Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing large language models (LLMs), but running it on-device has always been a challenge. Enter Google’s new Embedding Gemma model, a lightweight embedding model designed to make on-device RAG not only possible, but also easy and efficient.
This model is a best-in-class solution for its size, requiring a mere 200 MB of VRAM. With approximately 300 million parameters, it’s a small but mighty tool, perfect for mobile and edge devices where resources are limited.
One of the standout features of Embedding Gemma is its versatility. Built on the Gemma 3 architecture, it offers multilingual support for over 100 languages [01:10]. This makes it a highly flexible tool for global applications. Furthermore, the model allows for customizable output dimensions, letting you balance accuracy and performance [01:26]. You can reduce the output from a maximum of 768 down to 128 dimensions, which is a great way to save on compute cost and speed, though it comes with a slight trade-off in accuracy.
Beyond basic search, Embedding Gemma can be applied to a wide range of Natural Language Processing tasks, including classification, topic modeling, and question answering. It can even be used for more complex functions like fact-checking, reranking, and summarization.
A practical example highlighted in the video demonstrates how to build a simple RAG system with the transformers package [08:13]. Using a corpus of HR and leave policies, the model can efficiently retrieve the most relevant information to answer a user’s question, such as “how do I reset my password?”.
For those who need to tailor the model to specific needs, fine-tuning is an option [09:38]. By using a dataset with anchor, positive, and negative examples, you can improve the model’s similarity scores for your unique use case.
In conclusion, Embedding Gemma is an excellent choice for anyone looking for a lightweight, efficient, and versatile solution for on-device retrieval.
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