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TL;DR
"pylate-rs is a lightweight Rust rewrite of PyLate that runs ColBERT models 97% faster by dropping PyTorch/Transformers dependencies. Built with Candle, it works in Python, Rust, and even browsers (WebAssembly).
Key features: ultra-fast ColBERT inference, token pooling for efficiency, and integration with fast-plaid for vector search. Perfect for production deployments where you need millisecond model initialization without the heavy ML framework overhead.
Essentially: ColBERT inference that's fast enough for real-time applications."
PyLate, is a powerful tool for research and training with ColBERT developed at LightOn. It carries a heavy set of dependencies. That's fine for most environments and especially to train state-of-the-art information retrieval models, but it can be a real headache when you just want to run inference in a live application and spawn your model in milliseconds.That's why we built pylate-rs. The main difference is that we've completely removed the PyTorch and Transformers dependencies. Instead, we went a different route and built it with Candle, the deep-learning crate made with Rust. The goal was to create a focused, lightweight tool that does one thing well: compute ColBERT embeddings.
To continue, go read this blog entry.
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