Source URL: https://simonwillison.net/2025/Sep/4/embedding-gemma/#atom-everything
Source: Simon Willison’s Weblog
Title: Introducing EmbeddingGemma
Feedly Summary: Introducing EmbeddingGemma
Brand new open weights (under the slightly janky Gemma license) 308M parameter embedding model from Google:
Based on the Gemma 3 architecture, EmbeddingGemma is trained on 100+ languages and is small enough to run on less than 200MB of RAM with quantization.
It’s available via sentence-transformers, llama.cpp, MLX, Ollama, LMStudio and more.
As usual for these smaller models there’s a Transformers.js demo (via) that runs directly in the browser (in Chrome variants) – Semantic Galaxy loads a ~400MB model and then lets you run embeddings against hundreds of text sentences, map them in a 2D space and run similarity searches to zoom to points within that space.
Tags: google, ai, embeddings, transformers-js, gemma
AI Summary and Description: Yes
Summary: The text introduces EmbeddingGemma, an efficient open-source embedding model designed for multilingual applications. Its lightweight architecture and integration with various platforms make it particularly relevant for AI professionals focusing on natural language processing and embedding techniques.
Detailed Description: The content highlights EmbeddingGemma, a new embedding model from Google that has implications for AI, particularly in natural language processing tasks. Here are the major points of significance:
– **Model Overview**:
– EmbeddingGemma is a 308M parameter embedding model.
– It is based on the Gemma 3 architecture, known for its performance and efficiency.
– **Efficiency**:
– The model is designed to be lightweight, operating on less than 200MB of RAM when quantized. This is critical for deployment in environments with limited resources.
– **Multilingual Capability**:
– The model is trained on over 100 languages, making it versatile for global applications.
– **Accessibility**:
– Available through various platforms including:
– sentence-transformers
– llama.cpp
– MLX
– Ollama
– LMStudio
– A diverse range of integrations enhances its usability across different systems.
– **Interactive Tools**:
– The model features a demo via Transformers.js that functions directly in the browser.
– Users can load a ~400MB model and perform embeddings on multiple text sentences, which can be visualized in a 2D space for similarity searches.
– **Practical Implications**:
– The availability of such models democratizes access to advanced AI capabilities, enabling developers to incorporate sophisticated embedding techniques into their applications without heavy infrastructure costs.
– The multilingual support broadens market access and user engagement in diverse linguistic settings.
This introduction of EmbeddingGemma represents a notable advancement in accessible AI technologies, offering security and compliance professionals opportunities to innovate and adapt language processing solutions effectively.