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Here we show a code snippet to show you how to use the model with transformers for inference. |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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instruct_prompt = r"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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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"furonghuang-lab/ThinkLite-VL-7B", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("furonghuang-lab/ThinkLite-VL-7B") |
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greedy_generation_config = GenerationConfig( |
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do_sample=False, |
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max_new_tokens=2048 |
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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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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Describe this image." + instruct_prompt}, |
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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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inputs = processor( |
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text=text, |
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images=image_inputs, |
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padding=True, |
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return_tensors="pt", |
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).to("cuda") |
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output = model.generate( |
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**inputs, |
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generation_config=greedy_generation_config, |
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tokenizer=processor.tokenizer |
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) |
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output_text = processor.decode( |
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output[0], |
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skip_special_tokens=True, |
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clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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If you found this work useful, consider citing our paper as followed: |
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``` |
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@article{wang2025sota, |
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title={SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement}, |
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author={Wang, Xiyao and Yang, Zhengyuan and Feng, Chao and Lu, Hongjin and Li, Linjie and Lin, Chung-Ching and Lin, Kevin and Huang, Furong and Wang, Lijuan}, |
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journal={arXiv preprint arXiv:2504.07934}, |
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year={2025} |
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} |
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``` |