Model2vec-Rs – Fast Static Text Embeddings in Rust

In plain words

Model2vec-rs is a Rust library for generating static text embeddings without Python dependencies. It loads Model2Vec models from Hugging Face or local paths and runs inference at high throughput in microservices, CLI tools, and applications requiring semantic search, retrieval, or retrieval-augmented generation. The crate is lightweight at 1.7 MB with embedding models ranging from 7 to 30 MB, delivering approximately 1.7 times faster performance than Python implementations on single-threaded CPU inference.

written from the facts on this page · September 2026

From the sources

Official Rust Implementation of Model2Vec. Contribute to MinishLab/model2vec-rs development by creating an account on GitHub.

model2vec-rs is a Rust crate providing an efficient implementation for inference with Model2Vec static embedding models. Model2Vec is a technique for creating compact and fast static embedding models from sentence transformers, achieving significant reductions in model size and inference speed. This Rust crate is optimized for performance, making it suitable for applications requiring fast embedding generation. Integrate model2vec-rs into your Rust application to generate embeddings within your code. cargo add model2vec-rs b. Load a model and generate embeddings: use anyhow :: Result ; use model2vec_rs :: model :: StaticModel ; fn main ( ) -> Result < ( ) > { // Load a model from the…from github.com

In the maker’s words, at launch

Hey 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…

• 
  Model2vec – Lightning-fast Static Embeddings for RAG/Semantic Search2024 · github.com · ▲28

  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: make sentence transformers 500x faster on CPU, 15x smaller2024 · github.com · ▲9

  Hi 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&#x2F;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…

• 
  Model2Vec: make sentence transformers 500x faster on CPU, 15x smaller2024 · github.com · ▲6

  Hi 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&#x2F;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…

• 
  Word2vec Algorithm in ~100sloc with NumPy2023 · github.com · ▲72

  Here's a small demonstration of the fundamental aspects of the word-to-vec algorithm. It's implemented in a single python script and depends only on a single text file for training. It's not meant to be blazingly fast or anything, just a toy example to aid my understanding of how word vectors might be learnt from a corpus.

• WA
  Wikipedia2Vec – A tool for learning embeddings of words and entities2018 · wikipedia2vec.github.io · ▲105
• SM
  Sense2vec model trained on all 2015 Reddit comments2016 · sense2vec.spacy.io · ▲225

• 
  Spira Maxima▲578

  Video model that turns script into viral social media videos

  AI · 17d ago · spira.ai

• 
  Rill Browser▲540

  The browser where Claude Code and Codex work beside you

  AI · 16d ago · rill.love

• 
  Germany's new sovereign AI model Kolibri▲424

  Kolibri is Aleph Alpha

  AI · 18d ago · tej.as

• 
  Giving Opus 5.5 a simulated paint canvas▲392

  Paintings by AI models: programs run through a simulation of oil paint.

  AI · 20d ago · stillwet.art

• 
  Onepin▲384

  AI voiceover with every name, price and date said right

  AI · 30d ago · onepin.ai

• 
  PixVerse R2▲381

  A real-time world model you can explore and change

  AI · 28d ago · world.pixverse.video

• 
  Tapflow 2.0▲1,125

  We built Tapflow for devs, designers, PMs, and tech folks who’ve been sitting on internal docs, templates, and systems — but never productized them. Upload a PDF, notes, or workshop deck, and Tapflow drafts a structured product (with chapters, teaser and pricing plans). It’s good for AI kits, onboarding guides, design systems, and frameworks. You can sell through Stripe, LemonSqueezy, or PayPal. Real-time collaboration is in early beta. No site builder, no LMS — clean design and just focused tools to ship. Fully bootstrapped and just launched on PH today →…

  Work · 2025 · tapflow.ai

• C9
  Clippy – 90s UI for local LLMs▲1,122

  Life & fun · 2025 · felixrieseberg.github.io

• 
  Appwrite Sites▲990

  Appwrite 2.0 brings a new engine, a rebuilt Console, five database types, an S3 API, an OAuth 2.1 server, and organization-level Domains and Firewall.

  Dev tools · 2025 · appwrite.io

• 
  AI Meeting Notes by Notion▲965

  Perfect meeting memory in Notion

  AI · 2025 · notion.com

• 
  Den▲948

  Multiplayer AI agent builder

  AI · 2025 · getden.io

• 
  Raycast for iOS▲870

  Powerful productivity on the go

  AI · 2025 · raycast.com