di-zhang-fdu/Qwen2.5-VL-7B-R1-Distillation

A Large Multimodal Reasoning Model.

Usage

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



# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "di-zhang-fdu/Qwen2.5-VL-7B-R1-Distillation", torch_dtype="auto", device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen2.5-VL-7B-Instruct",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# default processer
processor = AutoProcessor.from_pretrained("di-zhang-fdu/Qwen2.5-VL-7B-R1-Distillation")

# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)

messages = [
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "Your role as an assistant involves thoroughly exploring questions through a systematic long thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution. In the Thought section, detail your reasoning process using the specified format: <think> {thought with steps separated with '\n\n'} </think> Each step should include detailed considerations such as analisying questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The solution should remain a logical, accurate, concise expression style and detail necessary step needed to reach the conclusion, formatted as follows: <answer> {final formatted, precise, and clear solution} </answer> Now, try to solve the following question through the above guidelines:",
                }
            ],
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
                },
                {
                    "type": "text",
                    "text": "Describe this image."
                    + "\nPlease reason step by step, and put your final answer within \boxed{}.",
                },
            ],
        },
        {
            "role": "assistant",
            "content": [
                {
                    "type": "text",
                    "text": "<think>\n",
                }
            ],
        },
    ]


# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=False
)
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("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=5120)
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)

Citations

@misc {di_zhang_2025,
    author       = { {Di Zhang} },
    title        = { Qwen2.5-VL-7B-R1-Distillation (Revision 6cc3c46) },
    year         = 2025,
    url          = { https://huggingface.co/di-zhang-fdu/Qwen2.5-VL-7B-R1-Distillation },
    doi          = { 10.57967/hf/4710 },
    publisher    = { Hugging Face }
}

@article{zhang2024critic,
  title={Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning},
  author={Zhang, Di and Lei, Jingdi and Li, Junxian and Wang, Xunzhi and Liu, Yujie and Yang, Zonglin and Li, Jiatong and Wang, Weida and Yang, Suorong and Wu, Jianbo and others},
  journal={arXiv preprint arXiv:2411.18203},
  year={2024}
}