At 740M parameters, it’s competitive across benchmarks – even outperforming some specialist models more than twice its size.
Developers can use it to add multimodal search to their apps – like finding moments in a video using a voice memo – or pair it with Gemma 4 for private, on-device RAG.
EmbeddingGemma 2 is released under an Apache 2.0 license. Check out the weights on @HuggingFace and @Kaggle.
Find out more → https://t.co/lRsjbvR5VB
Meet EmbeddingGemma 2, our first natively multimodal open model for on-device embeddings.
It expands beyond text to unify code, images, audio, and video in a shared space. 🧵