初始化项目，由ModelHub XC社区提供模型

Model: BytedTsinghua-SIA/RL-MemoryAgent-7B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+---
+
+## Model Description
+
+The **RL-MemAgent-7B** is a part of the **MemAgent** framework, which enables Large Language Models (LLMs) to process arbitrarily long texts through end-to-end Reinforcement Learning without altering their core architecture.
+
+
+
+## Usage
+
+This model is ideal for tasks requiring the understanding and processing of very long documents, such as comprehensive question answering, summarizing extensive reports, or analyzing large codebases.
+
+For detailed instructions on how to use, evaluate, and train models within the MemAgent framework, please refer to the main [MemAgent GitHub repository](https://github.com/BytedTsinghua-SIA/MemAgent).
+
+
+## Links
+
+* **Paper:** [https://arxiv.org/abs/2507.02259](https://arxiv.org/abs/2507.02259)
+* **Blog:** [https://memagent-sialab.github.io/](https://memagent-sialab.github.io/)
+* **GitHub:** [https://github.com/BytedTsinghua-SIA/MemAgent](https://github.com/BytedTsinghua-SIA/MemAgent)
+
+## Citation
+
+If you find this work useful, please consider citing our paper:
+
+```bibtex
+@article{yu2025memagent,
+  title={MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent},
+  author={Yu, Hongli and Chen, Tinghong and Feng, Jiangtao and Chen, Jiangjie and Dai, Weinan and Yu, Qiying and Zhang, Ya-Qin and Ma, Wei-Ying and Liu, Jingjing and Wang, Mingxuan and others},
+  journal={arXiv preprint arXiv:2507.02259},
+  year={2025}
+}
+```