98 lines
2.8 KiB
Markdown
98 lines
2.8 KiB
Markdown
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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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```
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