Latest news from Google's Deep Learning team: they have officially launched EmbeddingGemma, an open-source embedding model designed for mobile devices. With its efficient design of 308 million parameters, EmbeddingGemma has been rated as the best multilingual text embedding model under 500M in the MTEB (Massive Text Embedding Benchmark), demonstrating powerful capabilities such as Retrieval-Augmented Generation (RAG) and semantic search, which can run directly on devices like smartphones without an internet connection.
EmbeddingGemma's superiority lies in its performance that can rival popular models almost twice its size. It is not only small but also flexible, suitable for various scenarios, supporting customizable output dimensions from 768 to 128, and featuring a 2000-context token window, allowing it to run on everyday devices such as smartphones, laptops, and desktops. In addition, it integrates with various popular tools, enabling users to easily collaborate with tools like sentence-transformers, MLX, and Ollama.
EmbeddingGemma performs exceptionally well in building RAG pipelines, capable of generating embeddings for text, converting text into numerical representations to represent its meaning in high-dimensional space. In a RAG pipeline, embeddings are first generated based on user input, and then their similarity with all document embeddings in the system is calculated, retrieving the most relevant passages. This high-quality embedding ensures that the final generated response is accurate and contextually relevant.
In addition, EmbeddingGemma has been carefully designed for speed and resource consumption, offering features such as being compact, fast, and efficient. Its embedding inference time is less than 15 milliseconds, allowing real-time interaction. Its offline functionality ensures the privacy and security of user data, making it particularly suitable for developing mobile device-based applications.
Developers can now use EmbeddingGemma to create personalized chatbots, perform file searches, or quickly fine-tune for specific domains. Whether in offline applications or server-side applications requiring efficient performance, EmbeddingGemma provides an ideal choice.
Official blog: https://developers.googleblog.com/en/introducing-embeddinggemma/
Key points:
🌟 EmbeddingGemma is an open-source embedding model with 308M parameters, specifically designed for mobile devices, and can run without an internet connection.
📱 It supports integration with multiple tools, flexibly adapting to various application scenarios to meet developers' needs.
🔒 Powerful offline functionality ensures user data security, enhancing privacy protection, and providing reliable support for mobile applications.
