Instructions to use yushaohan/ProGuard-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yushaohan/ProGuard-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="yushaohan/ProGuard-3B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("yushaohan/ProGuard-3B")
model = AutoModelForMultimodalLM.from_pretrained("yushaohan/ProGuard-3B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yushaohan/ProGuard-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yushaohan/ProGuard-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yushaohan/ProGuard-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/yushaohan/ProGuard-3B

SGLang

How to use yushaohan/ProGuard-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yushaohan/ProGuard-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yushaohan/ProGuard-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yushaohan/ProGuard-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yushaohan/ProGuard-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use yushaohan/ProGuard-3B with Docker Model Runner:
```
docker model run hf.co/yushaohan/ProGuard-3B
```

ProGuard-3B

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---
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
datasets:
- yushaohan/ProGuard-data
language:
- en
tags:
- vlm
- safety
- guard
library_name: transformers
pipeline_tag: image-text-to-text
---

# ProGuard-3B

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.

- **Arxiv Paper:** [ProGuard: Towards Proactive Multimodal Safeguard](https://arxiv.org/abs/2512.23573)
- **Project Page:** [ProGuard Homepage](https://yushaohan.github.io/ProGuard/)
- **GitHub Repository:** [ProGuard Implementation](https://github.com/yushaohan/ProGuard), [DeepSafe Implementation](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}
}
```