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>
75 lines
3.2 KiB
Markdown
75 lines
3.2 KiB
Markdown
# Other Gemma variants
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Smaller / more specialized sisters that don't warrant a full file each. All on Gemma 2 or Gemma 3. **None on Gemma 4 as of April 2026.**
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## T5Gemma / T5Gemma 2
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**Encoder-decoder** Gemma, built by adapting decoder-only Gemma weights into a T5-style encoder-decoder via UL2 or PrefixLM pretraining.
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- **T5Gemma** (Jul 2025): Gemma 2-based. Sizes include 2B-2B, 9B-2B, 9B-9B plus new T5-sized small/base/large/XL models.
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- **T5Gemma 2** (Dec 2025): Gemma 3-based. Sizes: 270M-270M, 1B-1B, 4B-4B. Multimodal (128K context).
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### When to pick it
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- **Summarization, translation, QA** where the encoder's separate bidirectional attention buys quality.
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- Anywhere a decoder-only Gemma feels wasteful for "read input, compress into short output" tasks.
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HF: https://huggingface.co/google/t5gemma-2-4b-4b
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Blog: https://developers.googleblog.com/en/t5gemma/
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## FunctionGemma
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**270M tool/function-calling specialist.** Gemma 3-based. Released Dec 2025.
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Trained to emit structured function calls given a tool catalog. Not a generalist chat model — feed it a user message + tool schemas and it picks the right tool. Tiny enough to run as a pre-router in front of a larger model.
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### When to pick it
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- **Minecraft agent (Mortdecai):** plausibly interesting — use it as a 270M gateway that classifies intent and picks one of the Mortdecai tools, then hands off to the bigger `mortdecai:*` model for reasoning. Latency/cost savings if the tool decision is hot-path.
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- Any agent where tool-selection volume is high and model call cost matters.
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HF: search `google/functiongemma-270m`.
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## VaultGemma
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**1B Gemma 3 trained with differential privacy.** Released Sep 2025.
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The point is the training process (DP-SGD with rigorous privacy budget) more than the weights per se. Useful as a reference checkpoint or for deployments where "model cannot have memorized training data" is a hard requirement.
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### When to pick it
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- Niche. You almost never need DP-trained weights unless you're in regulated space.
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## Gemma-APS
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**Abstractive Proposition Segmentation.** 2B and 7B on Gemma 2. Oct 2024.
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Takes a passage, splits it into atomic propositions (self-contained factual statements). Useful for fact-checking, citation mapping, and as a preprocessing step for RAG indexing.
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### When to pick it
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- Building a **fact-verification pipeline** where you need to decompose generated text into checkable claims.
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- **Family history** — could decompose narrative biographical text into timestamped facts for structured storage.
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## Gemma Scope / Gemma Scope 2
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Sparse autoencoder (SAE) suites for **mechanistic interpretability** research. Gemma Scope on Gemma 2, Gemma Scope 2 on Gemma 3 (Dec 2025).
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Not models you deploy for product work. Tools for "which neurons activate on what" research.
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HF: https://huggingface.co/google/gemma-scope
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### When to pick it
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- Interpretability research only. Not a homelab deployment candidate.
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## Summary of homelab relevance
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| Variant | Homelab fit |
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| T5Gemma 2 4B-4B | Moderate — summarization for the news-briefing printer |
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| FunctionGemma 270M | **High — tool-router for Mortdecai** |
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| VaultGemma | None |
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| Gemma-APS | Low-moderate — niche preprocessing step |
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| Gemma Scope | None (research tool) |
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