# Other Gemma variants

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.**

## T5Gemma / T5Gemma 2

**Encoder-decoder** Gemma, built by adapting decoder-only Gemma weights into a T5-style encoder-decoder via UL2 or PrefixLM pretraining.

- **T5Gemma** (Jul 2025): Gemma 2-based. Sizes include 2B-2B, 9B-2B, 9B-9B plus new T5-sized small/base/large/XL models.
- **T5Gemma 2** (Dec 2025): Gemma 3-based. Sizes: 270M-270M, 1B-1B, 4B-4B. Multimodal (128K context).

### When to pick it

- **Summarization, translation, QA** where the encoder's separate bidirectional attention buys quality.
- Anywhere a decoder-only Gemma feels wasteful for "read input, compress into short output" tasks.

HF: https://huggingface.co/google/t5gemma-2-4b-4b
Blog: https://developers.googleblog.com/en/t5gemma/

## FunctionGemma

**270M tool/function-calling specialist.** Gemma 3-based. Released Dec 2025.

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.

### When to pick it

- **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.
- Any agent where tool-selection volume is high and model call cost matters.

HF: search `google/functiongemma-270m`.

## VaultGemma

**1B Gemma 3 trained with differential privacy.** Released Sep 2025.

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.

### When to pick it

- Niche. You almost never need DP-trained weights unless you're in regulated space.

## Gemma-APS

**Abstractive Proposition Segmentation.** 2B and 7B on Gemma 2. Oct 2024.

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.

### When to pick it

- Building a **fact-verification pipeline** where you need to decompose generated text into checkable claims.
- **Family history** — could decompose narrative biographical text into timestamped facts for structured storage.

## Gemma Scope / Gemma Scope 2

Sparse autoencoder (SAE) suites for **mechanistic interpretability** research. Gemma Scope on Gemma 2, Gemma Scope 2 on Gemma 3 (Dec 2025).

Not models you deploy for product work. Tools for "which neurons activate on what" research.

HF: https://huggingface.co/google/gemma-scope

### When to pick it

- Interpretability research only. Not a homelab deployment candidate.

## Summary of homelab relevance

| Variant | Homelab fit |
|---|---|
| T5Gemma 2 4B-4B | Moderate — summarization for the news-briefing printer |
| FunctionGemma 270M | **High — tool-router for Mortdecai** |
| VaultGemma | None |
| Gemma-APS | Low-moderate — niche preprocessing step |
| Gemma Scope | None (research tool) |
