docs: add canonical tooling corpus (147 files) from Google/HF/frameworks

Five-lane parallel research pass. Each subdir under tooling/ has its own
README indexing downloaded files with verified upstream sources.

- google-official/: deepmind-gemma JAX examples, gemma_pytorch scripts,
  gemma.cpp API server docs, google-gemma/cookbook notebooks, ai.google.dev
  HTML snapshots, Gemma 3 tech report
- huggingface/: 8 gemma-4-* model cards, chat-template .jinja files,
  tokenizer_config.json, transformers gemma4/ source, launch blog posts,
  official HF Spaces app.py
- inference-frameworks/: vLLM/llama.cpp/MLX/Keras-hub/TGI/Gemini API/Vertex AI
  comparison, run_commands.sh with 8 working launches, 9 code snippets
- gemma-family/: 12 per-variant briefs (ShieldGemma 2, CodeGemma, PaliGemma 2,
  Recurrent/Data/Med/TxGemma, Embedding/Translate/Function/Dolphin/SignGemma)
- fine-tuning/: Unsloth Gemma 4 notebooks, Axolotl YAMLs (incl 26B-A4B MoE),
  TRL scripts, Google cookbook fine-tune notebooks, recipe-recommendation.md

Findings that update earlier CORPUS_* docs are flagged in tooling/README.md
(not applied) — notably the new <|turn>/<turn|> prompt format, gemma_pytorch
abandonment, gemma.cpp Gemini-API server, transformers AutoModelForMultimodalLM,
FA2 head_dim=512 break, 26B-A4B MoE quantization rules, no Gemma 4 tech
report PDF yet, no Gemma-4-generation specialized siblings yet.

Pre-commit secrets hook bypassed per user authorization — flagged "secrets"
are base64 notebook cell outputs and example Ed25519 keys in the HDP
agentic-security demo, not real credentials.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tooling/fine-tuning/axolotl/README.md

@@ -0,0 +1,60 @@
+# Finetune Google's Gemma 4 with Axolotl
+
+[Gemma 4](https://huggingface.co/collections/google/gemma-4) is a family of multimodal models from Google. This guide covers how to train them with Axolotl.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+
+3. Run the finetuning example:
+
+```bash
+# 26B MoE QLoRA (1x80GB @ ~50 GiB)
+axolotl train examples/gemma4/26b-a4b-moe-qlora.yaml
+
+# 31B Dense QLoRA (1x80GB @ ~44 GiB)
+axolotl train examples/gemma4/31b-qlora.yaml
+
+# 31B Dense QLoRA Flex Attn (1x80GB @ ~26 GiB)
+axolotl train examples/gemma4/31b-qlora-flex.yaml
+```
+
+### MoE Expert Quantization & Expert LoRA (26B-A4B only)
+
+The 26B-A4B config uses ScatterMoE kernels via the transformers `ExpertsInterface` and quantizes expert weights on load. To learn about expert quantization, expert LoRA targeting, and related limitations, see the [MoE Expert Quantization](https://docs.axolotl.ai/docs/expert_quantization.html) docs.
+
+## Flex Attention
+
+Reduce ~40% VRAM (at the cost of up to half throughput) by setting the below (shown in `examples/gemma4/31b-qlora-flex.yaml`):
+
+```yaml
+torch_compile: true
+flex_attention: true
+```
+
+This works for both the MoE and Dense model.
+
+## Limitations
+
+- **Flash Attention**: FA2 (max head_dim=256) and FA4 (max head_dim=128) cannot support Gemma 4's `global_head_dim=512`. Use SDP or flex attention instead.
+- **LoRA kernels**: Not supported due to KV-sharing layers.
+- **lora_target_linear**: Incompatible for multimodal models — use `lora_target_modules` with a regex to restrict LoRA to the text backbone.
+
+### TIPS
+
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- You can run full finetuning by removing `adapter: qlora`, `load_in_4bit: true`, and `quantize_moe_experts: true` from the config. This is heavy and has not been tested.
+
+## Optimization Guides
+
+Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
+
+## Related Resources
+
+- [Gemma 4 Blog](https://huggingface.co/blog/gemma4)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl Website](https://axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)