106 lines
2.8 KiB
Markdown
106 lines
2.8 KiB
Markdown
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---
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language:
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- en
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library_name: transformers
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license: apache-2.0
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metrics:
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- accuracy
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tags:
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- multimodal
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pipeline_tag: video-text-to-text
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base_model: Qwen/Qwen2.5-VL-7B-Instruct
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---
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# 💡 VideoChat-R1-thinking_7B
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[\[📂 GitHub\]](https://github.com/OpenGVLab/VideoChat-R1)
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[\[📜 Tech Report\]](https://arxiv.org/pdf/2504.06958)
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## 🚀 How to use the model
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We provide a simple installation example below:
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```
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pip install transformers
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pip install qwen_vl_utils
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```
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Then you could use our model:
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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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model_path = "OpenGVLab/VideoChat-R1-thinking_7B"
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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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model_path, torch_dtype="auto", device_map="auto",
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attn_implementation="flash_attention_2"
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)
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# default processer
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processor = AutoProcessor.from_pretrained(model_path)
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video_path = "your_video.mp4"
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question = "Where is the final cup containing the object?"
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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": "video",
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"video": video_path,
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"max_pixels": 360 * 420,
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"fps": 1.0,
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},
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{"type": "text", "text": f"""{question}
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Output your thought process within the <think> </think> tags, including analysis with either specific timestamps (xx.xx) or time ranges (xx.xx to xx.xx) in <timestep> </timestep> tags.
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Then, provide your final answer within the <answer> </answer> tags.
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"""},
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],
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}
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]
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#In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time.
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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, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
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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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**video_kwargs,
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)
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inputs = inputs.to("cuda")
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# Inference
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generated_ids = model.generate(**inputs, max_new_tokens=512)
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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
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```bibtex
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@article{li2025videochatr1,
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title={VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning},
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author={Li, Xinhao and Yan, Ziang and Meng, Desen and Dong, Lu and Zeng, Xiangyu and He, Yinan and Wang, Yali and Qiao, Yu and Wang, Yi and Wang, Limin},
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journal={arXiv preprint arXiv:2504.06958},
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year={2025}
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}
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```
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