---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
---



# Shuffle-R1-Qwen-3B

This is the model checkpoint of Shuffle-R1-Qwen-3B. It is trained based on [**Qwen2.5-VL-3B**](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)

## Model Performance

| Model | MathVerse | MathVision | MathVista (mini) | WeMath (loose) | HallusionBench | ChartQA | Avg. |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Qwen2.5-VL-3B | 34.8 | 21.9 | 58.4 | 51.7 | 59.8 | 73.1 | 49.9 |
| Qwen2.5-VL-7B | 42.6 | 25.8 | 67.4 | 63.5 | 65.2 | 79.8 | 57.4 |
| Shuffle-R1-3B | 44.2 | 26.8 | 70.4 | 66.5 | 69.2 | 79.9 | 59.5 |
| Shuffle-R1-7B | 53.9 | 30.0 | 77.0 | 72.3 | 71.0 | 84.1 | 64.7 |

All models are evaluated under CoT prompt.


## Inference

### Using *Transformers*

The process is the same as [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL). Note that it is better to add a "Thinking prompt" at the begining of user query.

```
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

model_path = "path/to/your/checkpoint"

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained(model_path)

system_prompt = """
You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \\boxed{}.
"""

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/your/image"},
            {"type": "text", "text": system_prompt + "YOUR TEXT QUERY HERE"},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

### Using *vLLM*

Our model also supports inference using [**vLLM**](https://github.com/vllm-project/vllm).

Please refer to our [**Official Repo**](https://github.com/xiaomi-research/shuffle-r1) for detailed instructions.

## Citation
If you find our work useful for your research, please consider citing:
```
@misc{zhu2025shuffler1,
      title={Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle}, 
      author={Linghao Zhu, Yiran Guan, Dingkang Liang, Jianzhong Ju, Zhenbo Luo, Bin Qin, Jian Luan, Yuliang Liu, Xiang Bai},
      year={2025},
      eprint={2508.05612},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.05612}, 
}
```