onnx-community/embeddinggemma-2-ONNX
feature-extraction model by onnx-community
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
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: EmbeddingGemma 2 was evaluated across text, code, vision, visual document, video, and audio embedding benchmarks. All results reported below use the full-precision checkpoint. 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…
Read the full model card ↗ · Preview checked 2026-10-29T23:16:26.601Z
Inside the original model card — Document outline
- Model Overview
- Benchmark Results
- Overall Evaluation Results (768d)
- Evaluation Results with Vector Truncation
- Quick Start
- Text search
- Images, audio and video
- Best Practices
- 1. Task Instruction Prefixes
- 2. Selective Encoder Loading
- 3. Matryoshka Dimension Truncation
- 4. Choosing a dtype
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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
onnx-community/embeddinggemma-2-ONNX is a feature-extraction model repository published by onnx-community on Hugging Face. The source reports the transformers.js library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- feature-extraction
- Library
- transformers.js
- Recent downloads (30 days)
- 410,987
- Cumulative likes
- 34
- Hugging Face trending score
- 6
- Reported safetensors parameters
- Not reported
- Architecture
- EmbeddingGemma2Model
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-10-04T20:51:33.000Z
- Last modified
- 2026-10-06T16:58:12.000Z
Compatibility and lineage
Reported languages: multilingual
Reported base models: google/embeddinggemma-2
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.