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--- |
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base_model: |
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- Qwen/Qwen2.5-VL-7B-Instruct |
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license: apache-2.0 |
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library_name: transformers |
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pipeline_tag: image-text-to-text |
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--- |
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## Overview |
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OpenVLThinker-7B is a vision-language reasoning model designed to handle multimodal tasks. It is especially tuned for visual mathematical problem-solving. |
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For more details: [Blog](https://yihe-deng.notion.site/openvlthinker), [GitHub](https://github.com/yihedeng9/OpenVLThinker) |
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## How to use |
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```python |
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from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration |
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import torch |
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from qwen_vl_utils import process_vision_info |
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import requests |
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from PIL import Image |
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# 1. Define model and processor names |
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model_name = "ydeng9/OpenVLThinker-7B" |
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processor_name = "Qwen/Qwen2.5-VL-7B-Instruct" |
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# 2. Load the OpenVLThinker-7B model and processor |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, |
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attn_implementation="flash_attention_2", |
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device_map=device |
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) |
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processor = AutoProcessor.from_pretrained(processor_name) |
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# 3. Define a sample image URL and an instruction |
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image_url = "https://example.com/sample_image.jpg" # replace with your image URL |
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instruction = "Example question" |
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# 4. Create a multimodal prompt using a chat message structure |
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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": image_url}, |
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{"type": "text", "text": instruction}, |
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], |
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} |
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] |
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# 5. Generate a text prompt from the chat messages |
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text_prompt = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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# 6. Process image (and video) inputs from the messages |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text_prompt], |
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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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).to(device) |
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# 7. Generate the model's response (with specified generation parameters) |
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generated_ids = model.generate( |
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**inputs, |
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do_sample=True, |
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max_new_tokens=2048, |
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top_p=0.001, |
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top_k=1, |
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temperature=0.01, |
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repetition_penalty=1.0, |
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) |
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# 8. Decode the generated tokens into human-readable text |
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generated_text = processor.batch_decode( |
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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)[0] |
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# 9. Print the generated response |
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print("Generated Response:") |
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print(generated_text) |
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``` |
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### Citation |
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```text |
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@misc{deng2025openvlthinker, |
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title={OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement}, |
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author={Yihe Deng and Hritik Bansal and Fan Yin and Nanyun Peng and Wei Wang and Kai-Wei Chang}, |
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year={2025}, |
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eprint={2503.17352}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2503.17352}, |
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} |
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``` |
