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Dev tools · January 8, 2025

gline-rs – an inference engine for GLiNER models, in Rust

Hi everyone, wanted to share about gline-rs, an inference engine for GLiNER models written in Rust. This family of lightweight language models proved to be efficient at zero-shot Named Entity Recognition (NER) and other tasks such as Relation Extraction, while consuming less resources than large generative models (LLMs). This implementation has been written from the ground up in Rust, and supports both span- and token-oriented variants (for inference only). The goal is to provide a production-grade and user-friendly API in a modern and safe programming language, including a clean and…

gline-rs – an inference engine for GLiNER models, in Rust launched on January 8, 2025 with 5 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.

What it does

Inference engine for GLiNER models, in Rust. Contribute to fbilhaut/gline-rs development by creating an account on GitHub.

gline-rs is an inference engine for GLiNER models. These language models proved to be efficient at zero-shot Named Entity Recognition (NER) and other tasks such as Relation Extraction , while consuming less resources than large generative models (LLMs). This implementation has been written from the ground up in Rust, and supports both span- and token-oriented variants (for inference only). It is based on a flexible pipeline architecture with ready-to-use implementations for NER and Relation Extraction. It aims to provide a production-grade and user-friendly API in a modern and safe programming language, including a clean and maintainable implementation of the mechanics surrounding these…from github.com

In the maker’s words, at launch

Hi everyone, wanted to share about gline-rs, an inference engine for GLiNER models written in Rust. This family of lightweight language models proved to be efficient at zero-shot Named Entity Recognition (NER) and other tasks such as Relation Extraction, while consuming less resources than large generative models…

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