model2vec-rs

I think you are referring to for "batch in sentences.chunks(batch_size)"? This is not actually chunking sentences, chunks() is simply an iterator over a slice (in this case, a slice of all our input sentences of length batch_size). We don't have an actual constraint on input length. We truncate to 512 tokens by default, but you can easily set that to any amount by directly calling encode_with_args. There's an example in our quickstart: https://github.com/MinishLab/model2vec-rs/tree/main?tab=read....

I believe parent is referring to: https://github.com/MinishLab/model2vec-rs/blob/480ec988d7f4a... https://github.com/MinishLab/model2vec-rs/blob/480ec988d7f4a...

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 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: We benchmarked single-threaded on a CPU: - Python: ~4650 embeddings/sec - Rust: ~8000 embeddings/sec (~1.7× speedup) First open-source project in Rust for us, so would be great to get some feedback!