Introducing EmbeddingGemma: On-Device Text Embeddings for Generative AI

EmbeddingGemma: State-of-the-Art On-Device Embeddings

• 💡 EmbeddingGemma is a new 300 million parameter text embedding model designed for mobile-first AI and generative AI experiences directly on user hardware.
• 🧠 Embeddings are numerical representations of text, transforming data into vectors that generative models can use for downstream tasks.
• ⚡ The model is small, fast, and efficient, capable of running with as little as 300 megabytes of RAM due to quantization-aware training, while preserving state-of-the-art quality.

Key Features and Capabilities

• 🎯 EmbeddingGemma generates embeddings of 768 dimensions but supports customization down to 128 dimensions using Matryoshka Representation Learning (MRL).
• 🚀 It is based on the same technology as Gemini embedding models, offering high-quality semantic search, fast information retrieval, and customized classification/clustering.
• 🏆 The model achieves the best score on the massive text embedding benchmark for models under 500 million parameters and is trained across 100+ languages.

On-Device Performance and Privacy

• 🔒 Engineered for on-device performance, EmbeddingGemma ensures efficient computations and minimal memory footprint, even on resource-constrained hardware.
• 🛡️ It facilitates on-device embedding of local documents, ensuring sensitive user data never leaves the device.
• 🌐 Offline functionality means search and retrieval features work regardless of internet connectivity.

Building Generative AI Experiences

• 🧩 Together with generative models like Gemma 3N, EmbeddingGemma enables powerful mobile-first generative AI experiences and efficient Retrieval Augmented Generation (RAG) pipelines.
• 💬 Applications can leverage user context from data for more personalized responses, such as understanding a user's need for a carpenter based on context.
• 🌐 An example demo shows a user querying previously opened articles or web pages using a browser extension that utilizes EmbeddingGemma for on-device embedding and retrieval.

Customization and Accessibility

• 🛠️ EmbeddingGemma is designed for customization, allowing users to fine-tune it for their specific domain or language.
• 🤝 It works across popular platforms like Hugging Face and Kaggle, with example notebooks available in the Gemma cookbook.
• ✨ This next generation of on-device embedding models is open for everyone, offering a small, fast, and efficient solution for developers.