---
title: Thyme-SFT
canonical_url: "https://www.modelscope.ai/models/Kwai-Keye/Thyme-SFT"
md_url: "https://www.modelscope.ai/models/Kwai-Keye/Thyme-SFT.md"
repository: Kwai-Keye/Thyme-SFT
last_updated: 2026-05-29
license: mit
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - qwen2_5_vl
architectures:
  - Qwen2_5_VLForConditionalGeneration
base_model:
  - Qwen/Qwen2.5-VL-7B-Instruct
base_model_relation: finetune
parameters: 8.3B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 165
stars: 0
---

# Thyme-SFT

> Thyme-SFT - An open-source model by Kwai-Keye on ModelScope. [📖 Home Page] [📖 Github Repo] [📖 Technique Report]

Kwai-Keye/Thyme-SFT is a 8.3B-parameter image-text-to-text model on ModelScope. licensed under mit. derived from Qwen/Qwen2.5-VL-7B-Instruct.

- **Repository**: Kwai-Keye/Thyme-SFT
- **License**: mit
- **Tasks**: image-text-to-text
- **Parameters**: 8.3B
- **Base model**: Qwen/Qwen2.5-VL-7B-Instruct
- **Downloads**: 165
- **Stars**: 0
- **Last updated**: 2026-05-29

Source: https://www.modelscope.ai/models/Kwai-Keye/Thyme-SFT

---

<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/685ba798484e3233f5ff6f11/dxBp6TmwqwNuBuJR9gfQC.png" width="40%" alt="Thyme Logo">
</div>

<font size=4><div align='center' >  
[[📖 Home Page](https://thyme-vl.github.io/)] 
[[📖 Github Repo](https://github.com/yfzhang114/Thyme)] 
[[📖 Technique Report](https://arxiv.org/abs/2508.11630)] 

[[📊 Thyme SFT Model](https://huggingface.co/Kwai-Keye/Thyme-SFT)] 
[[📊 Thyme RL Model](https://huggingface.co/Kwai-Keye/Thyme-RL)] 
[[📝 SFT Data](https://huggingface.co/datasets/Kwai-Keye/Thyme-SFT)] 
[[📝 RL Data](https://huggingface.co/datasets/Kwai-Keye/Thyme-RL)] 
</div></font>

## 🔥 News
* **`2025.08.15`** 🌟 We are excited to introduce **Thyme: Think Beyond Images**. Thyme transcends traditional ``thinking with images'' paradigms by autonomously generating and executing diverse image processing and computational operations through executable code, significantly enhancing performance on high-resolution perception and complex reasoning tasks. Leveraging a novel two-stage training strategy that combines supervised fine-tuning with reinforcement learning and empowered by the innovative GRPO-ATS algorithm, Thyme achieves a sophisticated balance between reasoning exploration and code execution precision.

<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/685ba798484e3233f5ff6f11/c_D7uX3RT1WUANDRB70ZC.png" width="100%" alt="Thyme Logo">
</div>

We have provided the usage instructions, training code, and evaluation code in the [GitHub repo](https://github.com/yfzhang114/Thyme).

## Citation

If you find Thyme useful in your research or applications, please cite our paper:

```bibtex
@misc{zhang2025thymethinkimages,
      title={Thyme: Think Beyond Images}, 
      author={Yi-Fan Zhang and Xingyu Lu and Shukang Yin and Chaoyou Fu and Wei Chen and Xiao Hu and Bin Wen and Kaiyu Jiang and Changyi Liu and Tianke Zhang and Haonan Fan and Kaibing Chen and Jiankang Chen and Haojie Ding and Kaiyu Tang and Zhang Zhang and Liang Wang and Fan Yang and Tingting Gao and Guorui Zhou},
      year={2025},
      eprint={2508.11630},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.11630}, 
}
```