embeddinggemma-2:270m-nvfp4-text

1,605 19 hours ago

EmbeddingGemma 2 is a multimodal embedding model from Google built on the Gemma 4 architecture.

vision embedding audio 270m 440m 570m 740m 
ollama pull embeddinggemma-2:270m-nvfp4-text

Details

2 days ago

9e1df58d197e · 378MB

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{ "audio_token": "<|audio|>", "backend": "tokenizers", "boa_token": "<|audio>", "boi_token": "<|imag
413 tensors

Readme

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders.

Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering.

EmbeddingGemma 2 builds upon the architectural and capability advancements of Gemma 4, offering several core features:&nbsp;

  • Native multimodality: Native multimodality: Unifies 4 modalities (text, images, video, and audio) in a single shared 768-dimensional embedding space.
  • Multilinguality and code: EmbeddingGemma 2 understands 100+ languages, and achieves a ~14% improvement on code tasks relative to its predecessor.&nbsp;
  • Flexible footprint: Combines a 270M parameter text backbone (130M transformer + 140M embedder) with selectively loadable vision (170M) and audio (300M) encoders, allowing developers to load only the modalities required for their use case.
  • Matryoshka Representation Learning (MRL): Native support for truncated embeddings across 128d, 256d, 512d, and 768d, enabling up to a 6x reduction in vector storage costs with minimal impact on quality.
  • Context length: 8K token context window, capable of processing minutes of audio or video.
  • Task-steered representations: Uses lightweight text instruction prefixes to optimize embeddings for different tasks (search, classification, clustering, semantic similarity, etc.).

Model Overview

Parameters Total 740M
Backbone 130M
Embedder 140M
Modality Encoders Vision: 170M Audio: 300M
Architecture Layers 24
Model Dimension 512
Hidden Dimension 2048
Sliding Window 1024 tokens
Vocabulary Size 262,144
# Heads 4
# KV-Heads (Local/Global) 2⁄1
Local:Global 5:1
Attention GQA/MQA
Activation Gated FFN with GELU
Pooling Mean Pooling
Projection Layer 512→768
Input/Output Supported Modalities Text, Images, Video, Audio
Context Window 8,192 tokens
Native Output Dimension 768
MRL Truncation Dimensions 128, 256, 512

Benchmark Results

EmbeddingGemma 2 was evaluated across text, code, vision, visual document, video, and audio embedding benchmarks. All results reported below use the full-precision checkpoint.

Overall Evaluation Results (768d)

Modality Benchmark Metric EmbeddingGemma 2 EmbeddingGemma 1
Text Massive Text Embedding Benchmark (MTEB, multilingual, v2) Mean(Task), Multiple 61.36 61.15
Massive Text Embedding Benchmark (MTEB, code, v1) Mean(Task), NDCG@10 78.68 68.76
Image Massive Image Embedding Benchmark (MIEB, lite) Mean(TaskType), Multiple 64.64 -
Massive Multimodal Embedding Benchmark (MMEB v2 - Image) Mean(Task), Hit@1 57.28 -
Massive Multimodal Embedding Benchmark (MMEB v2 - VisDoc) Mean(Task), NDCG@5 67.84 -
Video Massive Multimodal Embedding Benchmark (MMEB v2 - Video) Mean(Task), Hit@1 50.67 -
Audio Massive Sound Embedding Benchmark (MSEB, Retrieval) Mean(Task), MRR@10 69.54 -
Massive Audio Embedding Benchmark (MAEB) Hugging Face&nbsp; Mean(Task), Multiple 49.39 -

Evaluation Results with Vector Truncation

With MRL, EmbeddingGemma 2 representations can be truncated below the native 768d to 128d, 256d, and 512d representations and re-normalized. With this, model users can reduce storage requirements, with minimal quality impact down to 256d. 128d is best suited to text-only workloads.

Output Dimension Compression Ratio MTEB (multilingual, v2) Mean(Task) MTEB (eng, v2) Mean(Task) MTEB (code, v1) Mean(Task) MIEB (lite) Mean(TaskType) MMEB (v2) Overall MSEB (Retrieval) Mean(Task) MAEB Mean(Task)
768d (Full) 1:1 61.36 68.46 78.68 64.64 59.01 69.54 49.39
512d 1:1.5 61.17 68.41 77.24 64.32 58.38 69.18 49.21
256d 1:3 60.41 67.78 76.18 63.13 56.24 66.76 48.91
128d 1:6 57.89 65.68 71.41 59.06 45.65 56.71 46.92