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>
94 lines
3.5 KiB
Markdown
94 lines
3.5 KiB
Markdown
# EmbeddingGemma
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On-device text embedding model. Released **September 2025**. Built on **Gemma 3 with T5Gemma initialization**. No Gemma 4 generation yet.
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## What it is
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A **308M-parameter** open embedding model. Trained on 100+ languages. State-of-the-art on MTEB for its size class. Uses **Matryoshka Representation Learning (MRL)** — one model produces embeddings at 768, 512, 256, or 128 dimensions by truncation + renormalization, with graceful quality degradation.
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## Sizes
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- **308M** — only size.
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## Model card
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- https://ai.google.dev/gemma/docs/embeddinggemma/model_card
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- HF: https://huggingface.co/google/embeddinggemma-300m
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- HF blog: https://huggingface.co/blog/embeddinggemma
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- DeepMind: https://deepmind.google/models/gemma/embeddinggemma/
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- Paper: https://arxiv.org/html/2509.20354v2
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## Prompt format
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EmbeddingGemma uses **task-prefixed inputs** — you prepend a task descriptor to each string before embedding.
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### Query prompts
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```
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task: {task description} | query: {your query}
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```
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Default task description: `search result`.
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Example: `task: search result | query: what is the capital of France?`
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### Document prompts
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```
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title: {title or "none"} | text: {document text}
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```
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Providing a real title improves retrieval; use `none` if unavailable.
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Example: `title: Eiffel Tower | text: The Eiffel Tower is a wrought-iron lattice tower...`
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## Minimum invocation
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### Sentence-Transformers (easy path)
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("google/embeddinggemma-300m")
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query = "Which planet is known as the Red Planet?"
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documents = [
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"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
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"Venus is often called Earth's twin due to its similar size.",
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]
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q_emb = model.encode_query(query)
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d_emb = model.encode_document(documents)
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print(model.similarity(q_emb, d_emb))
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```
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The `encode_query` / `encode_document` methods apply the task prefixes automatically.
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### Shorter embeddings (MRL)
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```python
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emb_768 = model.encode(text) # full
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emb_256 = emb_768[:, :256] # truncate
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emb_256 = emb_256 / emb_256.norm(dim=-1, keepdim=True) # renormalize
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```
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## Gotcha
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**Activations do not support `float16`.** Use `bfloat16` or `float32`. This is explicit in the model card.
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## When to choose it over base Gemma 4
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Always, when you want embeddings. Base Gemma 4 is a generative decoder — not trained as an embedding model. EmbeddingGemma is the correct tool for retrieval, clustering, semantic search, RAG.
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Its main competitor is `nomic-embed-text` (already in Seth's pantry). EmbeddingGemma's MRL and multilingual coverage (100+ vs. nomic's ~English-focused) are the differentiators.
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## Homelab fit
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**Highest-impact variant for Seth right now, along with TranslateGemma.**
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- **Family history agent:** 100+ language support + 128d embeddings = tight, multilingual indices over scanned documents, letters, census records. MRL lets you serve fast 128d approximate search and fall back to 768d for reranking.
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- **SearXNG / SethSearch:** drop-in upgrade from nomic-embed-text for the semantic-search layer. Bigger model but better quality.
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- **Mortdecai memory:** use 308M EmbeddingGemma for long-term memory over chat logs. Small enough to run alongside the big mortdecai qwen35 models on pve197 or steel141 without resource contention.
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- **Gemma-cookbook already has a tutorial** (`tutorials_RAG_EmbeddingGemma.ipynb` in the corpus) — skip straight to working code.
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