
An open, lightweight multimodal embedding model
EmbeddingGemma 2 is Google DeepMind's open embedding model that maps text, code, images, video and audio into one shared space, so you can search across them from a single query. At 740M parameters it runs on-device, needing about 567MB of RAM on a Pixel 11 Pro for the full multimodal model (191MB for text only). Apache 2.0 licensed, with weights on Hugging Face and Kaggle. MTEB Code score rises from 68.76 to 78.68.
Google EmbeddingGemma 2 is an open multimodal embedding model that integrates text, code, images, video, and audio into a unified search space. It features 740 million parameters, requires approximately 567MB of RAM on a Pixel 11 Pro, and is available under the Apache 2.0 license.
Scored deterministically. Only candidates that fire a story trigger are sent to a model, so this one has no written angle.
Gaps in our data, not findings about the product. Their weight is redistributed across the 5 we did measure.
A source that found nothing is a measurement. A source that has not run is a gap. Neither means the launch lacks the thing.