StudioGoogle DeepMind Releases Open-Source Multimodal Embedding Model EmbeddingGemma 2
Original title: google/embeddinggemma-2 (feature-extraction)
Google DeepMind has released EmbeddingGemma 2, an open-source multimodal embedding model with 740M parameters. Built on the Gemma 4 architecture, it maps text (including code), images, video, and audio into a unified 768-dimensional vector space, supporting over 100 languages for search, RAG, classification, and clustering on consumer hardware like smartphones and laptops. The model features a 270M-parameter text backbone, with optional vision (170M) and audio (300M) encoders. It supports native MRL truncation to 128d/256d/512d, reducing storage costs by up to six times, and offers an 8K token context window with approximately 14% improvement in code tasks. Achieving a score of 61.36 on MTEB multilingual v2, it is licensed under Apache 2.0 and accessible via sentence-transformers.
The source provides parameter breakdowns, storage savings from MRL truncation, and multimodal benchmark scores, allowing readers to assess its suitability for edge-side RAG scenarios.
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