| --- |
| base_model: |
| - Qwen/Qwen2.5-VL-7B-Instruct |
| datasets: |
| - yushaohan/ProGuard-data |
| language: |
| - en |
| tags: |
| - vlm |
| - safety |
| - guard |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| --- |
| |
| # ProGuard-7B |
|
|
| ProGuard is a proactive multimodal safeguard model introduced as part of the **DeepSight** toolkit. It is designed to identify and reason about unknown risks across both text and visual modalities, moving beyond rigid predefined classification systems. |
|
|
| - **ProGuard Paper:** [ProGuard: Towards Proactive Multimodal Safeguard](https://arxiv.org/abs/2512.23573) |
| - **DeepSight Paper:** [DeepSight: An All-in-One LM Safety Toolkit](https://huggingface.co/papers/2602.12092) |
| - **Project Page:** [DeepSight Homepage](https://ai45.shlab.org.cn/safety-entry) |
| - **GitHub Repository:** [DeepSafe (Safety Evaluation ToolKit)](https://github.com/AI45Lab/DeepSafe) |
|
|
| 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)**. |
|
|
| ## Citation |
|
|
| If you find this model helpful, please cite our research: |
|
|
| ```bibtex |
| @article{yu2025proguard, |
| title={ProGuard: Towards Proactive Multimodal Safeguard}, |
| author={Yu, Shaohan and Li, Lijun and Si, Chenyang and Sheng, Lu and Shao, Jing}, |
| journal={arXiv preprint arXiv:2512.23573}, |
| year={2025}, |
| url={https://yushaohan.github.io/ProGuard/} |
| } |
| |
| @article{zhang2026deepsight, |
| title={DeepSight: An All-in-One LM Safety Toolkit}, |
| 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}, |
| journal={arXiv preprint arXiv:2602.12092}, |
| year={2026} |
| } |
| ``` |
