---
library_name: transformers
pipeline_tag: text-generation
tags:
- QwenLong
- AWQ
- 量化修复
- vLLM
base_model:
  - iic/QwenLong-L1-32B
base_model_relation: quantized
---
# 通义智文-QwenLong-L1-32B-AWQ
基础型 [iic/QwenLong-L1-32B](https://www.modelscope.cn/models/iic/QwenLong-L1-32B)

### 【模型更新日期】
``` 
2025-05-28
1. 首次commit
```

### 【依赖】

```
vllm==0.8.5
transformers==4.51.3
```

<div style="
    background: rgba(255, 193, 61, 0.15);
    padding: 16px;
    border-radius: 6px;
    border: 1px solid rgba(255, 165, 0, 0.3);
    margin: 16px 0;
">
### 【💡新版 VLLM 注意事项💡】

#### 1. 需使用V0推理模式
启动vllm之前，先设置环境变量
```
export VLLM_USE_V1=0
```
</div>


### 【模型列表】

| 文件大小   | 最近更新时间       |
|--------|--------------|
| `19GB` | `2025-05-21` |



### 【模型下载】

```python
from modelscope import snapshot_download
snapshot_download('tclf90/QwenLong-L1-32B-AWQ', cache_dir="本地路径")
```


### 【介绍】
## QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning

<p align="center" width="100%">
</p>

<div id="top" align="center">

-----------------------------
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![arXiv](https://img.shields.io/badge/arXiv-2505.17667-b31b1b.svg)](https://arxiv.org/abs/2505.17667)
[![GitHub](https://img.shields.io/badge/GitHub-QwenLongL1-4b32c3?logo=github)](https://github.com/Tongyi-Zhiwen/QwenLong-L1)
[![ModelScope](https://img.shields.io/badge/🤖%20ModelScope-purple)](https://modelscope.cn/models/iic/QwenLong-L1-32B)
[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-yellow)](https://huggingface.co/Tongyi-Zhiwen/QwenLong-L1-32B)

<!-- **Authors:** -->

_**Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li,**_

_**Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan**_


<!-- **Affiliations:** -->


_Tongyi Lab, Alibaba Group_

<p align="center">
    <img src="./assets/fig1.png" width="100%"> <br>
</p>


</div>

## 🎉 News

- **May 26, 2025:** 🔥 We release [🤗 QwenLong-L1-32B](https://huggingface.co/Tongyi-Zhiwen/QwenLong-L1-32B), which is the first long-context LRM trained with reinforcement learniing for long-context reasoning. Experiments on seven long-context DocQA benchmarks demonstrate that **QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini and Qwen3-235B-A22B, achieving performance on par with Claude-3.7-Sonnet-Thinking**, demonstrating leading performance among state-of-the-art LRMs.

- **May 26, 2025:** 🔥 We release [🤗 DocQA-RL-1.6K](https://huggingface.co/datasets/Tongyi-Zhiwen/DocQA-RL-1.6K), which is a specialized RL training dataset comprising 1.6K document question answering (DocQA) problems spanning mathematical, logical, and multi-hop reasoning domains.


## 📚 Introduction

In this work, we propose QwenLong-L1, a novel reinforcement learning (RL) framework designed to facilitate the transition of LRMs from short-context proficiency to robust long-context generalization. In our preliminary experiments, we illustrate the differences between the training dynamics of short-context and long-context reasoning RL.

<p align="center">
    <img src="./assets/fig2.png" width="100%"> <br>
</p>

Our framework enhances short-context LRMs through progressive context scaling during RL training. The framework comprises three core components: a warm-up supervised fine-tuning (SFT) phase to initialize a robust policy, a curriculum-guided RL phase that facilitates stable adaptation from short to long contexts, and a difficulty-aware retrospective sampling mechanism that adjusts training complexity across stages to incentivize policy exploration. Leveraging recent RL algorithms, including GRPO and DAPO, our framework integrates hybrid reward functions combining rule-based and model-based binary outcome rewards to balance precision and recall. Through strategic utilization of group relative advantages during policy optimization, it guides LRMs to learn effective reasoning patterns essential for robust long-context grounding and superior reasoning capabilities. 

<p align="center">
    <img src="./assets/fig3.png" width="100%"> <br>
</p>


## 🎯 Model Release

We release [🤗 QwenLong-L1-32B](https://huggingface.co/Tongyi-Zhiwen/QwenLong-L1-32B), which is the first long-context LRM trained with reinforcement learniing for long-context reasoning. Experiments on seven long-context DocQA benchmarks demonstrate that **QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini and Qwen3-235B-A22B, achieving performance on par with Claude-3.7-Sonnet-Thinking**, demonstrating leading performance among state-of-the-art LRMs.

Here are the evaluation results.

<p align="center">
    <img src="./assets/tab4.png" width="100%"> <br>
</p>

## 📝 Citation

If you find this work is relevant with your research or applications, please feel free to cite our work!
```
@article{wan2025qwenlongl1,
  title={QwenLong-L1: : Towards Long-Context Large Reasoning Models with Reinforcement Learning},
  author={Fanqi Wan, Weizhou Shen, Shengyi Liao, Yingcheng Shi, Chenliang Li, Ziyi Yang, Ji Zhang, Fei Huang, Jingren Zhou, Ming Yan},
  journal={arXiv preprint arXiv:2505.17667},
  year={2025}
}
```
