google/embeddinggemma-2

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License: Apache 2.0 | Authors: Google DeepMind

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:

• 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.
• 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* | 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 |

Quick Start

Install the sentence-transformers library:

pip install -U sentence-transformers transformers

Generate text embeddings:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("google/embeddinggemma-2")

query = "What causes the northern lights?"
document = "The northern lights are caused by charged particles from the sun."

query_emb = model.encode(query, prompt_name="SearchQuery")
doc_emb = model.encode(document, prompt_name="Document")
print(model.similarity(query_emb, doc_emb))

Best Practices

For optimal embedding quality and runtime efficiency, follow these configurations and best practices:

1. Task Instruction Prefixes

EmbeddingGemma 2 is trained with short task instruction prefixes prepended to text inputs. Using the right prefix improves quality; omitting it still works but reduces precision. Prefixes apply to text only. Pass images, video, and audio without any prefix.

Documents with a real title should be formatted as title: {title} | text: {content}. Use title: none when no title is available.

Prefix Notation & Usage

We offer two types of task prefixes, depending on how embeddings are used in the task. There are two types of tasks:

• Asymmetric Tasks (e.g. retrieval): Use a query prefix for queries and a document prefix for corpus items.
• Symmetric Tasks (e.g. classification, similarity): Apply the same task prefix to all inputs being compared.

| Use Case | Task Type | Prompt Name | Query Task Instruction | Document Task Instruction *(use none if no title)* | | :---- | :---- | :---- | :---- | :---- | | Web / document search | Asymmetric |...