# EmbeddingGemma 2 (slim)

> EmbeddingGemma 2 is Google DeepMind's open-weight multimodal embedding model of 740M parameters, mapping text, code, images, video and audio into one 768-dimension vector space from up to 8,192 tokens, under Apache 2.0.

- Full: https://www.anchorterminal.com/tools/embeddinggemma-2.md (~7,550 tokens) · this version ~2,180 tokens · JSON https://www.anchorterminal.com/tools/embeddinggemma-2.json · canonical https://www.anchorterminal.com/tools/embeddinggemma-2
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-10-30 · pricing https://www.anchorterminal.com/tools/embeddinggemma-2#pricing · reviews https://www.anchorterminal.com/tools/embeddinggemma-2#reviews
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

**B · 66.4/100 · rank #16 of 117 open-weight models · #7 in Embeddings & rerankers · not agent-ready · confidence medium**


Assessment: A small multimodal embedding cousin that puts text, code, images, video and audio in one 768-dimension space, with task prefixes and vectors that truncate to 128. Five common runtimes run it and the BF16 files are 1.53 GB, with a browser build for Transformers.js. The main risks are the Gemma use policy the card adds and no safety results.

## Facts

- Kind: Open-weight model · vendor: Google DeepMind · category: Embeddings & rerankers · legal entity: Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author) · provenance 70/100
- Packages: huggingface `google/embeddinggemma-2`
- Auth: None · pricing: Free · x402: no · licence: Apache 2.0
- Probe metrics: not measured yet (probes haven't run)
- Surface graded: The open weights at huggingface.co/google/embeddinggemma-2, read from the card, config files, Sentence Transformers configs, processor config, template, the Hub's metadata, file list, commits and discussion list on 30 October 2026, with the runtimes' own code and the MTEB leaderboard's data. Google's documentation, launch blog, technical report, gemma.ai and the licence pages couldn't be fetched in this session; what this listing says about them comes from the EmbeddingGemma 300M and Gemma 4 checks of 21 October 2026. Nothing was downloaded or run
- Model: A bidirectional encoder built on the Gemma 4 architecture (EmbeddingGemma2Model), 24 layers with five sliding-window layers to each global one, mean pooling and a 512 to 768 projection. 740M parameters per Google, 744,371,512 BF16 in the safetensors file: a 270M text model (130M transformer and 140M embeddings) with a 170M vision encoder and a 300M audio encoder that can be left out at load time
- Input and output: Text, images, video and audio, alone or interleaved with <|image|>, <|video|> and <|audio|> placeholders, share one 8,192-token budget. Text costs a token per subword; an image 280 tokens by default (a budget of 70 to 1,120), about 29 images; video 140 tokens a frame at 1 frame per second, about 58 frames; audio, mono at 16 kHz, 25 tokens a second, about 327 seconds. Out comes one mean-pooled, L2-normalised 768-dimension vector, truncatable to 512, 256 or 128 and re-normalised. Text takes a task prefix, eight kinds named in config_sentence_transformers.json (SearchQuery, Document, QuestionAnswering, FactChecking, CodeRetrieval, Classification, Clustering, SentenceSimilarity, with aliases); media take none. bfloat16 or float32, never float16
- Files: model.safetensors (1.49 GB, BF16), the tokeniser (32 MB tokenizer.json and a SentencePiece model), config, processor, template and Sentence Transformers configs, 1.53 GB in all. No pickle files and no Python. Every file carries a checksum in the Hub's file list
- Licence: Apache 2.0 in the card's metadata, with the card's licence link pointing to Google's Gemma 4 licence page, which on 21 October 2026 redirected to Google's plain Apache 2.0 page. No LICENSE file in the repository. The card's Ethics and Safety section adds that deployments must adhere to the Gemma Prohibited Use Policy, which Google says it may update; Gemma 4's cards carry no such line
- Runtimes: Transformers 5.19.0 and later (embedding_gemma2) with Sentence Transformers 6.1.0 or later, vLLM 0.32.0 (EmbeddingGemma2Model in its registry), SGLang 0.5.22 (embedding_gemma2.py), llama.cpp (gemma-embedding2 architecture, with ggml-org's GGUF and mmproj files), Ollama 0.40.13 (its MLX runner's gemma4embedding model and import code). Transformers.js 4.3.2 runs the ONNX build in the browser. LM Studio's catalogue and Ollama's library weren't checked
- Builds: Google publishes BF16 only in its own organisation. Others publish quantised builds: onnx-community's ONNX (q4 about 0.5 GB with all three encoders), litert-community's LiteRT-LM int4 QAT builds (165 MB text, 388 MB text and vision, 485 MB full, plus NPU builds), ggml-org's BF16 and Q8_0 GGUF with a separate multimodal projector, unsloth's GGUF and mlx-community's MLX
- Training data: Web documents in over 140 languages, code, images, video, audio and paired cross-modal samples, with a January 2025 cut-off, filtered for CSAM, personal and sensitive data, and quality. No sources, mix or sizes
- Hardware: The card names mobile devices and laptops as targets and gives text-only, text and image, text and audio and full loading sizes (270M, 440M, 570M, 740M parameters) but no memory figure. The BF16 files are 1.53 GB

- Scores: Capability 59, Reach 75, Risk 60, Agent interface 70, Documentation and upkeep 80 · total over the 7 assessed categories
- Why: Capability, From 9 benchmark results the publisher submitted (counted at half weight) in 2 areas, each scored as its share of the way from the benchmark… · Reach, Memory earns the full 30, since Google's BF16 files are 1.53 GB and 4-bit builds by others are about 0.5 GB. · Risk, File safety gets the full 25 for one safetensors file loaded by Transformers' own code. · Agent interface, Read for an embedding model, the template, tool-calling, trained tool calling and structured-output lines stand for the prompt format and wh… · Documentation and upkeep, The card covers all seven items (20) and describes the training data by type with a cut-off (10).
- Sources: 21, open questions: 7, both in the full twin
- Capabilities: embed.text, embed.multimodal, embed.multilingual, embed.code, model.open-weights
- JSON: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json
- Verify (for the vendor): the badge `https://www.anchorterminal.com/badges/embeddinggemma-2.svg` or a link to https://www.anchorterminal.com/tools/embeddinggemma-2 from a page on gemma.ai or one of its subdomains, then `POST https://www.anchorterminal.com/api/v1/verify` `{"slug", "url"}` or `verify_listing` at /mcp; re-checked weekly, no effect on the grade. Snippets in the full twin.
- Support: Discord https://discord.gg/google-dev-community; https://huggingface.co/google/embeddinggemma-2/discussions; https://ai.google.dev/gemma/docs/embeddinggemma; more in the full twin

## Before you call it

1. Install sentence-transformers 6.1.0 or later and transformers 5.19.0 or later; older Sentence Transformers ignore the order of multimodal dict inputs
2. Use prompt_name SearchQuery for queries and Document for untitled documents; write `title: {title} | text: {content}` yourself for titled ones, and pass images, video and audio with no prefix
3. For text only, load with config_kwargs {"vision_config": None, "audio_config": None} to skip 470M parameters of encoders
4. Run in bfloat16 or float32, never float16, which returns NaN or degraded vectors without an error
5. Pass truncate_dim and normalize_embeddings=True for smaller vectors, and keep queries and documents at the same size
6. Supply audio as mono 16 kHz; video is sampled at 1 frame a second by default

## Connect

```bash
pip install -U "sentence-transformers>=6.1.0" "transformers>=5.19.0"
python -c "from sentence_transformers import SentenceTransformer; m=SentenceTransformer('google/embeddinggemma-2', config_kwargs={'vision_config': None, 'audio_config': None}); print(m.encode('Which planet is red?', prompt_name='SearchQuery').shape)"
```

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