShieldGemma
-               Previous generations: Gemma 3 · 2 · 1
-               Research: RecurrentGemma · DataGemma · Gemma Scope · Gemma APS
-               Releases
-Run            Local:  Ollama · llama.cpp · LM Studio · MLX · Transformers
-                       Keras · LiteRT · Web · Mobile
-               Serve:  vLLM · SGLang · Google Cloud
-               Frameworks: LangChain · Quantization & QAT
-Fine-tune      Overview · QLoRA · Full fine-tune · Vision QLoRA
-               Keras LoRA · Distributed · Spoken language · Mobile actions
-Learn          Visual guides · Tutorials · Notebooks
-Demos          Interactive tools · Apps & Spaces
-Ecosystem      Gemmaverse · Discord · Skills · Contribute
-```
-
-**Models stays model-card-centric.** It mirrors the upstream structure on purpose — model cards
-are canonical reference and shouldn't be creatively restructured. The improvement is in
-presentation, consistency, and cross-linking, not reorganization.
 
-`docs/models/` stays **flat**; grouping is nav-only, so a model's URL survives reclassification.
+`port_docs.py` runs the Markdown converter and fidelity check before writing. Visual guides use
+`scripts/importers/import_newsletter.py` instead. Temporary demo/vendor images are not worth
+reworking because they will be replaced with original PNGs.
 
-Decisions made: no "Migrating from Gemma 3 → 4" page (users swap one model string). Visual
-guides get fully ported pages, not outbound links. Nav labels are short ("Ollama"); page H1s
-stay as written ("Run Gemma with Ollama").
+### Markdown-first notebooks
 
----
+- Add `.notebook-cell` only to fences that should execute in Jupyter.
+- Ordinary prose becomes Markdown cells.
+- `.output` fences are omitted and recreated when the notebook runs.
+- Site-only regions use the notebook skip markers documented in
+  `docs/contributing/components.md`.
+- `.model-swap` exports its authored default; the selector remains a browser enhancement.
+- Do not maintain Markdown and notebook JSON in parallel.
 
-## Design system
+### Data and rendering
 
-**Official Gemma palette:**
+- `data/models.yml` and `data/frameworks.yml` are the single sources of truth for model and
+  framework facts. Do not hand-maintain copies in pages or JavaScript.
+- Frontmatter keys can shadow template data. Template macros use `modeldata.*` for this reason.
+- Measure rendered layout in a browser. Source inspection alone is not sufficient for visual work.
+- Keep Zensical pinned. A package update must not silently restyle the documentation.
+- New workflow changes must use least privilege, SHA-pinned actions and pass zizmor.
 
-| Token | Hex | Use |
-|---|---|---|
-| `--gemma-blue` | `#3186FF` | **Fill only** — buttons, bars, icons. 3.51:1 on white fails AA for text. |
-| `--gemma-blue-deep` | `#346BF1` | Text on light surfaces (4.65:1) |
-| `--gemma-blue-light` | `#4FA0FF` | Text on dark surfaces (5.98:1) |
-| `--gemma-white` | `#FFFFFF` | Light surface |
-| `--gemma-ink` | `#202124` | Dark surface, body text on light |
+## Maintained tooling
 
-Use `--gemma-accent`, which resolves to `blue-deep` in light and `blue-light` in dark, rather
-than naming a brand colour directly.
+The directory layout separates production infrastructure, optional import utilities and tests.
 
-**Stylesheets** — 10 files, each under 250 lines, each opening with what it owns and what it must
-not touch. Adding a focused sheet is the right move when a new concern appears; growing one into a
-catch-all is not.
+### Active build and validation: `scripts/`
 
-| File | Owns |
+| File | Responsibility |
 |---|---|
-| `gemma.css` | Tokens. **Only file with raw hex.** |
-| `layout.css` | Page grid, sidebars, typography scale |
-| `nav.css` | Header, tabs, sidebars, table of contents |
-| `content.css` | Tables, admonitions, code, content tabs |
-| `components.css` | Cards, badges, figures, steps, embeds |
-| `page-context.css` | The applicability bar above every H1 |
-| `tokens.css` | Chat-token badges |
-| `home.css` | Landing page |
-| `motion.css` | Transitions + reduced-motion opt-out |
-| `embeds.css` | Responsive external demo frames |
-
-Enforced by `lint_docs.py --check css`: raw hex only in `gemma.css`, every sheet registered in
-`extra_css`, none over 250 lines, no `<style>` blocks in templates.
+| `build_docs.py` | Production entry point: generators, Zensical build and raw Markdown publication. |
+| `agent_docs.py` | Builds `llms.txt` and raw `.md` routes. Remove page-metadata generation with the unused artifact. |
+| `lint_docs.py` | CI gate for documentation and repository invariants. |
+| `frontmatter_schema.py` | Shared frontmatter schema and controlled vocabularies. |
+| `generate_model_names.py` | Compiles model/framework data for templates and the browser selector. |
+| `notebooks.py` | Exports registered Markdown tutorials for notebook CI/publication. |
 
-Live catalogue with copy-paste markup: `docs/contributing/components.md`.
+### Reusable ingestion: `scripts/importers/`
 
----
+These remain supported because more official Markdown and notebook content is expected.
 
-## Frontmatter schema
-
-Defined in `scripts/frontmatter_schema.py` — the single source of truth, imported by both the
-linter and the backfill script so they cannot disagree. Validated by
-`lint_docs.py --check frontmatter`.
-
-**Required on every page:**
-
-| Key | Type | Notes |
-|---|---|---|
-| `title` | string | See *title is sometimes content* below |
-| `description` | string | One sentence, 40–200 chars. Feeds llms.txt, search, page cards. |
-
-**Optional, validated when present:**
-
-| Key | Type | Notes |
-|---|---|---|
-| `models` | list | Models the page is about. **Omit when it is about none.** |
-| `requires` | string | One input modality, narrowing `models` to the sizes that support it |
-| `frameworks` | list | Engines/libraries the page shows code for |
-| `prerequisites` | list | docs-relative paths; must resolve to real pages |
-| `name` | string | Stable kebab-case id for agents to reference |
-| `source_url` | string | Upstream page, from `scripts/sources.toml` |
-| `colab` | string | Generated notebook URL; renders a Colab link in the page context strip |
-
-**Reserved** for the Step 3.2 build hook and rejected if hand-written: `estimated_tokens` and
-`last_updated`. Theme keys (`template`, `hide`, `cards`, …) pass through.
-
-### Authored vs computed
-
-A key is either **authored** (only a person knows it) or **computed** (derivable from the file),
-never both. Hand-writing a token count guarantees it is wrong the next time anyone edits the
-page, so authoring a computed key is a lint *error*, not a style preference.
-
-### Vocabularies
-
-Both are read from data files, so adding an entry there is all that is needed:
-
-- **`models`** — `family` or `family/size`. Families come from the `docs/models/` directories;
-  Gemma 4 sizes come from `data/models.yml`. A bare `12b` is rejected: Gemma 3 and Gemma 4 both
-  ship a 12B, so the family is required. Only Gemma 4 has per-size data today; other families
-  are addressable at family level until someone adds sizes for them.
-
-    ```yaml
-    models: [gemma-4]                                  # the whole family
-    models: [gemma-4/e2b, gemma-4/e4b, gemma-4/12b]    # the audio-capable sizes
-    models: [embeddinggemma]                           # a variant
-    ```
-
-    **Omitting `models` means the page is not about a model** — community, tooling and site
-    pages. There is no "applies to everything" wildcard: that and "applies to nothing" were
-    being written identically, which made the key useless for the page context header.
-
-    The audio page's list is generated from the `modalities` field rather than typed out, so it
-    cannot drift from the model data.
-
-- **`frameworks`** — ids from `data/frameworks.yml`, which also carries display names,
-  categories and the docs page for each. A controlled vocabulary because the Step 4.2 switcher
-  cannot offer a free-text value, and `transformers` / `huggingface` / `hf` spread across 89
-  pages would make the feature unbuildable.
-
-### `title` is sometimes content
-
-Zensical synthesises an H1 from `title` (`<h1 id="__skip">`) **only when the page has no H1 of
-its own**; it never replaces an existing heading. That gives the key two roles:
-
-- **Page has an H1** — `title` reaches only the browser tab and llms.txt, and may be *more*
-  specific than the heading. This is why nine pages headed "Model card" carry titles like
-  `EmbeddingGemma model card`: the heading on screen is untouched, the tab and the
-  machine-readable index become useful.
-- **Page has no H1** — `title` *is* the visible heading and must say what the page already said.
-  Five pages are in this state: `deployment/index.md`, `deployment/google-cloud.md`,
-  `models/gemma-4/model-card.md`, `models/diffusiongemma/inference.md`, `models/releases.md`.
-  The linter requires `title` on any page lacking an H1.
-
-Giving those five a real H1 would be a content change, so it is Maarten's call, not the port's.
+| File | Responsibility |
+|---|---|
+| `convert_notebook.py` | Convert official `.md.txt`/notebook exports into Zensical Markdown. |
+| `port_docs.py` | Manifest-driven, freeze-aware import orchestration. |
+| `check_fidelity.py` | Detect prose lost during a new conversion. |
+| `rewrite_links.py`, `link_map.toml` | Convert upstream links after import. |
+| `vendor_images.py` | Optional remote-asset step for a new import. |
+| `import_newsletter.py` | Import additional first-party visual guides; remove if that roadmap is dropped. |
+| `sources.toml` | Import provenance and frozen-page safety boundary. |
 
----
+### Tests: `tests/`
 
-## Page context header — **done 2026-08-05**
+Regression tests and converter fixtures belong in `tests/`, not beside executable maintenance
+commands. CI discovers `test_*.py` from this directory.
 
-A Stripe-style bar at the top of **every page** — see <https://docs.stripe.com/billing/scripts>.
-Not silence-by-default: the slot is always present, so readers learn where to look.
+Removed from the maintained surface:
 
-It carries two things:
+- `backfill_frontmatter.py`: completed one-time migration.
+- `PLAN-ARCHIVE.md`: duplicated Git history and made the supported workflow harder to find.
 
-1. **Applicability** — ✅ built.
-2. **Model selection** (Step 4.2) — ✅ built; page-scoped context belongs here, not site chrome.
+## Verification
 
-- [x] `overrides/main.html` renders the bar on every page, inside `.md-content__inner` so it
-      shares one left edge with the breadcrumb, H1 and body.
-- [x] Applicability badges from `models`, display names from `data/models.yml`.
-- [x] Empty state holds its height, so the breadcrumb→H1 distance never changes between pages.
-- **Verified:** audio shows `Gemma 4 · E2B E4B 12B`, discord shows nothing, both 28px tall with
-  a 16px gap to the H1; all four left edges land on 304px; contrast passes AA in both schemes
-  (light 4.68/6.73/9.00, dark 6.64/9.25/12.12).
-- **Done when:** ✅ adding a size to `data/models.yml` changed the audio page without the page
-  being touched — confirmed with a throwaway `7b-probe` size, then reverted.
+Run before handoff or merge:
 
-### Applicability is declared two ways
+```bash
+uv run --frozen python -m unittest discover -s tests -p "test_*.py"
+uv run --frozen python scripts/lint_docs.py --baseline
+uv run --frozen python scripts/build_docs.py
+```
 
-| Frontmatter | Shows |
-|---|---|
-| *(no `models`)* | nothing — the slot stays empty |
-| `models: [gemma-3]` | `Gemma 3` |
-| `models: [gemma-4/e2b, gemma-4/e4b]` | `Gemma 4 · E2B E4B` |
-| `models: [gemma-4]` + `requires: audio` | `Gemma 4 · E2B E4B 12B`, resolved at build time |
+For a new official-doc import, additionally run the relevant importer in `--dry-run` mode and
+review its fidelity report before writing files.
 
-**Prefer `requires` whenever the set *is* "the models that can do X".** A listed set is a copy
-of the model data and goes stale the day a new size ships; a derived one cannot. `requires`
-takes one input modality, validated against `data/models.yml`, and narrows the family named in
-`models` — so it needs `models` to be present.
+## Deferred roadmap
 
-This was the gap that testing the "done when" exposed: the audio page's three sizes had been
-written into its frontmatter by the Step 3.1 backfill, which is generation at *authoring* time,
-not build time. It looked data-driven and wasn't.
+### Content and Learn
 
-### Design
+- Continue the Markdown-first notebook ingestion/export workflow, but keep it one-way after import.
+- Add visual-guide assets only when original, production-quality PNGs are ready.
+- Expand the simple task-first Learn page only when new content justifies another route.
 
-Measured off `docs.stripe.com`, not approximated. Their page toolbar is `display:flex`,
-`gap:16px`, `align-items:baseline`, `padding-bottom:12px`, a 1px hairline rule beneath, and
-between items a `<span role="separator">` **1px wide with `align-self: stretch`** in a tone
-deliberately darker than that rule. Nothing sits above the H1 at all.
+### Demos and embeds
 
-```
-Models  Gemma 4  │  Uses  Ollama  │  Examples  Gemma 4 12B ⌄
-──────────────────────────────────────────────────────────────
-```
+- Keep the current single HF Space as the POC example.
+- Defer additional spaces, media embeds and interactive demos until the core content is settled.
 
-Stripe's toolbar items are peer actions at one weight. Ours are label→value pairs, a structure
-they never have to solve — so two rules do the work their flat treatment cannot:
-
-1. **Spacing is hierarchical.** 6px inside a pair, 16px between pairs. An 8px/16px ratio was
-   too weak and the whole line read as one run of grey.
-2. **Every pair gets the same label/value contrast.** Emphasising the model value but not the
-   framework values made `Frameworks` and `Sentence Transformers` indistinguishable.
-
-Model names link to their local canonical page. Tool names link to the project home. Both stay
-neutral-coloured; the selector is the only control in the strip.
-
-### Subject vs. selection
-
-The strip carries two different kinds of thing, and conflating them was the original design
-error:
-
-| Item | Source | Answers |
-|---|---|---|
-| `Models` | `models:` frontmatter | what the page is **about** |
-| `Uses` | `frameworks:` frontmatter | what it uses |
-| `Examples` | the page's marked code blocks | what the **reader** wants the commands to target |
-
-Driving the selector from `models:` looked reasonable and was wrong: the Ollama page is about
-Gemma 4, but its commands could target anything Ollama publishes, and Quick Start ended up
-claiming `E2B` on a page that covers all five sizes. The selector is now built in JavaScript
-from the identifiers actually present in the page's `.model-swap` blocks:
-
-- a model must have a published id for **every** engine on the page, so a switch cannot rewrite
-  some snippets and leave others behind;
-- `requires:` narrows it further, so an audio page can never offer a model without ears;
-- with fewer than two candidates there is no control at all.
-
-This also gives the extension point for later generations: add Gemma 3's Ollama tags to
-`data/models.yml` and it appears in the Run pages' selector with no template change.
-
-### How the data reaches the template
-
-Zensical renders with **minijinja**, exposes `page.meta` to templates but *not* the macros
-plugin's YAML, and has no hook mechanism to inject any. So `data/models.yml` is compiled into
-`overrides/partials/model-names.html` by `scripts/generate_model_names.py`, and
-`lint_docs.py --check generated` fails if the two drift apart. Committed build output, verified
-in CI — the same arrangement as `.lintbaseline.json`.
-
-Writing template logic here: minijinja has no `.get()` and no Python string methods. Use
-`dict[key] | default(...)`, the `split` filter, and the `startingwith` test.
-
-**Interaction decision.** The picker is local to pages with swappable examples, but its full
-model choice is remembered globally and mirrored in `?model=` for shareable URLs. Code blocks
-opt in with `.model-swap`; there are no competing per-block selectors. If the remembered model
-is unavailable, the authored page default remains active and the menu names the incompatibility.
-Silent substitution is rejected.
-
----
-
-## Phase 3 — Agent-first infrastructure (3.1 ✅)
-
-This is the main unfinished POC phase. A good machine-readable index and raw Markdown pages give
-agents enough to discover and traverse the live site without cloning this repository.
-
-### Step 3.2 — Computed metadata
-- [x] Build-time generator computing `estimated_tokens` and `last_updated` automatically so they cannot
-      drift. Do not compute or expose code-language metadata; it adds noise without improving
-      discovery.
-- [x] Publish the values in `/page-metadata.json`; keep them out of authored frontmatter.
-- **Verify:** edit a page, rebuild, and confirm both values update without frontmatter changes.
-- **Done when:** neither value is hand-authored and the linter rejects attempts to do so.
-
-Zensical does not yet expose a stable build-hook API, so `scripts/build_docs.py` is the explicit
-build entry point. It runs the deterministic generators, delegates to Zensical, and then adds
-the raw-document routes. This keeps the integration replaceable when a supported hook arrives.
-
-### Step 3.3 — Machine-readable surfaces — **POC priority**
-- [x] Generate `/llms.txt` and serve raw Markdown at `<page>.md` for every documentation page.
-- [x] Keep `llms.txt` concise; do not add `/llms-full.txt` unless a demonstrated agent workflow
-      needs the entire site in one response.
-- [x] Mirror the useful structure of <https://docs.stripe.com/llms.txt>:
-    - one `# Gemma Documentation` heading;
-    - a short introduction with durable Gemma-specific guidance for agents;
-    - `##` sections matching the site's main information architecture, with an optional
-      one-sentence description;
-    - entries formatted as `- [Title](https://gemma.ai/path.md): Description` using absolute
-      raw-Markdown URLs;
-    - descriptions and titles generated from page frontmatter, with deterministic ordering and
-      no duplicate entries.
-- [x] Include a short agent-guidance section only for genuinely important defaults and
-      deprecations. Keep navigation in the sectioned link index rather than writing a separate
-      agent skill.
-- **Verify:** fetch `/llms.txt`, follow representative `.md` links from at least three sections,
-      and confirm each returns clean source Markdown rather than rendered HTML.
-- **Done when:** an agent starting with only `https://gemma.ai/llms.txt` can find the appropriate
-      page and retrieve its Markdown without repository access.
-
-Compose entry labels from `title` + `models` rather than adding a second title key —
-`models: [gemma-4]` plus `title: Model card` gives "Gemma 4 — Model card" without touching the
-page. Every input this needs already exists.
-
-### Deliberately omitted from the POC
-
-- **Step 3.4:** no "Copy page as Markdown" button and no "Open in Gemini / Claude / ChatGPT"
-  links. Raw `.md` URLs are the smaller, universal primitive.
-- **Step 3.5:** no packaged docs skill or MCP server. If `llms.txt` later proves insufficient,
-  a live-site skill should teach an agent to fetch `llms.txt`, choose a section, and follow `.md`
-  pages on the website—never require cloning the repository.
-
----
-
-## Phase 4 — Interactive context
-
-### Step 4.2 — Global model switcher — **complete**
-
-The current behavior is already global: selecting a Gemma 4 model persists the full model choice
-in `localStorage`, follows the reader across pages, and is mirrored in `?model=` for shareable
-URLs. The selector is only rendered on pages with examples it can safely rewrite.
-
-- [x] Persist the selected model across pages and in the URL.
-- [x] Rewrite opt-in quickstart, deployment, capability, QAT, and QLoRA examples.
-- [x] Gate choices by capability and published identifier flavor.
-- [x] Keep intentionally separate examples fixed when a matching checkpoint is unavailable.
-- **Verified:** selecting 31B persists across compatible pages; audio explains its fallback to
-  E2B; selecting 26B on vLLM rewrites regular examples while its unavailable 26B QAT example
-  remains explicitly fixed at 12B.
-- **Done:** no global engine preference is required for the POC. Revisit it only after several
-  pages have equivalent, verified examples for the same set of engines.
-
-### Deferred interactive tools
-
-The existing model switcher is enough to demonstrate interactive documentation. The former
-"Choose your model" flow and tokenizer/chat-template playground are useful follow-ups, but are
-not required for the POC.
-
----
-
-## Phase 5 — Demos & embeds — **finish after the core POC**
-
-### Step 5.1 — One simple HF Space embed
-- [x] Embed <https://huggingface.co/spaces/google/gemma4_vision_token_budget> on one Demos page
-      using the simplest supported iframe approach.
-- [x] Lazy-load it and provide a normal link as the fallback if the embed cannot load.
-- **Verify:** the embed and fallback link are usable at desktop and mobile widths.
-- **Done when:** one representative external demo works; no reusable embed framework or sleeping
-      Space detection is required.
-
-The former Step 5.2—concurrent inference and a broader collection of Spaces—is not planned for
-the current POC.
-
----
-
-## Phase 6 — Learn — **minimal POC scope**
-
-### Step 6.1 — Visual-guide asset work — **deferred**
-
-Do not vendor or rework the visual-guide images for the current POC. The existing guides can
-remain as they are; revisit ownership and asset quality only if they become part of the final
-production scope.
-
-### Step 6.2 — Markdown-first tutorials and generated notebooks
-
-The capability tutorials originated as notebooks in the
-[`google-gemma/cookbook`](https://github.com/google-gemma/cookbook/tree/main/docs/capabilities)
-repository, but the Zensical pages are now intentionally diverging: they add frontmatter,
-admonitions, styled output, HTML components, and runtime features such as the page-level model
-selector on `get-started/capabilities/thinking.md`. Re-importing an updated notebook would erase
-those documentation-specific improvements.
-
-**Decision:** Markdown is canonical for documentation content. Runnable notebooks are generated
-artifacts, not an alternate place to maintain the same tutorial.
-
-- [x] Spike the workflow on `get-started/capabilities/thinking.md` before applying it elsewhere.
-- [x] Apply the same first-party export and Colab behavior to the text, function calling, image,
-      video, audio, QLoRA, full fine-tuning, and vision QLoRA tutorials. Their Colab links open
-      notebooks generated in this repository, not the older Cookbook sources.
-- [x] Define an explicit export contract in Markdown:
-    - Python fences marked for notebook export become code cells;
-    - ordinary prose becomes Markdown cells;
-    - `.output` blocks are omitted and recreated by execution;
-    - site-only HTML, tabs, and interactive components are omitted or reduced to a plain-text
-      notebook fallback;
-    - `.model-swap` exports its authored default model—the browser selector remains a site-only
-      enhancement.
-- [x] Generate `.ipynb` with stable cell boundaries and kernel/Colab metadata through a small,
-      dependency-light exporter. Keep the contract compatible with a later Jupytext-backed
-      implementation rather than forcing MyST syntax into the website sources.
-- [ ] Support notebook → Markdown as a **one-time ingestion path** for new or heavily revised code
-      examples. Once imported, the Markdown becomes canonical and subsequent notebook builds run
-      Markdown → `.ipynb`; do not allow routine edits in both directions.
-- [x] Add one quiet **Colab** icon link to the page context strip. Do not add
-      redundant download or source actions; Colab already supports download and Markdown is the
-      visible source of truth. Do not claim a Kaggle target until there is a stable published URL.
-- [x] Start with lightweight structural tests rather than execution of GPU notebooks in CI:
-      validate nbformat v4 output, executable-cell selection, and removal of site-only regions.
-- **Verify:** export all registered pages, open at least one capability and one tuning notebook,
-      and confirm that their prose and code cells are useful while the live pages retain their
-      selectors and Zensical presentation.
-- **Done when:** each registered tutorial can be edited only in Markdown, published as the richer
-      Zensical page, and exported to a runnable notebook without hand-editing notebook JSON.
-
-This deliberately separates **authoring origin** from **source of truth**: it is fine to prototype
-new code in Jupyter and import it, but after the documentation-specific pass the Markdown owns the
-tutorial. That prevents a later notebook export from overwriting selector hooks, HTML, or styling.
-
-### Step 6.3 — One simple task-first page
-- [x] Create a single "I want to…" page with a small set of cards—such as run locally, understand
-      images, call tools, and fine-tune—linking to existing documentation.
-- [x] Do not add a recommendation engine or duplicate tutorial content.
-- **Done when:** a newcomer can choose a common goal and reach an existing starting page in one
-      click.
-
----
-
-## Phase 7 — The wow tier — **deferred; retain for later**
-
-Only after Phases 0–2 are clean.
-
-### VRAM & context calculator — **moved from Step 4.1**
-
-- [ ] Replace the static table in `docs/tools/vram-calculator.md` with inputs for model,
-      quantization, context length, batch size, KV-cache dtype, and a small set of GPU presets.
-- [ ] Show whether it fits with headroom and provide the exact command for the recommended engine.
-- [ ] Read model facts from `data/models.yml` and encode state in the URL.
-- **Verify:** results match the memory table in `models/gemma-4/index.md` at matching settings.
-- **Done when:** a shared URL reproduces the same result.
-
-- [ ] **In-browser playground** — E2B q4 via WebGPU/transformers.js, running in the docs page.
-      ~1.5–2 GB download, so explicit opt-in and clear progress UI.
-- [ ] **Ask Gemma about Gemma** — docs chatbot on transformers.js or an HF Space endpoint.
-- [ ] **Interactive Gemma 4 architecture explainer** — PLE, MoE routing on 26B A4B, MTP draft
-      heads, the 12B's encoder-free linear projections. These four confuse people and prose
-      isn't fixing it.
-- [ ] **MTP animated explainer** — draft-vs-verify with an acceptance-rate slider.
-- [ ] **DiffusionGemma denoising animation** — text unmasking step by step.
-- [ ] **Community throughput leaderboard** — measured tok/s per (model × quant × engine ×
-      hardware), PR-based. Exists nowhere authoritative; high maintenance, high payoff.
-- [ ] **Cost calculator** — GPU $/hr → $/1M tokens, self-host vs. API.
-
----
-
-## Phase 8 — Maintainer skill & handover — **deferred; retain for later**
-
-*Finalized once the docs are done, but **drafted incrementally** — every phase that establishes a
-convention adds its section immediately, while the reasoning is fresh.*
-
-### Step 8.1 — `gemma-docs` authoring skill
-Must cover:
-
-- [ ] **Content fidelity rules** — stated first and plainly.
-- [ ] **How to port a page** — fetch `.md.txt`, run the pipeline, what the converter handles vs.
-      what needs manual repair, and freezing immediately.
-- [ ] **How to add a page** — nav placement, frontmatter schema, which components to use.
-- [ ] **The design system** — which stylesheet owns what, the token layer, when to add a sheet.
-- [ ] **Component catalog** — every card/callout/badge/figure with copy-paste examples.
-- [ ] **The data files** — what reads `data/models.yml` and `data/frameworks.yml`, and why
-      hand-editing a model table is a bug.
-- [ ] **Verification** — the lint checks, how to run the build, what CI enforces.
-- [ ] **Gotchas** — everything in the archive's *Notes*.
-- [ ] **What not to do** — don't reword ported prose, don't hotlink images, don't add hex outside
-      the token file, don't commit or push.
-- **Verify:** hand a fresh agent the skill and a page to port. It should succeed with no
-  additional context and pass the linter first try.
-
-### Step 8.2 — Contributor docs
-- [ ] `docs/contributing/` covering the same ground for humans.
-
----
-
-## Tooling lifecycle
-
-Scripts are **migration tooling or permanent infrastructure**, and the two have opposite
-lifespans. Every entry below has a stated end date so the directory doesn't quietly become an
-archive of one-shot scripts nobody dares delete.
-
-| Script | Lines | Fate |
-|---|---|---|
-| `lint_docs.py` | 804 | **Permanent.** CI gate. |
-| `frontmatter_schema.py` | 233 | **Permanent.** Imported by the linter. |
-| `generate_model_names.py` | 86 | **Permanent.** Compiles `data/models.yml` into the template partial the page context header reads; CI verifies it is current. |
-| `convert_notebook.py` + `test_convert_notebook.py` | 896 | **Refactor during Step 6.2.** Keep notebook → Markdown as an ingestion path and add the Markdown → notebook exporter; remove assumptions that upstream notebooks remain canonical after import. |
-| `port_docs.py` | 135 | **Delete with the converter.** All 63 manifest pages are frozen, so today it can only skip. |
-| `check_fidelity.py` | 223 | **Delete with the converter.** Same reason: it reports `0 faithful, 63 frozen` and has nothing left to check. |
-| `rewrite_links.py` | 253 | **Delete with the converter.** One-shot link migration; `links` and `internal` have been 0 for weeks. |
-| `sources.toml` + `link_map.toml` | 577 | **Delete with the converter.** ⚠️ `source` is now duplicated in every page's `source_url` frontmatter — the manifest's only unique data is `frozen`, `assets` and `importer`. |
-| `backfill_frontmatter.py` | 373 | **Delete when Phase 3 closes.** One-shot; frontmatter is complete and hand-maintained from here. New pages get theirs from the Phase 8 skill. |
-| `import_newsletter.py` | 259 | **Keep through the POC.** Visual-guide work is deferred, so there is no cleanup deadline yet. |
-| `vendor_images.py` | 136 | **Keep through the POC.** Revisit only if visual-guide assets return to scope. |
-
-Roughly **2,450 of 3,877 lines are scheduled for deletion**, most of it gated on one decision:
-whether anything else gets ported from upstream. Making that call early shrinks the surface a
-maintainer has to understand.
-
----
-
-## Pipeline order
-
-Steps that rewrite pages must run in this order, because each stage can undo the previous one
-(conversion restores upstream image URLs; vendoring replaces them with local paths):
+### Interactive tools
 
-```
-port_docs.py  ->  vendor_images.py  ->  rewrite_links.py  ->  lint_docs.py --baseline
-```
+- VRAM and context calculator driven by `data/models.yml`, with model, quantization, context,
+  batch size, KV-cache dtype and GPU presets; encode state in the URL.
+- In-browser E2B q4 playground via WebGPU/transformers.js with an explicit large-download opt-in.
+- "Ask Gemma about Gemma" documentation assistant.
+- Interactive Gemma 4 architecture and MTP explainers.
+- DiffusionGemma denoising animation.
+- Community throughput leaderboard, only if its maintenance cost is justified.
+- Self-hosting cost calculator.
 
-`check_fidelity.py --manifest` can run at any point; it ignores image targets by design.
+### Maintainer handover
 
----
+Create a concise Gemma-docs authoring skill and corresponding contributor guide covering:
 
-## Open decisions
+- content fidelity and the freeze-on-import workflow;
+- adding pages, frontmatter and navigation;
+- design tokens and reusable components;
+- model/framework data ownership;
+- notebook export/publication;
+- verification and CI expectations.
 
-- **Will anything else be ported from upstream?** Gates the deletion of ~2,450 lines of tooling
-  (see above) and closes Phase 2.4.
-- The 6 unported upstream pages — keep or drop, individually.
-- Whether visual-guide diagrams can also appear inline in the reference docs (with attribution),
-  which would be a stronger integration than standalone guide pages alone.
-- Whether the throughput leaderboard is worth its ongoing maintenance cost.
-- The five pages with no H1 — leave as-is, or add real headings.
-- **Unresolved: Ollama download sizes.** `ollama.com/library/gemma4/tags` states E4B 9.6 GB and
-  12B 7.6 GB, making E4B *larger* than 12B, which contradicts the validated Q4_0 column in
-  `models/gemma-4/index.md`. Verified verbatim twice. Most likely the default tag per size uses a
-  different quantization, but no explanation appears on the page — so the figures stay
-  unexplained rather than guessed at.
+Do not copy this plan into another archive when work completes. Move durable instructions into
+the contributor guide, create issues for remaining product work, and delete completed checklist
+detail from this file.
diff --git a/README.md b/README.md
index 3f38820..16d3829 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ GitHub Pages serves those static files with their Markdown content type.
 Run these checks before committing documentation or styling changes:
 
 ```bash
-uv run --frozen python -m unittest discover -s scripts -p "test_*.py"
+uv run --frozen python -m unittest discover -s tests -p "test_*.py"
 uv run --frozen python scripts/lint_docs.py --baseline
 uv run --frozen python scripts/build_docs.py
 ```
diff --git a/data/models.yml b/data/models.yml
index 337e0c9..893e040 100644
--- a/data/models.yml
+++ b/data/models.yml
@@ -11,7 +11,7 @@
 # Hand-editing a model table in a page is a bug. Change it here instead.
 #
 # NOT generated from this file: the memory table in models/gemma-4/index.md. That page is a
-# straight port (scripts/sources.toml) and re-porting would overwrite any template. The figures
+# straight port (`scripts/importers/sources.toml`) and re-porting would overwrite any template. The figures
 # below are copied from it; keep them in sync if upstream revises them.
 
 # ---------------------------------------------------------------------------------------------
diff --git a/docs/contributing/components.md b/docs/contributing/components.md
index 9a5bfe9..d7f2077 100644
--- a/docs/contributing/components.md
+++ b/docs/contributing/components.md
@@ -217,7 +217,7 @@ For a diagram that needs different artwork per theme, mark the pair and let CSS
 
 ## Video
 
-Emitted automatically by `scripts/convert_notebook.py` from a standalone `[Video](youtube…)`
+Emitted automatically by `scripts/importers/convert_notebook.py` from a standalone `[Video](youtube…)`
 link. Full content width, 16:9, lazy-loaded, privacy-mode host.
 
 ```html
diff --git a/docs/page-metadata.json b/docs/page-metadata.json
index 2e9127d..a916e48 100644
--- a/docs/page-metadata.json
+++ b/docs/page-metadata.json
@@ -22,8 +22,8 @@
     "contributing/components.md": {
       "url": "/contributing/components/",
       "markdown_url": "/contributing/components.md",
-      "estimated_tokens": 2675,
-      "last_updated": "2026-08-05"
+      "estimated_tokens": 2677,
+      "last_updated": "2026-08-10"
     },
     "cookbook.md": {
       "url": "/cookbook/",
@@ -131,19 +131,19 @@
       "url": "/get-started/capabilities/audio/",
       "markdown_url": "/get-started/capabilities/audio.md",
       "estimated_tokens": 3814,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "get-started/capabilities/function-calling.md": {
       "url": "/get-started/capabilities/function-calling/",
       "markdown_url": "/get-started/capabilities/function-calling.md",
       "estimated_tokens": 5585,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "get-started/capabilities/image.md": {
       "url": "/get-started/capabilities/image/",
       "markdown_url": "/get-started/capabilities/image.md",
       "estimated_tokens": 5055,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "get-started/capabilities/mtp.md": {
       "url": "/get-started/capabilities/mtp/",
@@ -155,19 +155,19 @@
       "url": "/get-started/capabilities/text/",
       "markdown_url": "/get-started/capabilities/text.md",
       "estimated_tokens": 2560,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "get-started/capabilities/thinking.md": {
       "url": "/get-started/capabilities/thinking/",
       "markdown_url": "/get-started/capabilities/thinking.md",
       "estimated_tokens": 6288,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "get-started/capabilities/video.md": {
       "url": "/get-started/capabilities/video/",
       "markdown_url": "/get-started/capabilities/video.md",
       "estimated_tokens": 12092,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "get-started/capabilities/vision.md": {
       "url": "/get-started/capabilities/vision/",
@@ -479,7 +479,7 @@
       "url": "/tuning/full-finetune/",
       "markdown_url": "/tuning/full-finetune.md",
       "estimated_tokens": 4869,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "tuning/keras-lora.md": {
       "url": "/tuning/keras-lora/",
@@ -503,7 +503,7 @@
       "url": "/tuning/qlora/",
       "markdown_url": "/tuning/qlora.md",
       "estimated_tokens": 6042,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "tuning/spoken-language.md": {
       "url": "/tuning/spoken-language/",
@@ -515,7 +515,7 @@
       "url": "/tuning/vision-qlora/",
       "markdown_url": "/tuning/vision-qlora.md",
       "estimated_tokens": 5232,
-      "last_updated": "2026-08-05"
+      "last_updated": "2026-08-10"
     },
     "tutorials/visual-guides/diffusiongemma.md": {
       "url": "/tutorials/visual-guides/diffusiongemma/",
diff --git a/scripts/backfill_frontmatter.py b/scripts/backfill_frontmatter.py
deleted file mode 100644
index cd3c1f4..0000000
--- a/scripts/backfill_frontmatter.py
+++ /dev/null
@@ -1,373 +0,0 @@
-"""Fill in missing frontmatter across docs/ without touching a single line of body text.
-
-Every page is hand-maintained (PLAN.md, "Import once, then hand-maintain"), so this script
-is deliberately conservative:
-
-  * It never rewrites a key that already exists -- yours wins, always.
-  * It never touches the body. The block is rebuilt and the rest of the file is copied
-    through verbatim, which `--check` verifies.
-  * Existing keys keep their original raw lines, so nested structures (`cards:`, `hide:`)
-    and any comments survive exactly as written. Only the key *order* is normalised.
-
-Descriptions are derived from the page's opening paragraph. That is a starting point for
-review, not an authoring system -- run with `--report` to see which ones were guessed.
-
-    uv run python scripts/backfill_frontmatter.py --check     # report, write nothing
-    uv run python scripts/backfill_frontmatter.py             # write
-"""
-
-from __future__ import annotations
-
-import argparse
-import re
-import sys
-import tomllib
-from pathlib import Path
-
-import yaml
-
-from frontmatter_schema import (
-    DESCRIPTION_MAX,
-    DESCRIPTION_MIN,
-    DOCS_DIR,
-    REPO_ROOT,
-    SCHEMA,
-    THEME_KEYS,
-    models_with_modality,
-    ported_sources,
-)
-
-# Emission order: required keys first, then optional authored keys, then theme keys.
-KEY_ORDER = [f.name for f in SCHEMA] + sorted(THEME_KEYS)
-
-FENCE = re.compile(r"^\s*(```|~~~)")
-HEADING = re.compile(r"^#\s+(.*)$")
-ATTR_LIST = re.compile(r"\s*\{[^}]*\}\s*$")
-MD_LINK = re.compile(r"\[([^\]]*)\]\([^)]*\)")
-MD_EMPHASIS = re.compile(r"[*_`]+")
-HTML_TAG = re.compile(r"<[^>]+>")
-SENTENCE_END = re.compile(r"(?<=[.!?])\s+")
-
-# Lines that open a paragraph but are not prose.
-NON_PROSE_PREFIX = ("!!!", "???", "|", ">", "-", "*", "+", "<", "![", "=== ", ":", "{")
-
-
-# --- Frontmatter block surgery ----------------------------------------------------------------
-
-
-def split_block(text: str) -> tuple[list[str], str]:
-    """Return (frontmatter lines, rest-of-file). Rest is byte-identical to the input tail."""
-    if not text.startswith("---"):
-        return [], text
-    lines = text.split("\n")
-    for idx in range(1, len(lines)):
-        if lines[idx].strip() == "---":
-            return lines[1:idx], "\n".join(lines[idx + 1 :])
-    return [], text  # unterminated: treat it all as body
-
-
-def segment(block: list[str]) -> dict[str, list[str]]:
-    """Group a frontmatter block into {top-level key: its raw lines}.
-
-    Continuation lines (indented, blank) and preceding comments stay with their key, so a
-    nested `cards:` list round-trips exactly.
-    """
-    segments: dict[str, list[str]] = {}
-    pending: list[str] = []
-    current: str | None = None
-    for raw in block:
-        stripped = raw.strip()
-        is_top_level = raw[:1] not in (" ", "\t") and ":" in raw and not stripped.startswith("#")
-        if is_top_level:
-            current = raw.partition(":")[0].strip()
-            segments[current] = [*pending, raw]
-            pending = []
-        elif current is None or not stripped:
-            pending.append(raw)
-        else:
-            segments[current].append(raw)
-    return segments
-
-
-def parse(block: list[str]) -> dict:
-    try:
-        return yaml.safe_load("\n".join(block)) or {}
-    except yaml.YAMLError:
-        return {}
-
-
-def emit(key: str, value) -> list[str]:
-    if isinstance(value, list):
-        inline = ", ".join(str(v) for v in value)
-        return [f"{key}: [{inline}]"]
-    text = str(value)
-    if any(ch in text for ch in ':#"') or text != text.strip():
-        return [f'{key}: "{text}"']
-    return [f"{key}: {text}"]
-
-
-def render(segments: dict[str, list[str]], additions: dict[str, object]) -> str:
-    """Rebuild the block: known keys in canonical order, anything unrecognised last."""
-    lines: list[str] = []
-    ordered = [k for k in KEY_ORDER if k in segments or k in additions]
-    trailing = [k for k in segments if k not in KEY_ORDER]
-    for key in ordered + trailing:
-        lines.extend(segments[key] if key in segments else emit(key, additions[key]))
-    return "---\n" + "\n".join(lines) + "\n---\n"
-
-
-# --- Inference --------------------------------------------------------------------------------
-
-
-def strip_fences(lines: list[str]) -> list[str]:
-    """Blank out fenced blocks. Shell comments (`# Install PyTorch`) are not headings, and
-    reading them as one is how `diffusiongemma/inference.md` acquired the wrong title."""
-    out, in_fence = [], False
-    for line in lines:
-        if FENCE.match(line):
-            in_fence = not in_fence
-            out.append("")
-        else:
-            out.append("" if in_fence else line)
-    return out
-
-
-def infer_title(body: str, rel: str, nav_labels: dict[str, str]) -> str:
-    for line in strip_fences(body.split("\n")):
-        match = HEADING.match(line)
-        if match:
-            text = ATTR_LIST.sub("", match.group(1))
-            text = MD_EMPHASIS.sub("", MD_LINK.sub(r"\1", text)).strip()
-            if text:
-                return text
-    # No H1 on the page. The nav label is hand-curated, so it beats a filename guess.
-    return nav_labels.get(rel) or Path(rel).stem.replace("-", " ").capitalize()
-
-
-def lead_paragraphs(body: str, limit: int = 3) -> list[str]:
-    """The opening prose paragraphs, skipping fences, HTML embeds, images, tables, callouts.
-
-    Non-prose openers skip to the next blank line rather than advancing one line: an
-    `<iframe` spans several lines, and only its first one starts with `<`.
-    """
-    lines = strip_fences(body.split("\n"))
-    found: list[str] = []
-    idx = 0
-    while idx < len(lines) and len(found) < limit:
-        stripped = lines[idx].strip()
-        if not stripped or stripped.startswith("#"):
-            idx += 1
-            continue
-        if stripped.startswith(NON_PROSE_PREFIX):
-            while idx < len(lines) and lines[idx].strip():
-                idx += 1
-            continue
-        collected = []
-        while idx < len(lines) and lines[idx].strip():
-            collected.append(lines[idx].strip())
-            idx += 1
-        paragraph = " ".join(collected)
-        # Link rows ("Hugging Face | GitHub | Docs") and stray tag attributes are not prose.
-        if '="' not in paragraph and paragraph.count("|") < 2:
-            found.append(paragraph)
-    return found
-
-
-# Upstream pages open with a boilerplate "Gemma is a family of..." paragraph and only then
-# say what the page covers. That second paragraph is the description we actually want.
-SCOPE_OPENER = re.compile(r"^This (document|guide|page|tutorial|notebook|section)\b", re.I)
-
-
-def lead_paragraph(body: str) -> str:
-    paragraphs = lead_paragraphs(body)
-    for paragraph in paragraphs:
-        if SCOPE_OPENER.match(paragraph):
-            return paragraph
-    return paragraphs[0] if paragraphs else ""
-
-
-def infer_description(body: str, title: str) -> tuple[str, str]:
-    """Return (description, quality) where quality is "derived", "truncated" or "fallback"."""
-    paragraph = lead_paragraph(body)
-    paragraph = HTML_TAG.sub("", MD_LINK.sub(r"\1", paragraph))
-    paragraph = MD_EMPHASIS.sub("", paragraph).replace("\\|", "|").strip()
-
-    if paragraph:
-        sentences = SENTENCE_END.split(paragraph)
-        text = sentences[0].strip()
-        # A single short sentence says nothing useful; pull in the next one.
-        for extra in sentences[1:]:
-            if len(text) >= DESCRIPTION_MIN:
-                break
-            text = f"{text} {extra.strip()}".strip()
-        if len(text) > DESCRIPTION_MAX:
-            trimmed = text[: DESCRIPTION_MAX - 1].rsplit(" ", 1)[0].rstrip(",;:")
-            return trimmed + ".", "truncated"
-        if len(text) >= DESCRIPTION_MIN:
-            return text, "derived"
-
-    return f"Reference documentation for {title} in the Gemma model documentation.", "fallback"
-
-
-def infer_models(rel: str) -> list[str]:
-    """Which models a page is about. An empty list means "not about a model" -- community,
-    tooling and site pages, where naming a model would be noise."""
-    parts = rel.split("/")
-
-    # `models/<family>/…` names a family; `models/releases.md` sits at the top level and is
-    # about the family as a whole, so the directory check has to be real.
-    if parts[0] == "models" and len(parts) > 2:
-        return [parts[1]]
-
-    if rel == "tutorials/visual-guides/diffusiongemma.md":
-        return ["diffusiongemma"]
-    if rel == "tutorials/visual-guides/gemma-4-12b.md":
-        return ["gemma-4/12b"]
-
-    # Audio is E2B/E4B/12B only -- the large sparse models take text, image and video.
-    if rel == "get-started/capabilities/audio.md":
-        return list(models_with_modality("audio"))
-
-    if parts[0] in {"get-started", "deployment", "tuning", "tutorials", "learn"}:
-        return ["gemma-4"]
-
-    return []
-
-
-# Engines and libraries a page is *about*, keyed by filename. Pages that merely mention a
-# framework in passing are left blank rather than guessed at from body text.
-PATH_FRAMEWORKS: dict[str, list[str]] = {
-    "deployment/ollama.md": ["ollama"],
-    "deployment/llamacpp.md": ["llamacpp"],
-    "deployment/lmstudio.md": ["lmstudio"],
-    "deployment/mlx.md": ["mlx"],
-    "deployment/huggingface.md": ["transformers"],
-    "deployment/keras.md": ["keras"],
-    "deployment/litert.md": ["litert"],
-    "deployment/web.md": ["webgpu", "transformers"],
-    "deployment/mobile.md": ["litert", "android", "ios"],
-    "deployment/vllm.md": ["vllm"],
-    "deployment/sglang.md": ["sglang"],
-    "deployment/google-cloud.md": ["google-cloud", "vllm"],
-    "deployment/langchain.md": ["langchain", "ollama"],
-    "tuning/qlora.md": ["transformers", "peft", "trl"],
-    "tuning/full-finetune.md": ["transformers", "trl"],
-    "tuning/vision-qlora.md": ["transformers", "peft", "trl"],
-    "tuning/keras-lora.md": ["keras"],
-    "tuning/distributed.md": ["jax", "keras"],
-    "models/embeddinggemma/inference.md": ["sentence-transformers", "transformers"],
-    "models/embeddinggemma/fine-tuning.md": ["sentence-transformers"],
-    "models/functiongemma/inference.md": ["transformers"],
-    "models/functiongemma/fine-tuning.md": ["transformers", "peft", "trl"],
-    "models/paligemma/inference.md": ["keras"],
-    "models/paligemma/fine-tuning.md": ["jax"],
-    "models/recurrentgemma/inference.md": ["jax"],
-    "models/recurrentgemma/fine-tuning.md": ["jax"],
-    "models/diffusiongemma/inference.md": ["transformers"],
-}
-
-
-def nav_labels() -> dict[str, str]:
-    """docs-relative path -> the hand-written nav label, where zensical.toml gives one."""
-    config = tomllib.loads((REPO_ROOT / "zensical.toml").read_text(encoding="utf-8"))
-    labels: dict[str, str] = {}
-
-    def walk(node, label: str | None = None) -> None:
-        if isinstance(node, str):
-            if node.endswith(".md") and label and label not in {"Overview", "Index"}:
-                labels.setdefault(node, label)
-        elif isinstance(node, list):
-            # A label attached to a *list* names the section, not any page inside it, so it
-            # must not propagate -- that is how `models/releases.md` was titled "Models".
-            for item in node:
-                walk(item)
-        elif isinstance(node, dict):
-            for key, value in node.items():
-                walk(value, key)
-
-    walk(config["project"]["nav"])
-    return labels
-
-
-# --- Driver -----------------------------------------------------------------------------------
-
-
-def process(path: Path, labels: dict[str, str]) -> tuple[str, dict[str, object], str]:
-    """Return (new text, keys added, description quality)."""
-    text = path.read_text(encoding="utf-8")
-    rel = path.relative_to(DOCS_DIR).as_posix()
-    block, rest = split_block(text)
-    segments = segment(block)
-    existing = parse(block)
-
-    additions: dict[str, object] = {}
-    quality = ""
-
-    title = existing.get("title") or infer_title(rest, rel, labels)
-    if "title" not in existing:
-        additions["title"] = title
-
-    if "description" not in existing:
-        additions["description"], quality = infer_description(rest, title)
-
-    if "models" not in existing:
-        inferred = infer_models(rel)
-        if inferred:  # an empty list means "not about a model" -- omit the key entirely
-            additions["models"] = inferred
-
-    if "frameworks" not in existing and rel in PATH_FRAMEWORKS:
-        additions["frameworks"] = PATH_FRAMEWORKS[rel]
-
-    source = ported_sources().get(rel) or existing.get("source_url")
-    if source and "source_url" not in existing:
-        additions["source_url"] = source
-
-    if not additions:
-        return text, {}, ""
-
-    return render(segments, additions) + rest, additions, quality
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--check", action="store_true", help="report without writing")
-    args = parser.parse_args()
-
-    labels = nav_labels()
-    touched = 0
-    needs_review: list[tuple[str, str]] = []
-    seen_descriptions: dict[str, str] = {}
-
-    for path in sorted(DOCS_DIR.rglob("*.md")):
-        rel = path.relative_to(DOCS_DIR).as_posix()
-        original = path.read_text(encoding="utf-8")
-        new_text, additions, quality = process(path, labels)
-        if not additions:
-            continue
-
-        # The body must survive untouched -- this script only ever edits the block.
-        if split_block(new_text)[1] != split_block(original)[1]:
-            print(f"ABORT {rel}: body would change", file=sys.stderr)
-            return 1
-
-        touched += 1
-        description = str(additions.get("description", ""))
-        if quality in {"truncated", "fallback"}:
-            needs_review.append((rel, quality))
-        elif description and description in seen_descriptions:
-            needs_review.append((rel, f"duplicate of {seen_descriptions[description]}"))
-        elif description:
-            seen_descriptions[description] = rel
-
-        print(f"{'would add' if args.check else 'added':>9}  {rel}: {', '.join(additions)}")
-        if not args.check:
-            path.write_text(new_text, encoding="utf-8")
-
-    print(f"\n{touched} pages updated; {len(needs_review)} descriptions need a human")
-    for rel, why in needs_review:
-        print(f"  {rel:55} {why}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scripts/frontmatter_schema.py b/scripts/frontmatter_schema.py
index 68c3267..8fccec7 100644
--- a/scripts/frontmatter_schema.py
+++ b/scripts/frontmatter_schema.py
@@ -1,6 +1,6 @@
 """The frontmatter schema every page in docs/ must satisfy.
 
-Single source of truth. `lint_docs.py` enforces it; `backfill_frontmatter.py` writes it.
+Single source of truth enforced by `lint_docs.py`.
 Documented for humans in PLAN.md, "Frontmatter schema".
 
 Two kinds of key live here, and the distinction is the whole design:
@@ -30,7 +30,7 @@
 DOCS_DIR = REPO_ROOT / "docs"
 MODELS_DATA = REPO_ROOT / "data" / "models.yml"
 FRAMEWORKS_DATA = REPO_ROOT / "data" / "frameworks.yml"
-SOURCES = Path(__file__).resolve().parent / "sources.toml"
+SOURCES = Path(__file__).resolve().parent / "importers" / "sources.toml"
 
 # Descriptions feed llms.txt, search results, and page cards. Too short says nothing;
 # too long gets truncated everywhere it is used.
diff --git a/scripts/check_fidelity.py b/scripts/importers/check_fidelity.py
similarity index 98%
rename from scripts/check_fidelity.py
rename to scripts/importers/check_fidelity.py
index 9f768df..5b44369 100644
--- a/scripts/check_fidelity.py
+++ b/scripts/importers/check_fidelity.py
@@ -7,8 +7,8 @@
 anything dropped. Word-level rather than line-level, because the converter legitimately reflows
 wrapped list items and re-indents code, but must never lose a word.
 
-    python scripts/check_fidelity.py <source.md.txt> <converted.md>
-    python scripts/check_fidelity.py --manifest            # every page in sources.toml
+    python scripts/importers/check_fidelity.py <source.md.txt> <converted.md>
+    python scripts/importers/check_fidelity.py --manifest  # every page in sources.toml
 
 Expected drops are metadata the converter is meant to strip (Colab badges, "View on
 ai.google.dev", `<br>`). Those are filtered by EXPECTED_DROPS; anything else is a finding.
diff --git a/scripts/convert_notebook.py b/scripts/importers/convert_notebook.py
similarity index 99%
rename from scripts/convert_notebook.py
rename to scripts/importers/convert_notebook.py
index 733b29b..9c82c24 100644
--- a/scripts/convert_notebook.py
+++ b/scripts/importers/convert_notebook.py
@@ -27,7 +27,7 @@
                                [--assets-dir docs/assets/<section>] [--assets-url ../assets/<section>]
 
 Tests:
-    python scripts/test_convert_notebook.py
+    python tests/test_convert_notebook.py
 """
 
 import re
diff --git a/scripts/import_newsletter.py b/scripts/importers/import_newsletter.py
similarity index 98%
rename from scripts/import_newsletter.py
rename to scripts/importers/import_newsletter.py
index da66d34..6c40fdb 100644
--- a/scripts/import_newsletter.py
+++ b/scripts/importers/import_newsletter.py
@@ -2,7 +2,7 @@
 """
 Import a visual guide from newsletter.maartengrootendorst.com as markdown.
 
-    python scripts/import_newsletter.py <post-url> <output.md> --title "A Visual Guide to X"
+    python scripts/importers/import_newsletter.py <post-url> <output.md> --title "A Visual Guide to X"
 
 Extracts the article body verbatim -- headings, prose, lists, quotes and images -- rather than
 summarising it. The guides are Maarten's own writing, so the words have to survive the import
diff --git a/scripts/link_map.toml b/scripts/importers/link_map.toml
similarity index 99%
rename from scripts/link_map.toml
rename to scripts/importers/link_map.toml
index acc9c2d..5ca1562 100644
--- a/scripts/link_map.toml
+++ b/scripts/importers/link_map.toml
@@ -1,7 +1,7 @@
 # Upstream documentation path -> local page.
 #
 # Keys are the path after "https://ai.google.dev/gemma/docs" (http/https and #fragments are
-# handled by the rewriter). Values are paths under docs/. Consumed by scripts/rewrite_links.py.
+# handled by the rewriter). Values are paths under docs/. Consumed by `rewrite_links.py`.
 #
 # Not listed here on purpose:
 #   - URLs inside code blocks (sample data, BibTeX citations) stay external; see check_links.
diff --git a/scripts/port_docs.py b/scripts/importers/port_docs.py
similarity index 92%
rename from scripts/port_docs.py
rename to scripts/importers/port_docs.py
index b49a78c..232f9cb 100644
--- a/scripts/port_docs.py
+++ b/scripts/importers/port_docs.py
@@ -1,14 +1,14 @@
 #!/usr/bin/env python3
 """
-Batch-port upstream pages listed in scripts/sources.toml.
+Batch-port upstream pages listed in `scripts/importers/sources.toml`.
 
 Only pages in the manifest are touched, and only those not marked `frozen = true`.
 A page absent from the manifest, or frozen in it, is hand-maintained and must never be
 re-converted -- see "Hand-maintained sections" in PLAN.md.
 
-    python scripts/port_docs.py --dry-run              # show what would change
-    python scripts/port_docs.py --filter models/       # port one section
-    python scripts/port_docs.py                        # port everything in the manifest
+    python scripts/importers/port_docs.py --dry-run              # show what would change
+    python scripts/importers/port_docs.py --filter models/       # port one section
+    python scripts/importers/port_docs.py                        # port everything in the manifest
 
 Every page is fidelity-checked after conversion. A page that drops prose is reported and, unless
 --force is given, left unwritten -- a bad conversion never lands on disk.
@@ -122,7 +122,7 @@ def main() -> int:
         # The linter's `images` check is at zero, so a missed run fails CI -- but say it here
         # rather than making someone discover it from a red build.
         print("\nNext: re-vendor images, which this run reverted to upstream URLs:")
-        print("  uv run python scripts/vendor_images.py")
+        print("  uv run python scripts/importers/vendor_images.py")
     if failures:
         print("\nFidelity failures (not written):")
         for path in failures:
diff --git a/scripts/rewrite_links.py b/scripts/importers/rewrite_links.py
similarity index 95%
rename from scripts/rewrite_links.py
rename to scripts/importers/rewrite_links.py
index 7ee234d..85a57ff 100644
--- a/scripts/rewrite_links.py
+++ b/scripts/importers/rewrite_links.py
@@ -1,10 +1,10 @@
 #!/usr/bin/env python3
 """
-Rewrite upstream ai.google.dev links to local page paths, per scripts/link_map.toml.
+Rewrite upstream ai.google.dev links to local page paths, per `scripts/importers/link_map.toml`.
 
-    python scripts/rewrite_links.py --dry-run
-    python scripts/rewrite_links.py
-    python scripts/rewrite_links.py --report      # unmapped targets only, no changes
+    python scripts/importers/rewrite_links.py --dry-run
+    python scripts/importers/rewrite_links.py
+    python scripts/importers/rewrite_links.py --report      # unmapped targets only, no changes
 
 Three behaviours, driven by the map:
   [ported]   -> relative link to an existing page
@@ -29,7 +29,7 @@
 from collections import Counter
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).resolve().parent))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
 
 from lint_docs import load_pages, outside_code  # noqa: E402
 
@@ -238,7 +238,7 @@ def replace(match: re.Match) -> str:
         for path, count in unmapped.most_common():
             print(f"  {count:3}  {path}")
         if unmapped:
-            print("\nAdd these to scripts/link_map.toml and re-run.")
+            print("\nAdd these to scripts/importers/link_map.toml and re-run.")
 
     if not args.report:
         verb = "would rewrite" if args.dry_run else "rewrote"
diff --git a/scripts/sources.toml b/scripts/importers/sources.toml
similarity index 98%
rename from scripts/sources.toml
rename to scripts/importers/sources.toml
index 27bd76b..fe836c1 100644
--- a/scripts/sources.toml
+++ b/scripts/importers/sources.toml
@@ -1,6 +1,6 @@
 # Upstream source for every ported page.
 #
-# Consumed by scripts/port_docs.py and scripts/check_fidelity.py, and the origin of the
+# Consumed by `scripts/importers/port_docs.py` and `check_fidelity.py`, and the origin of the
 # `source_url` frontmatter added in PLAN.md Step 3.1.
 #
 #   frozen = true  -> HAND-MAINTAINED. Never re-convert; the file on disk is the source of
@@ -353,7 +353,7 @@ source = "https://ai.google.dev/gemma/docs/gemma_scope.md.txt"
 frozen = true
 
 # --- Visual guides ------------------------------------------------------------------------
-# Imported with scripts/import_newsletter.py, not convert_notebook.py.
+# Imported with `scripts/importers/import_newsletter.py`, not `convert_notebook.py`.
 # gemma-4.md and mtp.md were split by hand after import -- re-importing would undo that.
 
 [[page]]
diff --git a/scripts/vendor_images.py b/scripts/importers/vendor_images.py
similarity index 95%
rename from scripts/vendor_images.py
rename to scripts/importers/vendor_images.py
index b8a2210..7571198 100644
--- a/scripts/vendor_images.py
+++ b/scripts/importers/vendor_images.py
@@ -6,8 +6,8 @@
 Google's CDN staying put, blocks offline builds, and leaves no way to add dark-mode variants.
 This vendors them.
 
-    python scripts/vendor_images.py --dry-run    # show what would be downloaded
-    python scripts/vendor_images.py              # download and rewrite
+    python scripts/importers/vendor_images.py --dry-run    # show what would be downloaded
+    python scripts/importers/vendor_images.py              # download and rewrite
 
 Assets are grouped by the page's parent directory, matching the layout port_docs.py already
 uses (docs/assets/paligemma/, docs/assets/diffusiongemma/, ...). Original filenames are kept
@@ -28,7 +28,7 @@
 import urllib.request
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).resolve().parent))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
 
 from lint_docs import load_pages, outside_code  # noqa: E402
 
diff --git a/scripts/fixtures/admonitions.expected.md b/tests/fixtures/admonitions.expected.md
similarity index 100%
rename from scripts/fixtures/admonitions.expected.md
rename to tests/fixtures/admonitions.expected.md
diff --git a/scripts/fixtures/admonitions.md.txt b/tests/fixtures/admonitions.md.txt
similarity index 100%
rename from scripts/fixtures/admonitions.md.txt
rename to tests/fixtures/admonitions.md.txt
diff --git a/scripts/fixtures/base64_image.expected.md b/tests/fixtures/base64_image.expected.md
similarity index 100%
rename from scripts/fixtures/base64_image.expected.md
rename to tests/fixtures/base64_image.expected.md
diff --git a/scripts/fixtures/base64_image.md.txt b/tests/fixtures/base64_image.md.txt
similarity index 100%
rename from scripts/fixtures/base64_image.md.txt
rename to tests/fixtures/base64_image.md.txt
diff --git a/scripts/fixtures/list_continuation.expected.md b/tests/fixtures/list_continuation.expected.md
similarity index 100%
rename from scripts/fixtures/list_continuation.expected.md
rename to tests/fixtures/list_continuation.expected.md
diff --git a/scripts/fixtures/list_continuation.md.txt b/tests/fixtures/list_continuation.md.txt
similarity index 100%
rename from scripts/fixtures/list_continuation.md.txt
rename to tests/fixtures/list_continuation.md.txt
diff --git a/scripts/fixtures/prompt_template.expected.md b/tests/fixtures/prompt_template.expected.md
similarity index 100%
rename from scripts/fixtures/prompt_template.expected.md
rename to tests/fixtures/prompt_template.expected.md
diff --git a/scripts/fixtures/prompt_template.md.txt b/tests/fixtures/prompt_template.md.txt
similarity index 100%
rename from scripts/fixtures/prompt_template.md.txt
rename to tests/fixtures/prompt_template.md.txt
diff --git a/scripts/fixtures/split_code_block.expected.md b/tests/fixtures/split_code_block.expected.md
similarity index 100%
rename from scripts/fixtures/split_code_block.expected.md
rename to tests/fixtures/split_code_block.expected.md
diff --git a/scripts/fixtures/split_code_block.md.txt b/tests/fixtures/split_code_block.md.txt
similarity index 100%
rename from scripts/fixtures/split_code_block.md.txt
rename to tests/fixtures/split_code_block.md.txt
diff --git a/scripts/test_agent_docs.py b/tests/test_agent_docs.py
similarity index 94%
rename from scripts/test_agent_docs.py
rename to tests/test_agent_docs.py
index af75536..a3fcbd8 100644
--- a/scripts/test_agent_docs.py
+++ b/tests/test_agent_docs.py
@@ -4,7 +4,7 @@
 import unittest
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).resolve().parent))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
 import agent_docs
 
 
diff --git a/scripts/test_convert_notebook.py b/tests/test_convert_notebook.py
similarity index 95%
rename from scripts/test_convert_notebook.py
rename to tests/test_convert_notebook.py
index 717fd33..d94bbe4 100644
--- a/scripts/test_convert_notebook.py
+++ b/tests/test_convert_notebook.py
@@ -2,13 +2,13 @@
 """
 Golden tests for convert_notebook.py.
 
-Each fixture in scripts/fixtures/ pairs a `.md.txt` source with the `.expected.md` output the
+Each fixture in `tests/fixtures/` pairs a `.md.txt` source with the `.expected.md` output the
 converter must produce. Fixtures are minimised reproductions of real defects found in the port
 -- see PLAN.md Step 0.2. Named regression tests sit alongside the golden comparison so a failure
 says *which* defect came back rather than just "output differs".
 
-    python scripts/test_convert_notebook.py            # run
-    python scripts/test_convert_notebook.py --update   # regenerate .expected.md, then REVIEW the diff
+    python tests/test_convert_notebook.py            # run
+    python tests/test_convert_notebook.py --update   # regenerate .expected.md, then REVIEW the diff
 
 Stdlib only, so CI needs no install step.
 """
@@ -20,7 +20,7 @@
 import unittest
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).resolve().parent))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts" / "importers"))
 
 from convert_notebook import convert  # noqa: E402
 
diff --git a/scripts/test_notebooks.py b/tests/test_notebooks.py
similarity index 97%
rename from scripts/test_notebooks.py
rename to tests/test_notebooks.py
index 0b36ebc..7914dee 100644
--- a/scripts/test_notebooks.py
+++ b/tests/test_notebooks.py
@@ -6,7 +6,7 @@
 import unittest
 from pathlib import Path
 
-sys.path.insert(0, str(Path(__file__).resolve().parent))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts"))
 import notebooks
 
 

From 5004f63ec5b50e5d8ce184e798afaa1ffce43fd2 Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 10 Aug 2026 23:11:55 +0200
Subject: [PATCH 19/20] Fix tests

---
 PLAN.md                 |   6 +-
 README.md               |   4 +-
 docs/page-metadata.json | 545 ----------------------------------------
 scripts/agent_docs.py   |  47 +---
 4 files changed, 8 insertions(+), 594 deletions(-)
 delete mode 100644 docs/page-metadata.json

diff --git a/PLAN.md b/PLAN.md
index c0a5e0e..8eaabb3 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -47,8 +47,8 @@ code, but after its one-time import the Markdown owns all subsequent edits.
 
 ### Other generated artifacts
 
-- [ ] Remove `docs/page-metadata.json` unless a concrete consumer is introduced. Nothing reads it
-      today, and its Git-derived update dates make ordinary documentation commits appear stale.
+- [x] Remove `docs/page-metadata.json`. Nothing consumed it, and its Git-derived update dates made
+      ordinary documentation commits appear stale only after the commit already existed.
 - [ ] Decide whether `docs/llms.txt` remains committed for local `zensical serve` previews or is
       written only into `site/`. It changes only with navigation and frontmatter, so either choice
       is defensible; document the chosen behavior in `README.md`.
@@ -132,7 +132,7 @@ The directory layout separates production infrastructure, optional import utilit
 | File | Responsibility |
 |---|---|
 | `build_docs.py` | Production entry point: generators, Zensical build and raw Markdown publication. |
-| `agent_docs.py` | Builds `llms.txt` and raw `.md` routes. Remove page-metadata generation with the unused artifact. |
+| `agent_docs.py` | Builds `llms.txt` and raw `.md` routes. |
 | `lint_docs.py` | CI gate for documentation and repository invariants. |
 | `frontmatter_schema.py` | Shared frontmatter schema and controlled vocabularies. |
 | `generate_model_names.py` | Compiles model/framework data for templates and the browser selector. |
diff --git a/README.md b/README.md
index 16d3829..c55ef10 100644
--- a/README.md
+++ b/README.md
@@ -29,8 +29,8 @@ uv run --frozen python scripts/build_docs.py
 ```
 
 `build_docs.py` runs the normal Zensical build and then publishes raw Markdown routes such as
-`/get-started/quickstart.md`. The committed `docs/llms.txt` and `docs/page-metadata.json` are
-regenerated from page frontmatter and verified by the `generated` lint check.
+`/get-started/quickstart.md`. The committed `docs/llms.txt` is regenerated from page
+frontmatter and verified by the `generated` lint check.
 
 See `PLAN.md` for the migration roadmap and `docs/contributing/components.md`
 for the CSS tokens and reusable components.
diff --git a/docs/page-metadata.json b/docs/page-metadata.json
deleted file mode 100644
index a916e48..0000000
--- a/docs/page-metadata.json
+++ /dev/null
@@ -1,545 +0,0 @@
-{
-  "generated_at": "build-time",
-  "pages": {
-    "community/discord.md": {
-      "url": "/community/discord/",
-      "markdown_url": "/community/discord.md",
-      "estimated_tokens": 10,
-      "last_updated": "2026-08-05"
-    },
-    "community/gemma-skills.md": {
-      "url": "/community/gemma-skills/",
-      "markdown_url": "/community/gemma-skills.md",
-      "estimated_tokens": 15,
-      "last_updated": "2026-08-05"
-    },
-    "community/gemmaverse.md": {
-      "url": "/community/gemmaverse/",
-      "markdown_url": "/community/gemmaverse.md",
-      "estimated_tokens": 208,
-      "last_updated": "2026-08-05"
-    },
-    "contributing/components.md": {
-      "url": "/contributing/components/",
-      "markdown_url": "/contributing/components.md",
-      "estimated_tokens": 2677,
-      "last_updated": "2026-08-10"
-    },
-    "cookbook.md": {
-      "url": "/cookbook/",
-      "markdown_url": "/cookbook.md",
-      "estimated_tokens": 143,
-      "last_updated": "2026-08-05"
-    },
-    "demos/vision-token-budget.md": {
-      "url": "/demos/vision-token-budget/",
-      "markdown_url": "/demos/vision-token-budget.md",
-      "estimated_tokens": 146,
-      "last_updated": "2026-08-10"
-    },
-    "deployment/google-cloud.md": {
-      "url": "/deployment/google-cloud/",
-      "markdown_url": "/deployment/google-cloud.md",
-      "estimated_tokens": 929,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/huggingface.md": {
-      "url": "/deployment/huggingface/",
-      "markdown_url": "/deployment/huggingface.md",
-      "estimated_tokens": 2963,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/index.md": {
-      "url": "/deployment/",
-      "markdown_url": "/deployment.md",
-      "estimated_tokens": 2540,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/keras.md": {
-      "url": "/deployment/keras/",
-      "markdown_url": "/deployment/keras.md",
-      "estimated_tokens": 6212,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/langchain.md": {
-      "url": "/deployment/langchain/",
-      "markdown_url": "/deployment/langchain.md",
-      "estimated_tokens": 3241,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/litert.md": {
-      "url": "/deployment/litert/",
-      "markdown_url": "/deployment/litert.md",
-      "estimated_tokens": 1703,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/llamacpp.md": {
-      "url": "/deployment/llamacpp/",
-      "markdown_url": "/deployment/llamacpp.md",
-      "estimated_tokens": 803,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/lmstudio.md": {
-      "url": "/deployment/lmstudio/",
-      "markdown_url": "/deployment/lmstudio.md",
-      "estimated_tokens": 443,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/mlx.md": {
-      "url": "/deployment/mlx/",
-      "markdown_url": "/deployment/mlx.md",
-      "estimated_tokens": 586,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/mobile.md": {
-      "url": "/deployment/mobile/",
-      "markdown_url": "/deployment/mobile.md",
-      "estimated_tokens": 448,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/ollama.md": {
-      "url": "/deployment/ollama/",
-      "markdown_url": "/deployment/ollama.md",
-      "estimated_tokens": 763,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/qat.md": {
-      "url": "/deployment/qat/",
-      "markdown_url": "/deployment/qat.md",
-      "estimated_tokens": 710,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/sglang.md": {
-      "url": "/deployment/sglang/",
-      "markdown_url": "/deployment/sglang.md",
-      "estimated_tokens": 379,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/vllm.md": {
-      "url": "/deployment/vllm/",
-      "markdown_url": "/deployment/vllm.md",
-      "estimated_tokens": 498,
-      "last_updated": "2026-08-05"
-    },
-    "deployment/web.md": {
-      "url": "/deployment/web/",
-      "markdown_url": "/deployment/web.md",
-      "estimated_tokens": 217,
-      "last_updated": "2026-08-05"
-    },
-    "get-started/capabilities/audio.md": {
-      "url": "/get-started/capabilities/audio/",
-      "markdown_url": "/get-started/capabilities/audio.md",
-      "estimated_tokens": 3814,
-      "last_updated": "2026-08-10"
-    },
-    "get-started/capabilities/function-calling.md": {
-      "url": "/get-started/capabilities/function-calling/",
-      "markdown_url": "/get-started/capabilities/function-calling.md",
-      "estimated_tokens": 5585,
-      "last_updated": "2026-08-10"
-    },
-    "get-started/capabilities/image.md": {
-      "url": "/get-started/capabilities/image/",
-      "markdown_url": "/get-started/capabilities/image.md",
-      "estimated_tokens": 5055,
-      "last_updated": "2026-08-10"
-    },
-    "get-started/capabilities/mtp.md": {
-      "url": "/get-started/capabilities/mtp/",
-      "markdown_url": "/get-started/capabilities/mtp.md",
-      "estimated_tokens": 530,
-      "last_updated": "2026-08-05"
-    },
-    "get-started/capabilities/text.md": {
-      "url": "/get-started/capabilities/text/",
-      "markdown_url": "/get-started/capabilities/text.md",
-      "estimated_tokens": 2560,
-      "last_updated": "2026-08-10"
-    },
-    "get-started/capabilities/thinking.md": {
-      "url": "/get-started/capabilities/thinking/",
-      "markdown_url": "/get-started/capabilities/thinking.md",
-      "estimated_tokens": 6288,
-      "last_updated": "2026-08-10"
-    },
-    "get-started/capabilities/video.md": {
-      "url": "/get-started/capabilities/video/",
-      "markdown_url": "/get-started/capabilities/video.md",
-      "estimated_tokens": 12092,
-      "last_updated": "2026-08-10"
-    },
-    "get-started/capabilities/vision.md": {
-      "url": "/get-started/capabilities/vision/",
-      "markdown_url": "/get-started/capabilities/vision.md",
-      "estimated_tokens": 980,
-      "last_updated": "2026-08-05"
-    },
-    "get-started/prompt-formatting.md": {
-      "url": "/get-started/prompt-formatting/",
-      "markdown_url": "/get-started/prompt-formatting.md",
-      "estimated_tokens": 3773,
-      "last_updated": "2026-08-05"
-    },
-    "get-started/prompt-structure.md": {
-      "url": "/get-started/prompt-structure/",
-      "markdown_url": "/get-started/prompt-structure.md",
-      "estimated_tokens": 477,
-      "last_updated": "2026-08-05"
-    },
-    "get-started/quickstart.md": {
-      "url": "/get-started/quickstart/",
-      "markdown_url": "/get-started/quickstart.md",
-      "estimated_tokens": 565,
-      "last_updated": "2026-08-05"
-    },
-    "index.md": {
-      "url": "/",
-      "markdown_url": "/index.md",
-      "estimated_tokens": 1,
-      "last_updated": "2026-08-05"
-    },
-    "learn/business-email-assistant.md": {
-      "url": "/learn/business-email-assistant/",
-      "markdown_url": "/learn/business-email-assistant.md",
-      "estimated_tokens": 4438,
-      "last_updated": "2026-08-05"
-    },
-    "learn/index.md": {
-      "url": "/learn/",
-      "markdown_url": "/learn.md",
-      "estimated_tokens": 325,
-      "last_updated": "2026-08-10"
-    },
-    "learn/personal-code-assistant.md": {
-      "url": "/learn/personal-code-assistant/",
-      "markdown_url": "/learn/personal-code-assistant.md",
-      "estimated_tokens": 5079,
-      "last_updated": "2026-08-05"
-    },
-    "models/datagemma/index.md": {
-      "url": "/models/datagemma/",
-      "markdown_url": "/models/datagemma.md",
-      "estimated_tokens": 641,
-      "last_updated": "2026-08-05"
-    },
-    "models/diffusiongemma/explained.md": {
-      "url": "/models/diffusiongemma/explained/",
-      "markdown_url": "/models/diffusiongemma/explained.md",
-      "estimated_tokens": 1360,
-      "last_updated": "2026-08-05"
-    },
-    "models/diffusiongemma/index.md": {
-      "url": "/models/diffusiongemma/",
-      "markdown_url": "/models/diffusiongemma.md",
-      "estimated_tokens": 1080,
-      "last_updated": "2026-08-05"
-    },
-    "models/diffusiongemma/inference.md": {
-      "url": "/models/diffusiongemma/inference/",
-      "markdown_url": "/models/diffusiongemma/inference.md",
-      "estimated_tokens": 14213,
-      "last_updated": "2026-08-05"
-    },
-    "models/diffusiongemma/model-card.md": {
-      "url": "/models/diffusiongemma/model-card/",
-      "markdown_url": "/models/diffusiongemma/model-card.md",
-      "estimated_tokens": 3826,
-      "last_updated": "2026-08-05"
-    },
-    "models/embeddinggemma/fine-tuning.md": {
-      "url": "/models/embeddinggemma/fine-tuning/",
-      "markdown_url": "/models/embeddinggemma/fine-tuning.md",
-      "estimated_tokens": 2473,
-      "last_updated": "2026-08-05"
-    },
-    "models/embeddinggemma/index.md": {
-      "url": "/models/embeddinggemma/",
-      "markdown_url": "/models/embeddinggemma.md",
-      "estimated_tokens": 687,
-      "last_updated": "2026-08-05"
-    },
-    "models/embeddinggemma/inference.md": {
-      "url": "/models/embeddinggemma/inference/",
-      "markdown_url": "/models/embeddinggemma/inference.md",
-      "estimated_tokens": 3825,
-      "last_updated": "2026-08-05"
-    },
-    "models/embeddinggemma/model-card.md": {
-      "url": "/models/embeddinggemma/model-card/",
-      "markdown_url": "/models/embeddinggemma/model-card.md",
-      "estimated_tokens": 2793,
-      "last_updated": "2026-08-05"
-    },
-    "models/functiongemma/fine-tuning.md": {
-      "url": "/models/functiongemma/fine-tuning/",
-      "markdown_url": "/models/functiongemma/fine-tuning.md",
-      "estimated_tokens": 8622,
-      "last_updated": "2026-08-05"
-    },
-    "models/functiongemma/formatting.md": {
-      "url": "/models/functiongemma/formatting/",
-      "markdown_url": "/models/functiongemma/formatting.md",
-      "estimated_tokens": 2133,
-      "last_updated": "2026-08-05"
-    },
-    "models/functiongemma/full-sequence.md": {
-      "url": "/models/functiongemma/full-sequence/",
-      "markdown_url": "/models/functiongemma/full-sequence.md",
-      "estimated_tokens": 3073,
-      "last_updated": "2026-08-05"
-    },
-    "models/functiongemma/index.md": {
-      "url": "/models/functiongemma/",
-      "markdown_url": "/models/functiongemma.md",
-      "estimated_tokens": 678,
-      "last_updated": "2026-08-05"
-    },
-    "models/functiongemma/inference.md": {
-      "url": "/models/functiongemma/inference/",
-      "markdown_url": "/models/functiongemma/inference.md",
-      "estimated_tokens": 1993,
-      "last_updated": "2026-08-05"
-    },
-    "models/functiongemma/model-card.md": {
-      "url": "/models/functiongemma/model-card/",
-      "markdown_url": "/models/functiongemma/model-card.md",
-      "estimated_tokens": 3271,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-1/index.md": {
-      "url": "/models/gemma-1/",
-      "markdown_url": "/models/gemma-1.md",
-      "estimated_tokens": 3789,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-2/index.md": {
-      "url": "/models/gemma-2/",
-      "markdown_url": "/models/gemma-2.md",
-      "estimated_tokens": 3917,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-3/index.md": {
-      "url": "/models/gemma-3/",
-      "markdown_url": "/models/gemma-3.md",
-      "estimated_tokens": 6076,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-3n/index.md": {
-      "url": "/models/gemma-3n/",
-      "markdown_url": "/models/gemma-3n.md",
-      "estimated_tokens": 1572,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-3n/model-card.md": {
-      "url": "/models/gemma-3n/model-card/",
-      "markdown_url": "/models/gemma-3n/model-card.md",
-      "estimated_tokens": 4013,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-4/index.md": {
-      "url": "/models/gemma-4/",
-      "markdown_url": "/models/gemma-4.md",
-      "estimated_tokens": 2599,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-4/model-card.md": {
-      "url": "/models/gemma-4/model-card/",
-      "markdown_url": "/models/gemma-4/model-card.md",
-      "estimated_tokens": 4941,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-aps/index.md": {
-      "url": "/models/gemma-aps/",
-      "markdown_url": "/models/gemma-aps.md",
-      "estimated_tokens": 383,
-      "last_updated": "2026-08-05"
-    },
-    "models/gemma-scope/index.md": {
-      "url": "/models/gemma-scope/",
-      "markdown_url": "/models/gemma-scope.md",
-      "estimated_tokens": 448,
-      "last_updated": "2026-08-05"
-    },
-    "models/index.md": {
-      "url": "/models/",
-      "markdown_url": "/models.md",
-      "estimated_tokens": 441,
-      "last_updated": "2026-08-05"
-    },
-    "models/paligemma/fine-tuning.md": {
-      "url": "/models/paligemma/fine-tuning/",
-      "markdown_url": "/models/paligemma/fine-tuning.md",
-      "estimated_tokens": 12120,
-      "last_updated": "2026-08-05"
-    },
-    "models/paligemma/index.md": {
-      "url": "/models/paligemma/",
-      "markdown_url": "/models/paligemma.md",
-      "estimated_tokens": 605,
-      "last_updated": "2026-08-05"
-    },
-    "models/paligemma/inference.md": {
-      "url": "/models/paligemma/inference/",
-      "markdown_url": "/models/paligemma/inference.md",
-      "estimated_tokens": 3312,
-      "last_updated": "2026-08-05"
-    },
-    "models/paligemma/model-card-v1.md": {
-      "url": "/models/paligemma/model-card-v1/",
-      "markdown_url": "/models/paligemma/model-card-v1.md",
-      "estimated_tokens": 4647,
-      "last_updated": "2026-08-05"
-    },
-    "models/paligemma/model-card-v2.md": {
-      "url": "/models/paligemma/model-card-v2/",
-      "markdown_url": "/models/paligemma/model-card-v2.md",
-      "estimated_tokens": 6330,
-      "last_updated": "2026-08-05"
-    },
-    "models/paligemma/prompting.md": {
-      "url": "/models/paligemma/prompting/",
-      "markdown_url": "/models/paligemma/prompting.md",
-      "estimated_tokens": 908,
-      "last_updated": "2026-08-05"
-    },
-    "models/recurrentgemma/fine-tuning.md": {
-      "url": "/models/recurrentgemma/fine-tuning/",
-      "markdown_url": "/models/recurrentgemma/fine-tuning.md",
-      "estimated_tokens": 8212,
-      "last_updated": "2026-08-05"
-    },
-    "models/recurrentgemma/index.md": {
-      "url": "/models/recurrentgemma/",
-      "markdown_url": "/models/recurrentgemma.md",
-      "estimated_tokens": 299,
-      "last_updated": "2026-08-05"
-    },
-    "models/recurrentgemma/inference.md": {
-      "url": "/models/recurrentgemma/inference/",
-      "markdown_url": "/models/recurrentgemma/inference.md",
-      "estimated_tokens": 2464,
-      "last_updated": "2026-08-05"
-    },
-    "models/recurrentgemma/model-card.md": {
-      "url": "/models/recurrentgemma/model-card/",
-      "markdown_url": "/models/recurrentgemma/model-card.md",
-      "estimated_tokens": 2974,
-      "last_updated": "2026-08-05"
-    },
-    "models/releases.md": {
-      "url": "/models/releases/",
-      "markdown_url": "/models/releases.md",
-      "estimated_tokens": 1021,
-      "last_updated": "2026-08-05"
-    },
-    "models/shieldgemma/index.md": {
-      "url": "/models/shieldgemma/",
-      "markdown_url": "/models/shieldgemma.md",
-      "estimated_tokens": 563,
-      "last_updated": "2026-08-05"
-    },
-    "models/shieldgemma/model-card-v1.md": {
-      "url": "/models/shieldgemma/model-card-v1/",
-      "markdown_url": "/models/shieldgemma/model-card-v1.md",
-      "estimated_tokens": 2504,
-      "last_updated": "2026-08-05"
-    },
-    "models/shieldgemma/model-card-v2.md": {
-      "url": "/models/shieldgemma/model-card-v2/",
-      "markdown_url": "/models/shieldgemma/model-card-v2.md",
-      "estimated_tokens": 1989,
-      "last_updated": "2026-08-05"
-    },
-    "tools/index.md": {
-      "url": "/tools/",
-      "markdown_url": "/tools.md",
-      "estimated_tokens": 33,
-      "last_updated": "2026-08-05"
-    },
-    "tools/model-selector.md": {
-      "url": "/tools/model-selector/",
-      "markdown_url": "/tools/model-selector.md",
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-      "last_updated": "2026-08-05"
-    },
-    "tools/vram-calculator.md": {
-      "url": "/tools/vram-calculator/",
-      "markdown_url": "/tools/vram-calculator.md",
-      "estimated_tokens": 278,
-      "last_updated": "2026-08-05"
-    },
-    "tuning/distributed.md": {
-      "url": "/tuning/distributed/",
-      "markdown_url": "/tuning/distributed.md",
-      "estimated_tokens": 7031,
-      "last_updated": "2026-08-05"
-    },
-    "tuning/full-finetune.md": {
-      "url": "/tuning/full-finetune/",
-      "markdown_url": "/tuning/full-finetune.md",
-      "estimated_tokens": 4869,
-      "last_updated": "2026-08-10"
-    },
-    "tuning/keras-lora.md": {
-      "url": "/tuning/keras-lora/",
-      "markdown_url": "/tuning/keras-lora.md",
-      "estimated_tokens": 3957,
-      "last_updated": "2026-08-05"
-    },
-    "tuning/mobile-actions.md": {
-      "url": "/tuning/mobile-actions/",
-      "markdown_url": "/tuning/mobile-actions.md",
-      "estimated_tokens": 928,
-      "last_updated": "2026-08-05"
-    },
-    "tuning/overview.md": {
-      "url": "/tuning/overview/",
-      "markdown_url": "/tuning/overview.md",
-      "estimated_tokens": 1518,
-      "last_updated": "2026-08-05"
-    },
-    "tuning/qlora.md": {
-      "url": "/tuning/qlora/",
-      "markdown_url": "/tuning/qlora.md",
-      "estimated_tokens": 6042,
-      "last_updated": "2026-08-10"
-    },
-    "tuning/spoken-language.md": {
-      "url": "/tuning/spoken-language/",
-      "markdown_url": "/tuning/spoken-language.md",
-      "estimated_tokens": 4873,
-      "last_updated": "2026-08-05"
-    },
-    "tuning/vision-qlora.md": {
-      "url": "/tuning/vision-qlora/",
-      "markdown_url": "/tuning/vision-qlora.md",
-      "estimated_tokens": 5232,
-      "last_updated": "2026-08-10"
-    },
-    "tutorials/visual-guides/diffusiongemma.md": {
-      "url": "/tutorials/visual-guides/diffusiongemma/",
-      "markdown_url": "/tutorials/visual-guides/diffusiongemma.md",
-      "estimated_tokens": 9535,
-      "last_updated": "2026-08-05"
-    },
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-      "url": "/tutorials/visual-guides/gemma-4-12b/",
-      "markdown_url": "/tutorials/visual-guides/gemma-4-12b.md",
-      "estimated_tokens": 3530,
-      "last_updated": "2026-08-05"
-    },
-    "tutorials/visual-guides/gemma-4.md": {
-      "url": "/tutorials/visual-guides/gemma-4/",
-      "markdown_url": "/tutorials/visual-guides/gemma-4.md",
-      "estimated_tokens": 8765,
-      "last_updated": "2026-08-05"
-    },
-    "tutorials/visual-guides/mtp.md": {
-      "url": "/tutorials/visual-guides/mtp/",
-      "markdown_url": "/tutorials/visual-guides/mtp.md",
-      "estimated_tokens": 3102,
-      "last_updated": "2026-08-05"
-    }
-  }
-}
diff --git a/scripts/agent_docs.py b/scripts/agent_docs.py
index 17d9565..ef5bb1e 100644
--- a/scripts/agent_docs.py
+++ b/scripts/agent_docs.py
@@ -1,19 +1,16 @@
 #!/usr/bin/env python3
 """Generate agent-facing documentation artifacts from the canonical Markdown pages.
 
-The committed outputs (`docs/llms.txt` and `docs/page-metadata.json`) work with the normal
-Zensical preview. After a site build, `--publish site` also writes raw Markdown beside the
+The committed `docs/llms.txt` works with the normal Zensical preview. After a site build,
+`--publish site` also writes raw Markdown beside the
 rendered HTML using Stripe-style URLs such as `/get-started/quickstart.md`.
 """
 
 from __future__ import annotations
 
 import argparse
-import json
 import re
-import subprocess
 import tomllib
-from datetime import date
 from pathlib import Path
 from typing import Iterable
 
@@ -25,10 +22,8 @@
 CONFIG_FILE = REPO_ROOT / "zensical.toml"
 MODELS_FILE = REPO_ROOT / "data" / "models.yml"
 LLMS_TARGET = DOCS_DIR / "llms.txt"
-METADATA_TARGET = DOCS_DIR / "page-metadata.json"
 SITE_URL = "https://gemma.ai"
 
-TOKEN = re.compile(r"\w+|[^\w\s]", re.UNICODE)
 H1 = re.compile(r"^#\s+", re.MULTILINE)
 
 
@@ -99,46 +94,10 @@ def display_title(metadata: dict, family_names: dict[str, str]) -> str:
     return title
 
 
-def last_updated(path: Path) -> str:
-    relative = path.relative_to(REPO_ROOT).as_posix()
-    result = subprocess.run(
-        ["git", "log", "-1", "--format=%cs", "--", relative],
-        cwd=REPO_ROOT,
-        capture_output=True,
-        text=True,
-        check=False,
-    )
-    value = result.stdout.strip()
-    if value:
-        return value
-    return date.fromtimestamp(path.stat().st_mtime).isoformat()
-
-
-def estimated_tokens(body: str) -> int:
-    return max(1, len(TOKEN.findall(body)))
-
-
 def model_data() -> dict:
     return yaml.safe_load(MODELS_FILE.read_text(encoding="utf-8"))
 
 
-def render_metadata() -> str:
-    pages = source_pages()
-    payload = {
-        "generated_at": "build-time",
-        "pages": {
-            relative: {
-                "url": route_for(relative),
-                "markdown_url": markdown_route_for(relative),
-                "estimated_tokens": estimated_tokens(body),
-                "last_updated": last_updated(path),
-            }
-            for relative, (path, _metadata, body) in pages.items()
-        },
-    }
-    return json.dumps(payload, indent=2, ensure_ascii=False) + "\n"
-
-
 def render_llms() -> str:
     pages = source_pages()
     data = model_data()
@@ -200,7 +159,7 @@ def raw_targets(site_dir: Path) -> Iterable[tuple[Path, str]]:
 
 
 def generated_outputs() -> tuple[tuple[Path, str], ...]:
-    return ((LLMS_TARGET, render_llms()), (METADATA_TARGET, render_metadata()))
+    return ((LLMS_TARGET, render_llms()),)
 
 
 def write_generated() -> None:

From dc26575d693dccea7a0d64d690fd8477966dffee Mon Sep 17 00:00:00 2001
From: MaartenGr <maartengrootendorst@gmail.com>
Date: Mon, 10 Aug 2026 23:17:35 +0200
Subject: [PATCH 20/20] More cleanup

---
 PLAN.md                         | 2 +-
 docs/stylesheets/gemma.css      | 2 +-
 overrides/partials/content.html | 3 +--
 scripts/agent_docs.py           | 2 +-
 4 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/PLAN.md b/PLAN.md
index 8eaabb3..6020cbc 100644
--- a/PLAN.md
+++ b/PLAN.md
@@ -9,7 +9,7 @@ history; durable authoring instructions belong in `README.md` or `docs/contribut
 ## Current state
 
 - The Zensical design system, navigation, page-context header and global Gemma 4 selector work.
-- Agent-facing raw Markdown routes and a Stripe-style `llms.txt` are generated by the build.
+- Agent-facing raw Markdown routes and a `llms.txt` are generated by the build.
 - Nine Markdown-first tutorials can be exported to Jupyter notebooks and opened in Colab.
 - One HF Space and one task-first Learn page demonstrate richer documentation experiences.
 - All 63 imported pages are frozen and hand-maintained.
diff --git a/docs/stylesheets/gemma.css b/docs/stylesheets/gemma.css
index e2db731..1d27600 100644
--- a/docs/stylesheets/gemma.css
+++ b/docs/stylesheets/gemma.css
@@ -73,7 +73,7 @@
   --gemma-surface-sunken: var(--gemma-n50);
   --gemma-border: var(--gemma-n150);
   /* One step darker than the hairline. Separators inside a row have to out-read the rule
-     that closes the row, or the row looks like a grid. Stripe uses the same relationship. */
+     that closes the row, or the row looks like a grid. */
   --gemma-border-strong: var(--gemma-n200);
   /* n500 measures 4.46:1 on the n50 tint -- just under AA for the 12-13px text this is
      used for (table headers, nav, tabs). n600 clears it at 6.38:1. */
diff --git a/overrides/partials/content.html b/overrides/partials/content.html
index 8ccae70..0f10f88 100644
--- a/overrides/partials/content.html
+++ b/overrides/partials/content.html
@@ -1,7 +1,6 @@
 {#-
   Overrides the theme's content partial for one reason: the page context strip belongs
-  *below* the H1, the way docs.stripe.com places its page-level controls (measured: their
-  control row sits ~60px under the title, with nothing at all between breadcrumb and title).
+  *below* the H1.
 
   The H1 comes out of rendered markdown, so the only way to get beneath it is to split the
   content on the first `</h1>`. Autoescaping is off in this theme, so the two halves emit as
diff --git a/scripts/agent_docs.py b/scripts/agent_docs.py
index ef5bb1e..0e5e041 100644
--- a/scripts/agent_docs.py
+++ b/scripts/agent_docs.py
@@ -3,7 +3,7 @@
 
 The committed `docs/llms.txt` works with the normal Zensical preview. After a site build,
 `--publish site` also writes raw Markdown beside the
-rendered HTML using Stripe-style URLs such as `/get-started/quickstart.md`.
+rendered HTML.
 """
 
 from __future__ import annotations