# Model2Vec-rs

> A Rust crate and CLI for fast inference with Model2Vec static embedding models.

Model2Vec-rs is the official Rust implementation of Model2Vec inference, published by MinishLab (Minish) as an MIT-licensed crate. It generates text embeddings from Model2Vec static embedding models, either inside Rust applications or through a command-line tool. The latest release listed is v0.3.0.

## What It Is

Model2Vec-rs is an inference engine for Model2Vec static embedding models, which are compact models distilled from sentence transformers. It handles inference only. Distillation, training and fine-tuning stay in the Python model2vec package. Developers load a model by Hugging Face Hub ID or from a local path, then encode sentences into embeddings returned as vectors of f32 values.

## How You Use It

As a library, you add the crate with cargo, load a model with `StaticModel::from_pretrained`, and call `encode` or `encode_with_args`. The second call sets a maximum token length and batch size. Defaults are a max length of 512 and a batch size of 1024. As a CLI, you install with the `cli` feature. `encode-single` prints one embedding as JSON, and `encode` reads a text file and writes embeddings to a JSON output file.

## Deployment Options and Performance

Models can be loaded from raw bytes or owned/borrowed data, which suits embedded and WebAssembly use. Feature flags include `local-only` to disable remote downloads, `fancy-regex` as an alternative tokenizer backend, and `wasm` for in-memory loading. Supported weight types are f32, f16 and i8 in safetensors files. The README reports a single-threaded CPU benchmark of 8000 samples per second for Rust against 4650 for Python, roughly 1.7x faster. Its model table lists English, multilingual and code-focused potion models.

## Features
- Fast Rust inference for Model2Vec static embeddings
- Load models from Hugging Face Hub or local paths
- Supports f32, f16 and i8 weights in safetensors files
- Load from bytes, owned or borrowed data for embedded and WASM use
- Batch encoding with configurable max length and batch size
- CLI for encoding single sentences or files to JSON
- Feature flags: cli, fancy-regex, local-only, wasm

## Integrations
Hugging Face Hub, Model2Vec, crates.io

## Platforms
API, DEVELOPER_SDK, CLI

## Pricing
Open Source

## Version
v0.3.0

## Links
- Website: https://minish.ai/packages/model2vec-rs/usage
- Documentation: https://minish.ai/packages/model2vec-rs/usage
- Repository: https://github.com/MinishLab/model2vec-rs
- EveryDev.ai: https://www.everydev.ai/tools/model2vec-rs