初始化项目，由ModelHub XC社区提供模型

Model: OpenHands/CodeScout-4B
Source: Original Platform

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README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model: Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+tags:
+- code-search
+- code-localization
+- reinforcement-learning
+- agent
+- software-engineering
+- GSPO
+- OpenHands
+- SWE-Bench
+datasets:
+- OpenHands/SWE-smith-py-code-search
+- OpenHands/SWE-Gym-code-search
+- OpenHands/CodeScout_Training_Rollouts
+---
+
+# CodeScout-4B
+
+[📄 Paper](https://arxiv.org/abs/2603.17829) • [💻 Code](https://github.com/OpenHands/codescout) • [🤗 Collection](https://huggingface.co/collections/OpenHands/codescout-69b9a6adcf21f348f4db937f)
+
+**Best efficiency–performance trade-off — outperforms 8× larger Qwen3-32B across all benchmarks.**
+
+<p align="center">
+  <img src="codescout_overview.png" alt="CodeScout Overview" width="100%">
+</p>
+
+CodeScout-4B is part of the **CodeScout** family of open-source RL-trained code search agents.
+CodeScout models achieve state-of-the-art repository-level code localization using *nothing more than a standard Unix terminal* — no static analysis, no repository graphs, no language-specific tooling.
+
+## Key Highlights
+
+- Consistently **outperforms 8× larger Qwen3-32B** on all benchmarks
+- Surpasses RepoNavigator-14B by **2–10%** in file F1 and **8–11%** in function F1
+- Exceeds GPT-5 with RepoNavigator by **9%** in file F1 and **5%** in function F1 on SWE-Bench Verified
+- Best efficiency–performance trade-off in the CodeScout family
+
+## Results
+
+Performance on SWE-Bench code localization (instance-averaged F1 scores):
+
+
+| Benchmark | CodeScout-1.7B | CodeScout-4B | CodeScout-14B |
+|---|---|---|---|
+| **SWE-Bench Verified** — File F1 | 55.46 | 68.52 | **68.57** |
+| **SWE-Bench Verified** — Func F1 | 28.22 | 36.78 | **40.32** |
+| **SWE-Bench Pro** — File F1 | 40.96 | 51.77 | **53.63** |
+| **SWE-Bench Pro** — Func F1 | 18.24 | **29.03** | 28.74 |
+| **SWE-Bench Lite** — File F1 | 56.57 | 67.03 | **71.84** |
+| **SWE-Bench Lite** — Func F1 | 27.07 | 39.87 | **44.43** |
+
+
+<p align="center">
+  <img src="f1_vs_params_file.png" alt="File-level F1 vs Model Size" width="48%">
+  <img src="f1_vs_params_function.png" alt="Function-level F1 vs Model Size" width="48%">
+</p>
+
+<p align="center"><em>Code localization performance on SWE-Bench Verified. CodeScout (⭐) achieves superior or competitive results over larger open-source LLMs and narrows the gap with closed-source frontier models.</em></p>
+
+## Training
+
+CodeScout-4B is trained directly from `Qwen3-4B-Instruct-2507` using GSPO reinforcement learning.
+
+- **Training data:** 1,600 instances from [SWE-Smith](https://huggingface.co/datasets/OpenHands/SWE-smith-py-code-search) (39K filtered, 128 repos)
+- **RL steps:** 200
+- **Batch size:** 8, with 8 rollouts per instance
+- **Max context length:** 40K tokens
+- **Max turns per episode:** 6
+- **Reward:** Multi-level F1 (file + module + function)
+- **Hardware:** 8×H100 GPUs
+- **Learning rate:** 1e-6 (constant)
+
+## How It Works
+
+CodeScout uses the **OpenHands-Bash** scaffold — an agent equipped with only a `Terminal` tool (supporting standard Unix commands like `rg`, `find`, `grep`, `ls`) and a `LocalizationFinish` tool for structured output submission. The agent iteratively navigates the repository to identify relevant files, classes, and functions related to a given issue.
+
+The model is trained with **GSPO** (Group Sequence Policy Optimization) using multi-level F1 rewards at the file, module, and function level.
+
+## Intended Use
+
+CodeScout-4B is designed for **repository-level code localization**: given a GitHub issue description and a code repository, it identifies the relevant files, classes, and functions that need to be modified. It is intended to be used as a localization subagent within larger coding agent pipelines.
+
+## Limitations
+
+- Trained and evaluated exclusively on **Python** repositories
+- Designed for code *localization*, not code *editing* or issue resolution
+- Performance may vary on repositories significantly different from the training distribution
+- Requires the OpenHands-Bash scaffold for optimal performance
+
+## Citation
+
+```bibtex
+@misc{sutawika2026codescouteffectiverecipereinforcement,
+      title={CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents}, 
+      author={Lintang Sutawika and Aditya Bharat Soni and Bharath Sriraam R R and Apurva Gandhi and Taha Yassine and Sanidhya Vijayvargiya and Yuchen Li and Xuhui Zhou and Yilin Zhang and Leander Melroy Maben and Graham Neubig},
+      year={2026},
+      eprint={2603.17829},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE},
+      url={https://arxiv.org/abs/2603.17829}, 
+}
+```

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special_tokens_map.json

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tokenizer_config.json

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