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TencentARC
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Model Details
Model Provider
TencentARC
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
TimeLens-8B is an MLLM with state-of-the-art video temporal grounding performance among open-source models, finetuned from Qwen3-VL-8B-Instruct. It is trained with carefully crafted RLVR (reinforcement learning with verifiable rewards) recipe proposed in our paper, utilizing our high-quality VTG training dataset TimeLens-100K.
TimeLens-8B achieves state-of-the-art video temporal grounding performance among open-source models:
For detailed comparison with other models, please refer to the 🏆 Leaderboard.
Install the following packages:
bash
pip install transformers==4.57.1 accelerate==1.6.0 torch==2.6.0 torchvision==0.21.0pip install qwen-vl-utils[decord]==0.0.14# use Flash-Attention 2 to speed up generationpip install flash-attn==2.7.4.post1 --no-build-isolation --no-cache-dirUsing 🤗Transformers for Inference:
python
import requestsimport osimport torchfrom transformers import AutoModelForImageTextToText, AutoProcessorfrom qwen_vl_utils import process_vision_info def download_video(url): save_path = os.path.basename(url) if not os.path.exists(save_path): print(f"Downloading video from {url}...") response = requests.get(url, stream=True) response.raise_for_status() with open(save_path, 'wb') as f: for chunk in response.iter_content(chunk_size=8192): f.write(chunk) return save_path # Load model and processormodel = AutoModelForImageTextToText.from_pretrained( "TencentARC/TimeLens-8B", dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto",) processor = AutoProcessor.from_pretrained( "TencentARC/TimeLens-8B", padding_side="left", do_resize=False,) # Prepare inputquery = "A man drinks water with a glass"video_path = download_video("https://huggingface.co/datasets/JungleGym/TimeLens-Assets/resolve/main/2Y8XQ.mp4") GROUNDER_PROMPT = "Please find the visual event described by the sentence '{}', determining its starting and ending times. The format should be: 'The event happens in <start time> - <end time> seconds'." messages = [{ 'role': 'user', 'content': [ { 'type': 'video', 'video': video_path, 'min_pixels': 64 * 32 * 32, 'total_pixels': 14336 * 32 * 32, 'fps': 2, }, { 'type': 'text', 'text': GROUNDER_PROMPT.format(query) } ]}] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)images, videos, video_kwargs = process_vision_info( messages, image_patch_size=16, return_video_kwargs=True, return_video_metadata=True,) videos, video_metadatas = zip(*videos)videos, video_metadatas = list(videos), list(video_metadatas) inputs = processor( text=[text], images=images, videos=videos, video_metadata=video_metadatas, padding=True, return_tensors='pt', **video_kwargs,).to("cuda") output_ids = model.generate( **inputs, do_sample=False, max_new_tokens=512,) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, output_ids)]answer = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]print(f"Answer: {answer}")If you find our work helpful for your research and applications, please cite our paper:
bibtex
@article{zhang2025timelens, title={TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs}, author={Zhang, Jun and Wang, Teng and Ge, Yuying and Ge, Yixiao and Li, Xinhao and Shan, Ying and Wang, Limin}, journal={arXiv preprint arXiv:2512.14698}, year={2025}}pip install transformers==4.57.1 accelerate==1.6.0 torch==2.6.0 torchvision==0.21.0pip install qwen-vl-utils[decord]==0.0.14# use Flash-Attention 2 to speed up generationpip install flash-attn==2.7.4.post1 --no-build-isolation --no-cache-dirimport requestsimport osimport torchfrom transformers import AutoModelForImageTextToText, AutoProcessorfrom qwen_vl_utils import process_vision_info def download_video(url): save_path = os.path.basename(url) if not os.path.exists(save_path): print(f"Downloading video from {url}...") response = requests.get(url, stream=True) response.raise_for_status() with open(save_path, 'wb') as f: for chunk in response.iter_content(chunk_size=8192): f.write(chunk) return save_path # Load model and processormodel = AutoModelForImageTextToText.from_pretrained( "TencentARC/TimeLens-8B", dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto",) processor = AutoProcessor.from_pretrained( "TencentARC/TimeLens-8B", padding_side="left", do_resize=False,) # Prepare inputquery = "A man drinks water with a glass"video_path = download_video("https://huggingface.co/datasets/JungleGym/TimeLens-Assets/resolve/main/2Y8XQ.mp4") GROUNDER_PROMPT = "Please find the visual event described by the sentence '{}', determining its starting and ending times. The format should be: 'The event happens in <start time> - <end time> seconds'." messages = [{ 'role': 'user', 'content': [ { 'type': 'video', 'video': video_path, 'min_pixels': 64 * 32 * 32, 'total_pixels': 14336 * 32 * 32, 'fps': 2, }, { 'type': 'text', 'text': GROUNDER_PROMPT.format(query) } ]}] text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)images, videos, video_kwargs = process_vision_info( messages, image_patch_size=16, return_video_kwargs=True, return_video_metadata=True,) videos, video_metadatas = zip(*videos)videos, video_metadatas = list(videos), list(video_metadatas) inputs = processor( text=[text], images=images, videos=videos, video_metadata=video_metadatas, padding=True, return_tensors='pt', **video_kwargs,).to("cuda") output_ids = model.generate( **inputs, do_sample=False, max_new_tokens=512,) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, output_ids)]answer = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]print(f"Answer: {answer}")@article{zhang2025timelens, title={TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs}, author={Zhang, Jun and Wang, Teng and Ge, Yuying and Ge, Yixiao and Li, Xinhao and Shan, Ying and Wang, Limin}, journal={arXiv preprint arXiv:2512.14698}, year={2025}}