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OpenGVLab
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Model Details
Model Provider
OpenGVLab
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
We provide a simple installation example below:
text
pip install transformersUsing qwen_vl_utils in https://github.com/OpenGVLab/VideoChat-R1/blob/main/Videochat-R1.5/src_eval/my_vision_process.py
Then you could use our model:
python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info model_path = "OpenGVLab/VideoChat-R1_5"# default: Load the model on the available device(s)model = Qwen2_5_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 = "your_qa.mp4"num_percptions = 3 QA_THINK_GLUE = """Answer the question: "[QUESTION]" according to the content of the video. Output your think process within the <think> </think> tags. Then, provide your answer within the <answer> </answer> tags, output the corresponding letter of the option. At the same time, in the <glue> </glue> tags, present the precise time period in seconds of the video clips on which you base your answer to this question in the format of [(s1, e1), (s2, e2), ...]. For example: <think>...</think><answer>A</answer><glue>[(5.2, 10.4)]</glue>.""" QA_THINK = """Answer the question: "[QUESTION]" according to the content of the video. Output your think process within the <think> </think> tags. Then, provide your answer within the <answer> </answer> tags, output the corresponding letter of the option. For example: <think>...</think><answer>A</answer><glue>[(5.2, 10.4)]</glue>.""" def inference(video_path, prompt, model, processor, max_new_tokens=2048, device="cuda:0", client = None, pred_glue=None): messages = [ {"role": "user", "content": [ {"type": "video", "video": video_path, 'key_time':pred_glue, "total_pixels": 128*12 * 28 * 28, "min_pixels": 128 * 28 * 28, }, {"type": "text", "text": prompt}, ] }, ] text = 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, client = client) fps_inputs = video_kwargs['fps'] inputs = processor(text=[text], images=image_inputs, videos=video_inputs, fps=fps_inputs, padding=True, return_tensors="pt") inputs = inputs.to(device) with torch.no_grad(): output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, use_cache=True) generated_ids = [output_ids[i][len(inputs.input_ids[i]):] for i in range(len(output_ids))] output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) return output_text[0] for percption in range(num_percptions): if percption == num_percptions - 1: example_prompt = QA_THINK.replace("[QUESTION]", item["problem"]["question"]) else: example_prompt = QA_THINK_GLUE.replace("[QUESTION]", item["problem"]["question"]) ans = inference(video_path, example_prompt, model, processor, device=device, client=client, pred_glue=pred_glue) pattern_glue = r'<glue>(.*?)</glue>' match_glue = re.search(pattern_glue, ans, re.DOTALL) # print(f'ann:{ans}') answers.append(ans) pred_glue = None try: if match_glue: glue = match_glue.group(1) pred_glue = ast.literal_eval(glue) except Exception as e: pred_glue = Noneprint(ans)If you find this project useful in your research, please consider cite:
BibTeX
@article{li2025videochatr1, title={VideoChat-R1: Enhancing Spatio-TemporalPerception 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}} @article{yan2025videochatr15, title={VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception}, author={Yan, Ziang and Li, Xinhao and He, Yinan and Zhengrong Yue and Zeng, Xiangyu and Wang, Yali and Qiao, Yu and Wang, Limin and Wang, Yi}, journal={arXiv preprint arXiv:2509.21100}, year={2025}}pip install transformersfrom transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info model_path = "OpenGVLab/VideoChat-R1_5"# default: Load the model on the available device(s)model = Qwen2_5_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 = "your_qa.mp4"num_percptions = 3 QA_THINK_GLUE = """Answer the question: "[QUESTION]" according to the content of the video. Output your think process within the <think> </think> tags. Then, provide your answer within the <answer> </answer> tags, output the corresponding letter of the option. At the same time, in the <glue> </glue> tags, present the precise time period in seconds of the video clips on which you base your answer to this question in the format of [(s1, e1), (s2, e2), ...]. For example: <think>...</think><answer>A</answer><glue>[(5.2, 10.4)]</glue>.""" QA_THINK = """Answer the question: "[QUESTION]" according to the content of the video. Output your think process within the <think> </think> tags. Then, provide your answer within the <answer> </answer> tags, output the corresponding letter of the option. For example: <think>...</think><answer>A</answer><glue>[(5.2, 10.4)]</glue>.""" def inference(video_path, prompt, model, processor, max_new_tokens=2048, device="cuda:0", client = None, pred_glue=None): messages = [ {"role": "user", "content": [ {"type": "video", "video": video_path, 'key_time':pred_glue, "total_pixels": 128*12 * 28 * 28, "min_pixels": 128 * 28 * 28, }, {"type": "text", "text": prompt}, ] }, ] text = 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, client = client) fps_inputs = video_kwargs['fps'] inputs = processor(text=[text], images=image_inputs, videos=video_inputs, fps=fps_inputs, padding=True, return_tensors="pt") inputs = inputs.to(device) with torch.no_grad(): output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, use_cache=True) generated_ids = [output_ids[i][len(inputs.input_ids[i]):] for i in range(len(output_ids))] output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) return output_text[0] for percption in range(num_percptions): if percption == num_percptions - 1: example_prompt = QA_THINK.replace("[QUESTION]", item["problem"]["question"]) else: example_prompt = QA_THINK_GLUE.replace("[QUESTION]", item["problem"]["question"]) ans = inference(video_path, example_prompt, model, processor, device=device, client=client, pred_glue=pred_glue) pattern_glue = r'<glue>(.*?)</glue>' match_glue = re.search(pattern_glue, ans, re.DOTALL) # print(f'ann:{ans}') answers.append(ans) pred_glue = None try: if match_glue: glue = match_glue.group(1) pred_glue = ast.literal_eval(glue) except Exception as e: pred_glue = Noneprint(ans)@article{li2025videochatr1, title={VideoChat-R1: Enhancing Spatio-TemporalPerception 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}} @article{yan2025videochatr15, title={VideoChat-R1.5: Visual Test-Time Scaling to Reinforce Multimodal Reasoning by Iterative Perception}, author={Yan, Ziang and Li, Xinhao and He, Yinan and Zhengrong Yue and Zeng, Xiangyu and Wang, Yali and Qiao, Yu and Wang, Limin and Wang, Yi}, journal={arXiv preprint arXiv:2509.21100}, year={2025}}