Instructions to use PolicyShiftGuard/PolicyShiftGuard-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PolicyShiftGuard/PolicyShiftGuard-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="PolicyShiftGuard/PolicyShiftGuard-7B")
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("PolicyShiftGuard/PolicyShiftGuard-7B")
model = AutoModelForMultimodalLM.from_pretrained("PolicyShiftGuard/PolicyShiftGuard-7B", 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 PolicyShiftGuard/PolicyShiftGuard-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PolicyShiftGuard/PolicyShiftGuard-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PolicyShiftGuard/PolicyShiftGuard-7B",
		"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/PolicyShiftGuard/PolicyShiftGuard-7B

SGLang

How to use PolicyShiftGuard/PolicyShiftGuard-7B 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 "PolicyShiftGuard/PolicyShiftGuard-7B" \
    --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": "PolicyShiftGuard/PolicyShiftGuard-7B",
		"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 "PolicyShiftGuard/PolicyShiftGuard-7B" \
        --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": "PolicyShiftGuard/PolicyShiftGuard-7B",
		"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 PolicyShiftGuard/PolicyShiftGuard-7B with Docker Model Runner:
```
docker model run hf.co/PolicyShiftGuard/PolicyShiftGuard-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

PolicyShiftGuard-7B

📜 Paper | 💻 Code | 🏠 Project Page

PolicyShiftGuard-7B is a policy-conditioned image guardrail model based on Qwen2.5-VL-7B. It is trained to follow a supplied policy bundle and produce structured image-safety decisions under changing application policies.

Expected Output Format

true | <two-digit risk category id> | <short reason>
false | <short reason>

Training Data

This checkpoint is trained with the PolicyShiftBench public data release:

Dataset: PolicyShiftBench/PolicyShiftBench
Main evaluation splits: ID/adaptive branch and OOD/shift branch
Training stages: randomized policy SFT followed by boundary-pair policy adaptation

Intended Use

Use this model for research on policy-conditioned multimodal safety, adaptive image moderation, and robustness under policy shifts. The model should be evaluated with explicit policy bundles rather than as a fixed universal safety classifier.

Limitations

This is a research checkpoint. It may fail under policies, languages, visual domains, or deployment settings not represented in the benchmark. Outputs should not be treated as legal or compliance advice.

Citation

If you use this model, please cite the paper:

@article{song2026policyshiftguard,
  title   = {PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails},
  author  = {Song, Mingyang and Xu, Luxin and Sun, Haoyu and Pan, Minzhou and Cheng, Yu and Li, Bo},
  journal = {arXiv preprint arXiv:2607.05910},
  year    = {2026}
}