MinishLab/model2vec-rs: Official Rust Implementation of Model2Vec

This crate provides a lightweight Rust implementation for loading and inference of Model2Vec static embedding models. For distillation and training, the Python Model2Vec package can be used.

Add the crate:

Make embeddings:

use anyhow::Result;
use model2vec_rs::model::StaticModel;

fn main() -> Result()> {
    // Load a model from the Hugging Face Hub or a local path
    // args = (repo_or_path, token, normalize, subfolder)
    let model = StaticModel::from_pretrained("minishlab/potion-base-8M", None, None, None)?;

    // Prepare a list of sentences
    let sentences = vec![
        "Hello world".to_string(),
        "Rust is awesome".to_string(),
    ];

    // Create embeddings
    let embeddings = model.encode(&sentences);
    println!("Embeddings: {:?}", embeddings);

    Ok(())
}

Make embeddings with the CLI:

# Single sentence
cargo run -- encode "Hello world" minishlab/potion-base-8M

# Multiple lines from a file
echo -e "Hello world\nRust is awesome" > input.txt
cargo run -- encode input.txt minishlab/potion-base-8M --output embeds.json

Make embeddings with custom encode args:

let embeddings = model.encode_with_args(
    &sentences,     // input texts
    Some(512),  // max length
    1024,       // batch size
);

We provide a number of models that can be used out of the box. These models are available on the HuggingFace hub and can be loaded using the from_pretrained method. The models are listed below.

We compared the performance of the Rust implementation with the Python version of Model2Vec. The benchmark was run single-threaded on a CPU.

The Rust version is roughly 1.7× faster than the Python version.

MIT

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