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--- |
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license: apache-2.0 |
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--- |
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## GitHub |
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https://github.com/Reallm-Labs/Infi-MMR |
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## Inference |
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Our models are established on top of the Qwen2.5-VL family. So we include a simple use case here, and refer the readers to [the standard inference procedure of Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL). |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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# default: Load the model on the available device(s) |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"Reallm-Labs/Infi-MMR-3B", torch_dtype="auto", device_map="auto" |
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) |
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min_pixels = 256*28*28 |
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max_pixels = 1280*28*28 |
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processor = AutoProcessor.from_pretrained("Reallm-Labs/Infi-MMR-3B", min_pixels=min_pixels, max_pixels=max_pixels) |
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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."}, |
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], |
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} |
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] |
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# Preparation for inference |
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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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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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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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) |
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inputs = inputs.to(model.device) |
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# Inference: Generation of the output |
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generated_ids = model.generate(**inputs, max_new_tokens=4096) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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## Citation Information |
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If you find this work useful, we would be grateful if you consider citing the following papers: |
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```bibtex |
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@article{liu2025infimmr, |
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title={Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models}, |
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author={Zeyu Liu and Yuhang Liu and Guanghao Zhu and Congkai Xie and Zhen Li and Jianbo Yuan and Xinyao Wang and Qing Li and Shing-Chi Cheung and Shengyu Zhang and Fei Wu and Hongxia Yang}, |
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journal={arXiv preprint arXiv:2505.23091}, |
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year={2025} |
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} |
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``` |
