--- license: mit datasets: - Kwai-Keye/Thyme-SFT - Kwai-Keye/Thyme-RL language: - en metrics: - accuracy base_model: - Qwen/Qwen2.5-VL-7B-Instruct pipeline_tag: image-text-to-text ---
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[[📖 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)]
## 🔥 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.
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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 @article{zhang2025thyme, title={Thyme: Think Beyond Images}, author={Kwai Keye}, journal={arXiv preprint}, year={2025} } ```