gemma4-research/tooling

Gemma 4 — Canonical Tooling Corpus

Actual scripts, notebooks, model cards, and configs downloaded from Google, Hugging Face, and the canonical framework maintainers. Populated 2026-04-18 by parallel research across five lanes. 147 files, ~14 MB.

Triage: read the subdirectory README that matches your task, not this one. This file is an index.

Directory map

Dir	What's there	When to open it
google-official/	google-deepmind/gemma JAX/Flax examples, google/gemma_pytorch scripts, gemma.cpp README + gemma_api_server docs, google-gemma/cookbook notebooks, official ai.google.dev HTML snapshots, Gemma 3 tech report PDF	Before trusting any non-Google source; when you need the authoritative prompt format or function-calling spec
huggingface/	All 8 google/gemma-4-* model cards, chat-template .jinja files, tokenizer_config.json (with response-schema regex), transformers gemma4/ source, official Gemma 4 Spaces app.py, HF launch blog posts	Before writing any transformers / processor integration; for the canonical chat-template handling
inference-frameworks/	Comparison table across vLLM / llama.cpp / MLX / Keras-hub / TGI / Gemini API / Vertex AI. Real launch commands in run_commands.sh, 9 code snippets under snippets/	When picking a non-Ollama runtime; when you need audio/video input (Ollama doesn't expose it)
gemma-family/	12 per-variant briefs: ShieldGemma 2, CodeGemma, PaliGemma 2, RecurrentGemma, DataGemma, MedGemma, TxGemma, EmbeddingGemma, TranslateGemma, FunctionGemma, DolphinGemma, SignGemma + index.md	When scoping a project that needs a specialized sister model (embeddings, safety, vision-grounded, translation, tool routing)
fine-tuning/	Unsloth Gemma 4 notebooks (text/vision/audio/GRPO), Axolotl Gemma 4 YAMLs (including 26B-A4B MoE), TRL reference scripts, Google cookbook fine-tune notebooks, recipe-recommendation.md with Seth's homelab-specific path	Before spending a dollar on cloud GPU or starting any Gemma 4 fine-tune

Findings that update / contradict the existing corpus

These are real gaps worth patching into SYNTHESIS.md, GOTCHAS.md, or CORPUS_tool_calling_format.md. Flagged here, not applied — the user asked for research, not a rewrite.

1. Prompt-token format changed in Gemma 4. Gemma 1/2/3 used <start_of_turn>user ... <end_of_turn>. Gemma 4 uses asymmetric pipe-brackets: <|turn>user\n ... <turn|>. Also new: <|think|>, <|channel>thought...<channel|>, <|tool>, <|tool_call>, <|tool_response> (+ inverses), <|image>, <|audio>, and string delimiter <|"|>. The existing CORPUS_tool_calling_format.md documents the tool tokens but doesn't reflect the turn-token change or the thinking/channel tokens. Canonical source: huggingface/model-cards/gemma-4-31B-it-chat_template.jinja and google-official/docs/ai-google-dev_prompt_formatting_gemma4.html.

2. google/gemma_pytorch is abandoned for Gemma 4. Last push 2025-05-30; the variants validator rejects Gemma 4 IDs. Anyone pointing at it as the PyTorch reference is wrong — use HF transformers or google-deepmind/gemma (JAX/Flax) instead.

3. gemma.cpp ships a Gemini-API-compatible local HTTP server (gemma_api_server, endpoint POST /v1beta/models/<model>:generateContent, SSE streaming). This is a Google-authored alternative to Ollama that speaks the real Gemini REST API — possibly the single most interesting discovery in this research pass. See google-official/gemma-cpp/API_SERVER_README.md.

4. Transformers exposes AutoModelForMultimodalLM (new AutoClass) — not AutoModelForCausalLM. It also exposes processor.parse_response(..., response_schema=...) driven from tokenizer_config.json, which replaces the hand-rolled regex in the current CORPUS_tool_calling_format.md. Pin: transformers>=5.5.4.

5. Gemma 4 breaks Flash Attention. FA2's max head_dim is 256, FA4's is 128, and Gemma 4's global head_dim is 512. Use SDP or Flex Attention. Axolotl hard-codes sdp_attention: true for Gemma 4. This belongs in GOTCHAS.md.

6. The 26B variant is a MoE — gemma-4-26B-A4B (A4B = 4B active per token). Quantization rules differ: Unsloth says use 16-bit LoRA, not 4-bit QLoRA, for acceptable quality. Axolotl's ScatterMoE + expert-LoRA config is the only tool validated for 4-bit MoE training. Worth a line in CORPUS_ollama_variants.md.

7. No Gemma 4 technical report PDF exists yet as of 2026-04-18. DeepMind repo says "Gemma 4 (Coming soon)". Gemma 3 report (downloaded at google-official/tech-report/Gemma3Report.pdf) remains the closest authoritative family citation.

8. No google/gemma-4-* specialized siblings yet — ShieldGemma, CodeGemma, PaliGemma, MedGemma, DataGemma are all still on Gemma 2 or 3 base. Historical lag is 3–6 months; expect siblings-on-4 mid-to-late 2026.

9. No Gemma-4-specific TRL script in huggingface/trl yet. HF blog says "fully supported," but the SFT/DPO/GRPO examples are still on Gemma 3 model IDs. Drop-in with model_id swap works. Only Gemma-4-dedicated TRL example today is huggingface-gemma-recipes/carla_vlm_gemma.py (VLM GRPO).

10. HF Spaces app.py files are the shortest Gemma 4 inference examples — Google and HF both use them as ref. See huggingface/spaces/huggingface-projects_gemma-4-{31b,e4b}-it-app.py.

Immediate homelab plug-ins (from the gemma-family research)

• EmbeddingGemma (308M) — 100+ languages, Matryoshka to 128d. Drop-in upgrade from nomic-embed-text on both hosts.
• FunctionGemma (270M) — cheap tool-router in front of mortdecai:* (latency win on hot path).
• PaliGemma 2 3B-448 — vision grounding with bbox output for AI_Visualizer / AI visualizer CT 167 alongside SDXL.
• TranslateGemma 4B — useful for the family history agent (German/Polish sources).

Source-url discipline

Every URL in the subdirectory READMEs was fetched and verified, not reconstructed from training. If a downloaded file is wrong, git log will show when it was pulled; the agent transcripts are the record of the source commit. Upstream repos can and do rename paths (see: google-gemini/gemma-cookbook → google-gemma/cookbook). Re-verify before citing externally.