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In this video we learn how to use Google’s Embedding Gemma (300M) to build fast, on-device RAG with ≈200MB memory and support for 100+ languages. We will look at a RAG example.
LINK:
https://developers.googleblog.com/en/introducing-embeddinggemma/
https://huggingface.co/blog/embeddinggemma
https://arxiv.org/pdf/2205.13147
https://huggingface.co/blog/matryoshka
https://github.com/google-gemini/gemma-cookbook/blob/main/Gemma/%5BGemma_3%5DRAG_with_EmbeddingGemma.ipynb
https://ai.google.dev/gemma/docs/embeddinggemma/fine-tuning-embeddinggemma-with-sentence-transformers
https://ai.google.dev/gemma/docs/embeddinggemma/model_card
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RAG Beyond Basics Course:
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TIMESTAMPS:
00:00 EmbeddingGemma
02:15 Comparison with Other Embedding Models
02:41 Google’s Interesting position
03:21 Dense Embeddings is Killing Retrieval
06:13 RAG with EmbeddingGemma
09:37 Fine-Tuning and Training the Model Read More Prompt Engineering
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