# Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval

arXiv: https://arxiv.org/abs/2411.04677 · HTML: https://mlsanity.com/paper/2411.04677/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2024.

## Built on by (most cited first, 2 of 2)

- [Investigating Task Arithmetic for Zero-Shot Information Retrieval](https://mlsanity.com/paper/2505.00649/) (2025)
- [The Viability of Crowdsourcing for RAG Evaluation](https://mlsanity.com/paper/2504.15689/) (2025)

## Abstract (arXiv)

A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based language models in retrieval scenarios. Lightning IR provides a modular and extensible architecture that supports all stages of a retrieval pipeline: from fine-tuning and indexing to searching and re-ranking. Designed to be scalable and reproducible, Lightning IR is available as open-source: https://github.com/webis-de/lightning-ir.