| license: cc-by-nc-4.0 | |
| base_model: | |
| - nomic-ai/CodeRankLLM | |
| `SweRankLLM-Small` is a 7B LLM based on `Qwen2.5-Coder-7B-Instruct` finetuned for listwise code-reranking. When combined with performant code retrievers like `SweRankEmbed`, it significantly enhances the quality of results for software issue localization. | |
| The model has been trained on large-scale issue localization data collected from public python github repositories. Check out our [blog post](https://gangiswag.github.io/SweRank/) and [paper](https://arxiv.org/abs/2505.07849) for more details! | |
| We release the scripts to evaluate our model's performance [here](https://github.com/gangiswag/SweRank?tab=readme-ov-file#swerankllm-evaluation-reranking). | |
| ## Citation | |
| If you find this model work useful in your research, please consider citing our paper: | |
| ``` | |
| @article{reddy2025swerank, | |
| title={SweRank: Software Issue Localization with Code Ranking}, | |
| author={Reddy, Revanth Gangi and Suresh, Tarun and Doo, JaeHyeok and Liu, Ye and Nguyen, Xuan Phi and Zhou, Yingbo and Yavuz, Semih and Xiong, Caiming and Ji, Heng and Joty, Shafiq}, | |
| journal={arXiv preprint arXiv:2505.07849}, | |
| year={2025} | |
| } | |
| ``` |