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--- |
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license: apache-2.0 |
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base_model: |
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- Qwen/Qwen2.5-VL-3B-Instruct |
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--- |
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# Shuffle-R1-Qwen-3B |
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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) |
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## Model Performance |
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| Model | MathVerse | MathVision | MathVista (mini) | WeMath (loose) | HallusionBench | ChartQA | Avg. | |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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| Qwen2.5-VL-3B | 34.8 | 21.9 | 58.4 | 51.7 | 59.8 | 73.1 | 49.9 | |
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| Qwen2.5-VL-7B | 42.6 | 25.8 | 67.4 | 63.5 | 65.2 | 79.8 | 57.4 | |
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| Shuffle-R1-3B | 44.2 | 26.8 | 70.4 | 66.5 | 69.2 | 79.9 | 59.5 | |
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| Shuffle-R1-7B | 53.9 | 30.0 | 77.0 | 72.3 | 71.0 | 84.1 | 64.7 | |
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All models are evaluated under CoT prompt. |
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## Inference |
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### Using *Transformers* |
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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. |
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``` |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model_path = "path/to/your/checkpoint" |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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model_path, |
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torch_dtype=torch.bfloat16, |
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attn_implementation="flash_attention_2", |
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device_map="auto", |
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) |
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processor = AutoProcessor.from_pretrained(model_path) |
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system_prompt = """ |
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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{}. |
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""" |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image", "image": "path/to/your/image"}, |
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{"type": "text", "text": system_prompt + "YOUR TEXT QUERY HERE"}, |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to(model.device) |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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### Using *vLLM* |
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Our model also supports inference using [**vLLM**](https://github.com/vllm-project/vllm). |
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Please refer to our [**Official Repo**](https://github.com/xiaomi-research/shuffle-r1) for detailed instructions. |
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## Citation |
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If you find our work useful for your research, please consider citing: |
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``` |
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@misc{zhu2025shuffler1, |
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title={Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle}, |
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author={Linghao Zhu, Yiran Guan, Dingkang Liang, Jianzhong Ju, Zhenbo Luo, Bin Qin, Jian Luan, Yuliang Liu, Xiang Bai}, |
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year={2025}, |
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eprint={2508.05612}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2508.05612}, |
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} |
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``` |
