Model2vec – Lightning-fast Static Embeddings for RAG/Semantic Search
We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
In plain words
Model2vec is an open-source method for converting sentence transformers into lightweight static embeddings for semantic search and retrieval-augmented generation. The best model contains only 8 million parameters and runs approximately 500 times faster than comparable models on CPUs. It requires minimal dependencies, can be distilled in 30 seconds using only a vocabulary, and achieves state-of-the-art performance on embedding benchmarks. The tool is designed for developers who need efficient, hardware-friendly alternatives to larger embedding models.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal dependencies for lightweight deployments - The library is integrated in SentenceTransformers, making it easy to use with other popular libraries We built this because we think static embeddings can provide a hardware friendly alternative to many of the larger embedding models out there, while still being performant enough to power usecases such as RAG, or semantic search. We are curious to hear your feedback on this and whether there’s any usecases you can think of that we have not explored yet! Link to the code and results: https://github.com/MinishLab/model2vec
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