Google has launched EmbeddingGemma: An Efficient Text Embedding Model for Mobile Devices.

Fresh news from Google’s Deep Learning team: they have officially launched EmbeddingGemma, an open-source embedding model designed specifically for mobile devices. With its highly efficient 308 million parameters, EmbeddingGemma has been named the best multilingual text embedding model under 500M on the MTEB (Massive Text Embedding Benchmark). It showcases powerful capabilities such as Retrieval-Augmented Generation (RAG) and semantic search, and can run directly on devices like mobile phones without an internet connection.

Key Advantages

• Exceptional Performance: Its performance is comparable to popular models nearly twice its size.
• Compact and Flexible: The model is small yet adaptable, supporting customizable output dimensions from 768 to 128 and featuring a 2000-context-token window, allowing it to run on everyday devices like phones, laptops, and desktops.
• Tool Integration: It integrates with various popular tools, making it easy for users to work with sentence-transformers, MLX, and Ollama.

Core Functionality

EmbeddingGemma excels at creating RAG pipelines by generating text embeddings, which convert text into a numerical representation that captures its meaning in a high-dimensional space. In a RAG pipeline, an embedding is first generated from a user’s input, and its similarity is then calculated against the embeddings of all documents in the system to retrieve the most relevant passages. These high-quality embeddings ensure that the final answer is accurate and contextually relevant.

Furthermore, EmbeddingGemma is meticulously designed for speed and low resource consumption, making it compact, fast, and efficient. Its embedding inference time is under 15 milliseconds, enabling real-time interaction. Its offline capability ensures user data privacy and security, making it particularly well-suited for developing mobile applications.

Developer Opportunities

Developers can now leverage EmbeddingGemma to create personalized chatbots, perform local file searches, or quickly fine-tune for specific domains. Whether for offline applications or server-side applications requiring high performance, EmbeddingGemma is an ideal choice.