This repository contains the supervised fine-tuning (SFT) checkpoint for TimeLens2-4B, a video multimodal language model for temporal grounding. Given a video and a text query, the model is trained to return all time intervals that contain the requested visual evidence.

It is initialized from Qwen3-VL-4B-Instruct and fine-tuned with long-context temporal-grounding supervision. This checkpoint is the starting point for TimeLens2 rollout generation and GRPO training.

For the released TimeLens2 model, see TimeLens2-4B.

Training data and recipe

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 4B SFT recipe trains for one epoch with packed sequences up to 100K tokens, a global batch size of 256, and a learning rate of 5e-6. The visual encoder is frozen during training.

Intended use

Generate off-policy rollouts for the TimeLens2 GRPO stage.
Reproduce or extend the SFT-to-GRPO training pipeline.
Study long-context video temporal grounding before reinforcement learning.

This is an SFT checkpoint. It is not the final GRPO-optimized release reported in the TimeLens2 paper.

Loading the checkpoint

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_download
from transformers import AutoModelForImageTextToText, AutoProcessor

model_dir = snapshot_download(
    repo_id="MCG-NJU/TimeLens2-4B-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.

Citation

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},
}