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OpenGVLab
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Model Details
Model Provider
OpenGVLab
Model Tree
Qwen/Qwen2-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
We provide a simple installation example below:
markdown
pip install transformerspip install qwen_vl_utils
Then you could use our model:
python
from transformers import Qwen2_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_infomodel_path = "OpenGVLab/VideoChat-R1_7B_caption"# default: Load the model on the available device(s)model = Qwen2_VLForConditionalGeneration.from_pretrained(model_path, torch_dtype="auto", device_map="auto",attn_implementation="flash_attention_2")# default processerprocessor = AutoProcessor.from_pretrained(model_path)video_path = "your_video.mp4"question = "Describe the video in detail."messages = [{"role": "user","content": [{"type": "video","video": video_path,"max_pixels": 360 * 420,"fps": 1.0,},{"type": "text", "text": f""""{question} First output the thinking process in <think> </think> tags and then output the final answer in <answer> </answer> tags"""},],}]#In Qwen 2 VL, frame rate information is also input into the model to align with absolute time.# Preparation for inferencetext = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)inputs = processor(text=[text],images=image_inputs,videos=video_inputs,padding=True,return_tensors="pt",**video_kwargs,)inputs = inputs.to("cuda")# Inferencegenerated_ids = model.generate(**inputs, max_new_tokens=512)generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text)
bibtex
@article{li2025videochatr1,title={VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning},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},journal={arXiv preprint arXiv:2504.06958},year={2025}}