AI

google/embeddinggemma-2 · Hugging Face

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 20:47:26 UTC.

Briefings

Google DeepMind released EmbeddingGemma 2, an open multimodal embedding model mapping text, images, video, and audio into a 768-dimensional space. It features 740M parameters with 100+ language support and a 14% code task improvement over Gemma 4. The model supports Matryoshka Representation Learning for variable vector dimensions and runs on consumer hardware with an 8K token context window.

Key points

• EmbeddingGemma 2 unifies four modalities (text, images, video, audio) into a single 768-dimensional embedding space.
• The model achieves a ~14% improvement on code tasks compared to its predecessor Gemma 4.

Context

This release builds on Gemma 4 advancements, offering native multimodality, multilingual support, and flexible parameter loading for specific use cases like RAG and search.

Limitations

Evidence is limited to the article's summary; specific performance metrics for other modalities are not provided. The 14% code improvement is relative to Gemma 4, not absolute. No independent validation or large-scale testing data is included in the source.

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