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>
106 lines
3.5 KiB
Markdown
106 lines
3.5 KiB
Markdown
# TranslateGemma
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Multilingual text + image translation. Released **January 15, 2026**. Built on **Gemma 3** (not Gemma 4, despite being the newest variant at time of writing).
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## What it is
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Gemma 3 fine-tuned for translation across **55 languages**, using a two-stage distillation from Gemini. Retains Gemma 3's multimodal capability — can translate text embedded in images.
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## Sizes
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- **4B IT**
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- **12B IT**
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- **27B IT**
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Google's headline claim: the 12B beats Gemma 3 27B baseline translation quality with less than half the parameters.
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## Model card
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- HF: https://huggingface.co/google/translategemma-4b-it
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- Blog: https://blog.google/innovation-and-ai/technology/developers-tools/translategemma/
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- InfoQ: https://www.infoq.com/news/2026/01/google-translategemma-models/
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## Supported languages
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55 languages via ISO 639-1 codes (`en`, `de`, `es`, `fr`, `pl`, `ja`, `zh`, `ar`, `hi`, etc.) plus regional variants (`en-US`, `en-GB`, `pt-BR`, `pt-PT`, `de-DE`, `de-AT`, `de-CH`, `zh-CN`, `zh-TW`, etc.).
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## Prompt format
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**Strict chat-template format.** Content list must contain exactly **one entry**, with mandatory `source_lang_code` and `target_lang_code`.
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### Text translation
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```python
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messages = [{
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"role": "user",
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"content": [{
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"type": "text",
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"source_lang_code": "cs",
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"target_lang_code": "de-DE",
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"text": "V nejhorším případě i k prasknutí čočky.",
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}],
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}]
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```
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### Image translation (translates text inside the image)
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```python
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messages = [{
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"role": "user",
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"content": [{
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"type": "image",
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"source_lang_code": "ja",
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"target_lang_code": "en",
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"url": "https://example.com/japanese-sign.jpg",
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}],
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}]
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```
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Only `"text"` and `"image"` types are supported. Only `user` and `assistant` roles. Image input is normalized to 896×896 (256 vision tokens).
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## Minimum invocation
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```python
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from transformers import pipeline
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import torch
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pipe = pipeline(
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"image-text-to-text",
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model="google/translategemma-4b-it",
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device="cuda",
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dtype=torch.bfloat16,
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)
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messages = [{
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"role": "user",
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"content": [{
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"type": "text",
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"source_lang_code": "pl",
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"target_lang_code": "en",
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"text": "Dziadek mieszkał w Warszawie przed wojną.",
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}],
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}]
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out = pipe(text=messages, max_new_tokens=200)
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print(out[0]["generated_text"][-1]["content"])
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```
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## Performance
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- **WMT24++ across 55 languages:** MetricX 5.32, COMET 81.6.
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- Context window: 2K tokens (short — this is a translation model, not a long-doc summarizer).
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## When to choose it over base Gemma 4
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- You want **translation quality > general Gemma 4** at equivalent size, with the strict prompt contract making it easy to drop into a pipeline.
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- You need **image-text translation** (street signs, menus, old documents) as a first-class task.
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- You care about the 55-language coverage and regionalized variants.
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Base Gemma 4 31B *can* translate — fine for casual use. TranslateGemma wins for production pipelines and when you care about metric-validated quality.
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## Homelab fit
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**Strong fit for family history agent.** If source documents are in German, Polish, Hungarian, Yiddish, or any of the 55 supported languages, TranslateGemma 4B on pve197 (GPU-backed) becomes the translation leg of an ingest pipeline: OCR → TranslateGemma → Gemma 4 for reasoning. The 4B size fits alongside the other models on the V100.
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Also useful for SearchXNG (if Seth ever wants to auto-translate non-English search results) and the news-summary print system (translate foreign-language feeds before summarization).
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