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README.md

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-license: mit
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+---
+license: mit
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+---
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+`CodeRankLLM` is a 7B LLM fine-tuned for listwise code-reranking. When combined with performant code retrievers like [`CodeRankEmbed`](https://huggingface.co/cornstack/CodeRankEmbed), it significantly enhances the quality of retrieved results for various code retrieval tasks. 
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+We release the scripts to evaluate our model's performance [here](https://github.com/gangiswag/cornstack).
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+## Training
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+Our code reranker is based on LLM-based listwise reranking, which has gained prominence for the ability to score multiple passages simultaneously. Training data for listwise reranking was generated by selecting 50,000 <query, positive, negatives> tuples from our high-quality dataset [CoRNStack](https://gangiswag.github.io/cornstack/), filtered to ensure higher similarity scores and better ranks for the positives. Since CoRNStack doesn't contain the ranked ordering data required for training listwise rerankers, we leverage [Qwen-2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) LLM provided ranked orderings for each example to serve as ranking supervision. We initialize our reranker with [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) and fine-tune using a language modeling objective that minimizes the prediction error of the next token in the sequence.