Introducing EmbeddingGemma 2: An open model for natively multimodal embeddings

Sebastian Russo, Ivan Llanos, and Sahil Dua from Google DeepMind introduce EmbeddingGemma 2, a lightweight open model that maps text, images, video, and audio into a single unified embedding space. At just 740 million parameters, EmbeddingGemma 2 is engineered for on-device use cases. Watch this video to discover: *EmbeddingGemma 2’s modular architecture with optional audio and vision encoders *Instant media search and video moment retrieval running fully offline on device *Retrieval-augmented generation (RAG) pipelines where sensitive data never leaves the hardware *Fine-tuning for domain-specific use cases 🔗 Resources: Learn about the model → https://goo.gle/4hE3Lb9 Read the developer guide → https://goo.gle/4hFnyqO Explore on-device embeddings with Google AI Edge → https://goo.gle/3VSXqS1 Download model weights from HuggingFace → https://goo.gle/4i45qbp Get started with the Gemma Cookbook → https://goo.gle/3TCsqVO Have questions? Drop them in the comments. Subscribe to Google for Developers → https://goo.gle/developers Speakers: Sebastian Russo, Ivan Llanos, Sahil Dua Products Mentioned: Gemma, Google AI Edge