---
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
tags:
- multimodal
- reasoning
- arxiv:2505.14677
---


### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/maifoundations/Visionary-R1
- **Paper:** https://arxiv.org/pdf/2505.14677
- **Blog:** https://www.maifoundations.com/blog/visionary-r1/

## Quick Start
The model is trained based on the Qwen2.5-VL-3B-Instruct. Here we present an example of the use of inference.
```
import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info


model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "maifoundations/Visionary-R1",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

# default processer
processor = AutoProcessor.from_pretrained("maifoundations/Visionary-R1")

SYSTEM_PROMPT = (
    '''You are tasked with analyzing an image to generate an exhaustive and detailed description. Your goal is to extract and describe all possible information from the image, including but not limited to objects, numbers, text, and the relationships between these elements. The description should be as fine and detailed as possible, capturing every nuance. After generating the detailed description, you need to analyze it and provide step-by-step detailed reasoning for the given question based on the information. Finally, provide a single word or phrase answer to the question. The description, reasoning process and answer are enclosed within <info> </info>, <think> </think> and <answer> </answer> tags, respectively, i.e., <info> image description here </info> <think> reasoning process here </think> <answer> answer here </answer>.
    '''
)

messages = [
    {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": image_path,
            },
            {"type": "text", "text": question},
        ],
    }
]

# Preparation for inference
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("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=512)
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)
```

## Citation 

```
@article{xia2025visionary,
  title={Visionary-R1: Mitigating Shortcuts in Visual Reasoning with Reinforcement Learning},
  author={Xia, Jiaer and Zang, Yuhang and Gao, Peng and Li, Yixuan and Zhou, Kaiyang},
  journal={arXiv preprint arXiv:2505.14677},
  year={2025}
}
```