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--- |
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license: apache-2.0 |
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pipeline_tag: image-text-to-text |
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--- |
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# HermesFlow |
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Official Repository of the paper: *[HermesFlow](https://github.com/Gen-Verse/HermesFlow)*. |
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<p align="left"> |
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<a href='https://arxiv.org/abs/2502.12148'> |
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<img src='https://img.shields.io/badge/Arxiv-2502.12148-A42C25?style=flat&logo=arXiv&logoColor=A42C25'></a> |
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<a href='https://github.com/Gen-Verse/HermesFlow'> |
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<img src='https://img.shields.io/badge/GitHub-Code-black?style=flat&logo=github&logoColor=white'></a> |
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</p> |
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<img src="./pipeline.png" style="zoom:100%;" /> |
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<img src="./image.png" style="zoom:100%;" /> |
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<img src="./image-1.png" style="zoom:100%;" /> |
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## News🔥🔥🔥 |
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* Feb.18, 2025. Our checkpoints are publicly available on [HuggingFace Repo](https://huggingface.co/Gen-Verse/HermesFlow). |
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## Introduction |
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HermesFlow is a general alignment framework for multimodal LLMs, which cruate homologous preference data itself and utilize self-play iterative optimization with Pair-DPO to seamlessly close the gap between multimodal understanding and generation. |
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## Citation |
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``` |
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@article{yang2025hermesflow, |
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title={HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation}, |
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author={Yang, Ling and Zhang, Xinchen and Tian, Ye and Shang, Chenming and Xu, Minghao and Zhang, Wentao and Cui, Bin}, |
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journal={arXiv preprint arXiv:2502.12148}, |
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year={2025} |
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} |
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``` |
