GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
OpenGVLab
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
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:
text
pip install transformerspip install qwen_vl_utilsThen you could use our model:
python
from transformers import Qwen2_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info model_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}}from transformers import Qwen2_VLForConditionalGeneration, AutoTokenizer, AutoProcessorfrom qwen_vl_utils import process_vision_info model_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)@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}}