Mortdecai
eecebe7ef5
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>
64 lines
2.2 KiB
Markdown
64 lines
2.2 KiB
Markdown
# TxGemma
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Therapeutic-development / drug-discovery variant. Built on **Gemma 2**. No Gemma 3 or 4 generation yet.
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## What it is
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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.
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## Sizes
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- **2B predict** — prediction-only, narrow prompt format.
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- **9B predict** + **9B chat** — prediction plus conversational reasoning.
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- **27B predict** + **27B chat** — same, larger.
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## Model card
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- https://developers.google.com/health-ai-developer-foundations/txgemma/model-card
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- DeepMind: https://deepmind.google/models/gemma/txgemma/
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- Paper: https://deepmind.google/research/publications/153799/
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## Prompting modes
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**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).
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**Conversational mode** (9B, 27B): chat-template interactions, can explain reasoning behind predictions.
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## Minimum invocation — prediction
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```python
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from transformers import pipeline
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pipe = pipeline(
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"text-generation",
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model="google/txgemma-27b-predict",
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device="cuda",
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)
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prompt = (
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"Instructions: Predict whether the molecule can penetrate the blood-brain barrier.\n"
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"Context: Blood-brain barrier penetration is an important property for CNS drugs.\n"
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"Question: Given the SMILES string CN1C=NC2=C1C(=O)N(C(=O)N2C)C, "
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"predict BBB penetration. Answer with 'Yes' or 'No'.\n"
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"Answer:"
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)
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out = pipe(prompt, max_new_tokens=8)
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print(out[0]["generated_text"])
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```
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## License
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Health AI Developer Foundations — same terms as MedGemma. Non-clinical, research-use.
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## When to choose it over base Gemma 4
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- You're doing **drug-discovery research** and need TDC-format predictions out of the box.
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- You want **SMILES-aware reasoning** without a custom cheminformatics stack.
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Almost never chosen for general-purpose work. TxGemma's value is the training data, not the base model.
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## Homelab fit
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Zero. Noted for completeness.
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