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---
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datasets:
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- yushaohan/ProGuard-data
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-VL-3B-Instruct
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tags:
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- vlm
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- safety
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- guard
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---
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@article{yu2025proguard,
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title={ProGuard: Towards Proactive Multimodal Safeguard},
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author={Yu, Shaohan and Li, Lijun and Si, Chenyang and Sheng, Lu and Shao, Jing},
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year={2025},
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url={https://yushaohan.github.io/ProGuard/}
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}
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```
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---
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base_model:
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- Qwen/Qwen2.5-VL-3B-Instruct
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datasets:
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- yushaohan/ProGuard-data
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language:
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- en
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tags:
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- vlm
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- safety
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- guard
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library_name: transformers
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pipeline_tag: image-text-to-text
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---
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# ProGuard-3B
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ProGuard is a proactive multimodal safeguard model. It is designed to identify and reason about unknown risks across both text and visual modalities, moving beyond rigid predefined classification systems.
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- **Arxiv Paper:** [ProGuard: Towards Proactive Multimodal Safeguard](https://arxiv.org/abs/2512.23573)
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- **Project Page:** [ProGuard Homepage](https://yushaohan.github.io/ProGuard/)
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- **GitHub Repository:** [ProGuard Implementation](https://github.com/yushaohan/ProGuard), [DeepSafe Implementation](https://github.com/AI45Lab/DeepSafe)
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This model is the official open-source implementation of **ProGuard**. For deployment instructions, please refer to **[this link](https://github.com/yushaohan/ProGuard/tree/master/deploy)**.
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## Citation
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If you find this model helpful, please cite our research:
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```bibtex
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@article{yu2025proguard,
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title={ProGuard: Towards Proactive Multimodal Safeguard},
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author={Yu, Shaohan and Li, Lijun and Si, Chenyang and Sheng, Lu and Shao, Jing},
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year={2025},
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url={https://yushaohan.github.io/ProGuard/}
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}
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@article{zhang2026deepsight,
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title={DeepSight: An All-in-One LM Safety Toolkit},
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author={Zhang, Bo and Guo, Jiaxuan and Li, Lijun and Liu, Dongrui and Chen, Sujin and Chen, Guanxu and Zheng, Zhijie and Lin, Qihao and Yan, Lewen and Qian, Chen and others},
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journal={arXiv preprint arXiv:2602.12092},
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year={2026}
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}
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```
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