Post Content
Discover EmbeddingGemma, a state-of-the-art 308 million parameter text embedding model designed to power generative AI experiences directly on your hardware. Ideal for mobile-first Al, EmbeddingGemma brings powerful capabilities to your applications, enabling features like semantic search, information retrieval, and custom classification – all while running efficiently on-device.
In this video, Alice Lisak and Lucas Gonzalez from the Gemma team introduce EmbeddingGemma and explain how it works. Learn how you can run this model on less than 200MB of RAM with quantization, customize its output dimensions with Matryoshka Representation Learning (MRL), and
build powerful offline Al features.
Resources:
Learn about EmbeddingGemma → https://developers.googleblog.com/en/introducing-embeddinggemma
EmbeddingGemma documentation → https://ai.google.dev/gemma/docs/embeddinggemma
Gemma Cookbook → https://github.com/google-gemini/gemma-cookbook
Quickstart RAG notebook → https://github.com/google-gemini/gemma-cookbook/blob/main/Gemma/%5BGemma_3%5DRAG_with_EmbeddingGemma.ipynb
Discover Gemma models → https://deepmind.google/models/gemma
Chapters
0:00 – Intro
0:26 – Model overview
1:18 – Model features
2:29 – RAG
2:54 – Website embedding demo
3:23 – Tools and platforms
3:41 – Conclusion
Subscribe to Google for Developers → https://goo.gle/developers
Speaker:Alice Lisak Lucas Gonzalez
Products Mentioned: Google AI, Gemma,Generative AI Read More Google for Developers
