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/google-official/deepmind-gemma/README.md

@@ -0,0 +1,99 @@
+# Gemma
+
+[![Unittests](https://github.com/google-deepmind/gemma/actions/workflows/pytest_and_autopublish.yml/badge.svg)](https://github.com/google-deepmind/gemma/actions/workflows/pytest_and_autopublish.yml)
+[![PyPI version](https://badge.fury.io/py/gemma.svg)](https://badge.fury.io/py/gemma)
+[![Documentation Status](https://readthedocs.org/projects/gemma-llm/badge/?version=latest)](https://gemma-llm.readthedocs.io/en/latest/?badge=latest)
+
+[Gemma](https://ai.google.dev/gemma) is a family of open-weights Large Language
+Model (LLM) by [Google DeepMind](https://deepmind.google/), based on Gemini
+research and technology.
+
+This repository contains the implementation of the
+[`gemma`](https://pypi.org/project/gemma/) PyPI package. A
+[JAX](https://github.com/jax-ml/jax) library to use and fine-tune Gemma.
+
+For examples and use cases, see our
+[documentation](https://gemma-llm.readthedocs.io/). Please
+report issues and feedback in
+[our GitHub](https://github.com/google-deepmind/gemma/issues).
+
+### Installation
+
+1.  Install JAX for CPU, GPU or TPU. Follow the instructions on
+    [the JAX website](https://jax.readthedocs.io/en/latest/installation.html).
+1.  Run
+
+    ```sh
+    pip install gemma
+    ```
+
+### Examples
+
+Here is a minimal example to have a multi-turn, multi-modal conversation with
+Gemma:
+
+```python
+from gemma import gm
+
+# Model and parameters (Gemma 4)
+model = gm.nn.Gemma4_E4B()
+params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA4_E4B_IT)
+
+# Example of multi-turn conversation
+sampler = gm.text.ChatSampler(
+    model=model,
+    params=params,
+    multi_turn=True,
+)
+
+prompt = """Which of the 2 images do you prefer ?
+
+Image 1: <|image|>
+Image 2: <|image|>
+
+Write your answer as a poem."""
+out0 = sampler.chat(prompt, images=[image1, image2])
+
+out1 = sampler.chat('What about the other image ?')
+```
+
+The same `ChatSampler` API works with all Gemma versions (2, 3, 3n, 4).
+
+Our documentation contains various Colabs and tutorials, including:
+
+* [Sampling](https://gemma-llm.readthedocs.io/en/latest/colab_sampling.html)
+* [Multi-modal](https://gemma-llm.readthedocs.io/en/latest/colab_multimodal.html)
+* [Fine-tuning](https://gemma-llm.readthedocs.io/en/latest/colab_finetuning.html)
+* [LoRA](https://gemma-llm.readthedocs.io/en/latest/colab_lora_sampling.html)
+* ...
+
+Additionally, our
+[examples/](https://github.com/google-deepmind/gemma/tree/main/examples) folder
+contain additional scripts to fine-tune and sample with Gemma.
+
+### Learn more about Gemma
+
+* To use this library: [Gemma documentation](https://gemma-llm.readthedocs.io/)
+* Technical reports for metrics and model capabilities:
+  * [Gemma 1](https://goo.gle/GemmaReport)
+  * [Gemma 2](https://goo.gle/gemma2report)
+  * [Gemma 3](https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf)
+  * Gemma 4 (Coming soon)
+* Other Gemma implementations and doc on the
+  [Gemma ecosystem](https://ai.google.dev/gemma/docs)
+
+### Downloading the models
+
+To download the model weights. See
+[our documentation](https://gemma-llm.readthedocs.io/en/latest/checkpoints.html).
+
+### System Requirements
+
+Gemma can run on a CPU, GPU and TPU. For GPU, we recommend 8GB+ RAM on GPU for
+The 2B checkpoint and 24GB+ RAM on GPU are used for the 7B checkpoint.
+
+### Contributing
+
+We welcome contributions! Please read our [Contributing Guidelines](./CONTRIBUTING.md) before submitting a pull request.
+
+*This is not an official Google product.*