Google DeepMind just shipped EmbeddingGemma 2, a 740-million-parameter open embedding model that handles text, code, images, audio, and video in a single shared space.
AspireAI community · blog.google
Google DeepMind just shipped EmbeddingGemma 2, a 740-million-parameter open embedding model that handles text, code, images, audio, and video in a single shared space.
It is built on the Gemma 4 architecture, ships under Apache 2.0, and is small enough to run on a phone. Google says it jumps 9.92 points on code retrieval (MTEB Code) and beats larger specialist embedders on vision and audio for its size.
The trick is modular: a 270M text core plus optional 170M vision and 300M audio encoders, with Matryoshka truncation that lets you shrink the output vector from 768 down to 128 dimensions. Text-only fits in about 191MB of RAM.
Weights are live on Hugging Face and Kaggle today, with runners including LiteRT, llama.cpp, Ollama, vLLM, and MLX.
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