gemma4-research/tooling/gemma-family/embeddinggemma.md

EmbeddingGemma

On-device text embedding model. Released September 2025. Built on Gemma 3 with T5Gemma initialization. No Gemma 4 generation yet.

What it is

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.

Sizes

• 308M — only size.

Model card

• https://ai.google.dev/gemma/docs/embeddinggemma/model_card
• HF: https://huggingface.co/google/embeddinggemma-300m
• HF blog: https://huggingface.co/blog/embeddinggemma
• DeepMind: https://deepmind.google/models/gemma/embeddinggemma/
• Paper: https://arxiv.org/html/2509.20354v2

Prompt format

EmbeddingGemma uses task-prefixed inputs — you prepend a task descriptor to each string before embedding.

Query prompts

task: {task description} | query: {your query}

Default task description: search result.

Example: task: search result | query: what is the capital of France?

Document prompts

title: {title or "none"} | text: {document text}

Providing a real title improves retrieval; use none if unavailable.

Example: title: Eiffel Tower | text: The Eiffel Tower is a wrought-iron lattice tower...

Minimum invocation

Sentence-Transformers (easy path)

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("google/embeddinggemma-300m")

query = "Which planet is known as the Red Planet?"
documents = [
    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    "Venus is often called Earth's twin due to its similar size.",
]

q_emb = model.encode_query(query)
d_emb = model.encode_document(documents)

print(model.similarity(q_emb, d_emb))

The encode_query / encode_document methods apply the task prefixes automatically.

Shorter embeddings (MRL)

emb_768 = model.encode(text)                       # full
emb_256 = emb_768[:, :256]                         # truncate
emb_256 = emb_256 / emb_256.norm(dim=-1, keepdim=True)  # renormalize

Gotcha

Activations do not support float16. Use bfloat16 or float32. This is explicit in the model card.

When to choose it over base Gemma 4

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.

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.

Homelab fit

Highest-impact variant for Seth right now, along with TranslateGemma.

• 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.
• SearXNG / SethSearch: drop-in upgrade from nomic-embed-text for the semantic-search layer. Bigger model but better quality.
• 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.
• Gemma-cookbook already has a tutorial (tutorials_RAG_EmbeddingGemma.ipynb in the corpus) — skip straight to working code.