Rohan Paul
@rohanpaul_ai
Google released its EmbeddingGemma 2 paper.
embedding model for on-device multimodal AI, under an Apache 2.0 license.
> puts text, code, images, audio and video into 1 searchable space on phones. gives phones a missing piece: a way to understand and search your own stuff without sending it to a server.
> shows that a 744M-parameter open model can embed text, images, audio, and video, beating many models 2 to 12 times larger.
> On a Pixel 11 Pro, quantized text weights take about 191MB of active RAM, and the full multimodal model takes about 567MB.
> The context window grows 4x to 8K tokens, enough for roughly 5.5 minutes of audio, 29 images or 58 video frames in 1 input.
They built EmbeddingGemma 2 on Gemma 4, trained it to copy the larger Gemini Embedding 2, then tuned it with contrastive training. All media types share 1 vector space, and the full model fits in 567 MB on a Pixel 11 Pro with 4-bit weights.
Embedding speech directly beat transcribing it, 69.54 versus 57.59 on the MSEB spoken-query retrieval benchmark. Code search scored 78.68 on MTEB Code, up from 68.76 for EmbeddingGemma 1.
