---
license: apache-2.0
library_name: transformers
pipeline_tag: video-text-to-text
base_model: Qwen/Qwen3-VL-8B-Instruct
tags:
  - video
  - temporal-grounding
  - qwen3-vl
---

# TimeLens2-8B-SFT

This repository contains the **supervised fine-tuning (SFT) checkpoint** for
TimeLens2-8B, 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-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-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
[TimeLens2-8B](https://huggingface.co/MCG-NJU/TimeLens2-8B).

## Training data and recipe

SFT uses examples from
[TimeLens2-93K](https://huggingface.co/datasets/MCG-NJU/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 8B 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/TimeLens2-8B-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](https://github.com/MCG-NJU/TimeLens2) 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},
}
```