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Mortdecai eecebe7ef5 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>

2026-04-18 12:24:48 -04:00

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TxGemma

Therapeutic-development / drug-discovery variant. Built on Gemma 2. No Gemma 3 or 4 generation yet.

What it is

Gemma 2 fine-tuned on 7M examples curated from the Therapeutics Data Commons (TDC) — predictive tasks across small molecules, proteins, nucleic acids, diseases, and cell lines. Beats or matches state-of-the-art on 50 of 66 TDC tasks; beats specialist models on 26 of them.

Sizes

2B predict — prediction-only, narrow prompt format.
9B predict + 9B chat — prediction plus conversational reasoning.
27B predict + 27B chat — same, larger.

Model card

Prompting modes

Prediction mode (all sizes): structured TDC-format prompt with instruction + context + question + optional few-shot. Output is a short prediction (sometimes a single token or a float).

Conversational mode (9B, 27B): chat-template interactions, can explain reasoning behind predictions.

Minimum invocation — prediction

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="google/txgemma-27b-predict",
    device="cuda",
)

prompt = (
    "Instructions: Predict whether the molecule can penetrate the blood-brain barrier.\n"
    "Context: Blood-brain barrier penetration is an important property for CNS drugs.\n"
    "Question: Given the SMILES string CN1C=NC2=C1C(=O)N(C(=O)N2C)C, "
    "predict BBB penetration. Answer with 'Yes' or 'No'.\n"
    "Answer:"
)

out = pipe(prompt, max_new_tokens=8)
print(out[0]["generated_text"])

License

Health AI Developer Foundations — same terms as MedGemma. Non-clinical, research-use.

When to choose it over base Gemma 4

You're doing drug-discovery research and need TDC-format predictions out of the box.
You want SMILES-aware reasoning without a custom cheminformatics stack.

Almost never chosen for general-purpose work. TxGemma's value is the training data, not the base model.

Homelab fit

Zero. Noted for completeness.

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