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MCG-NJU
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Model Details
Model Provider
MCG-NJU
Model Tree
Qwen/Qwen3-VL-2B-Instruct
Input Modalities
Output Modalities
Supported Functionality
SFT uses examples from TimeLens2-93K, TimeLens-100K, and the official Ego4D-NLQ-v2 training split. Training retains full-video context and supervises variable-cardinality temporal interval sets. The model sees diverse grounding instructions and timestamp response formats.
The 2B SFT recipe trains for one epoch with packed sequences up to 160K tokens, a global batch size of 128, and a learning rate of 1e-5. The visual encoder is frozen during training.
This is an SFT checkpoint. It is not the final GRPO-optimized release reported in the TimeLens2 paper.
This checkpoint is hosted in a Hugging Face dataset repository. Download it locally, then load the directory with Transformers:
python
from huggingface_hub import snapshot_downloadfrom transformers import AutoModelForImageTextToText, AutoProcessormodel_dir = snapshot_download(repo_id="MCG-NJU/TimeLens2-2B-SFT",repo_type="dataset",)model = AutoModelForImageTextToText.from_pretrained(model_dir,torch_dtype="auto",device_map="auto",)processor = AutoProcessor.from_pretrained(model_dir)
See the official repository for the SFT and GRPO training recipes, data annotations, rollout configuration, and evaluation code.
bibtex
@misc{zhu2026timelens2,title={TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs},author={Yuhan Zhu and Changlian Ma and Xiangyu Zeng and Xinhao Li and Zhiqiu Zhang and Songze Li and Jun Zhang and Tianxiang Jiang and Yuandong Yang and Ziang Yan and Zikang Wang and Xinyu Chen and Haoran Chen and Shaowei Zhang and Limin Wang},year={2026},eprint={2607.17423},archivePrefix={arXiv},primaryClass={cs.CV},url={https://arxiv.org/abs/2607.17423},}