czlll
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Qwen2.5-Coder-32B-CL

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 ---
 library_name: transformers
 tags:
 - unsloth
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+pipeline_tag: text-generation
 tags:
 - unsloth
 ---
+# Model Card for LocAgent
+This model is described in the paper [LocAgent: Graph-Guided LLM Agents for Code Localization](https://huggingface.co/papers/2503.09089). LocAgent uses a graph-based code representation to enable LLMs to perform accurate code localization, significantly improving accuracy compared to existing methods. Notably, the fine-tuned Qwen-2.5-Coder-Instruct-32B model achieves near state-of-the-art performance with a substantial cost reduction.
+Code: https://github.com/gersteinlab/LocAgent
+## How to Use
+LocAgent involves two main steps: graph indexing and code localization.
+**1. Graph Indexing (Optional but Recommended):**  For efficient batch processing, pre-generate graph indexes for your codebase using `dependency_graph/batch_build_graph.py`. This script parses the codebase into a graph representation.  See the Github README for detailed command-line arguments and setup instructions.  Example:
+```bash
+python dependency_graph/batch_build_graph.py \
+       --dataset 'czlll/Loc-Bench' \
+       --split 'test' \
+       --num_processes 50 \
+       --download_repo
+```
+**2. Code Localization:** Use `auto_search_main.py` to perform code localization.  This script leverages LLMs to search and locate relevant code entities within the pre-generated graph indexes.  See the Github README for detailed command-line arguments and environment variable setup. Example:
+```bash
+python auto_search_main.py \
+   --dataset 'czlll/SWE-bench_Lite' \
+   --split 'test' \
+   --model 'azure/gpt-4o' \
+   --localize \
+   --merge \
+   --output_folder $result_path/location \
+   --eval_n_limit 300 \
+   --num_processes 50 \
+   --use_function_calling \
+   --simple_desc
+```
+**3. Evaluation:** After localization, evaluate the results using `evaluation.eval_metric.evaluate_results`. An example Jupyter Notebook is provided in `evaluation/run_evaluation.ipynb`.
+## Citation
+```bibtex
+@article{chen2025locagent,
+  title={LocAgent: Graph-Guided LLM Agents for Code Localization},
+  author={Chen, Zhaoling and Tang, Xiangru and Deng, Gangda and Wu, Fang and Wu, Jialong and Jiang, Zhiwei and Prasanna, Viktor and Cohan, Arman and Wang, Xingyao},
+  journal={arXiv preprint arXiv:2503.09089},
+  year={2025}
+}
+```