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…
Model2vec – Lightning-fast Static Embeddings for RAG/Semantic Search launched on November 17, 2024 with 28 votes, #650 of 4,990 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
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
Fast State-of-the-Art Static Embeddings. Contribute to MinishLab/model2vec development by creating an account on GitHub.
Model2Vec is a technique to turn any sentence transformer into a small, fast static embedding model. Model2Vec reduces model size by a factor up to 50 and makes models up to 500 times faster, with a small drop in performance. Our best model is the most performant static embedding model in the world. See our results , read our docs , or dive in to see how it works. pip install model2vec You can start using Model2Vec by loading one of our flagship models from the HuggingFace hub . These models are pre-trained and ready to use. The following code snippet shows how to load a model and make embeddings, which you can use for any task, such as text classification, retrieval, clustering, or…from github.com
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…
Does a similar job
all alternatives →
Model2vec-Rs – Fast Static Text Embeddings in Rust2025 · github.com · ▲60Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance:…
Model2Vec: make sentence transformers 500x faster on CPU, 15x smaller2024 · github.com · ▲6Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This reduction of course comes at a cost: distilled models are a lot worse than their parent models. Even so, they are actually a lot better than large sets of conventional static embeddings, such as GLoVe or word2vec-based models, which are many…
Model2Vec: make sentence transformers 500x faster on CPU, 15x smaller2024 · github.com · ▲9Hi HN! We (Thomas and Stéphan, hello!) recently released Model2Vec, a Python library for distilling any sentence transformer into a small set of static embeddings. This makes inference with such a model up to 500x faster, and reduces model size by a factor of 15 (7.5M params or 15/30MB on disk, depending on whether you use float16 or float32). This allows you to embed 50-100k documents per second on a cpu on a macbook. This reduction of course comes at a cost: distilled models are worse than their parent models. Even so, they are actually a lot better than large sets of conventional…
- WAWikipedia2Vec – A tool for learning embeddings of words and entities2018 · wikipedia2vec.github.io · ▲105
- SMSense2vec model trained on all 2015 Reddit comments2016 · sense2vec.spacy.io · ▲225
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