Embedding Gemma: On-Device RAG Made Easy

Introduction to a new lightweight embedding model called Embedding Gemma, trained on top of Gemma 3, requiring only 200 megabytes of VRAM and useful for search, classification, and topic modeling.

Details on the architecture of the lightweight Embedding Gemma model, supporting over 100 languages, customizable with a dimensionality of 128, and considerations on reducing dimensions and compute costs.

A comparison of Embedding Gemma with other models, highlighting its lightweight nature and suitability for retrieval tasks, particularly for open-weight models like Gemma 3.

Discussion on the theoretical limits of dense embedding-based retrieval, emphasizing the reliance on dense embedding models irrespective of their size and the impact on accuracy.

Exploration of setting up tasks for retrieval augmented generation using Embedding Gemma, including considerations for prompt instructions, nature of tasks, and metadata.

An example scenario demonstrating the use of Embedding Gemma for query processing, retrieval, and document ranking based on user queries and prompt instructions.

Guidance on curating a dataset, selecting relevant examples, choosing appropriate loss, using sentence transformer, specifying output directory, and training the model.

Information on fine-tuning the embedding model for improved performance, especially with a small number of documents, and a call to share experiences and feedback.