Instructions to use prithivMLmods/VisionGuardrail-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/VisionGuardrail-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/VisionGuardrail-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/VisionGuardrail-9B")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/VisionGuardrail-9B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/VisionGuardrail-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/VisionGuardrail-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/VisionGuardrail-9B",
		"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/prithivMLmods/VisionGuardrail-9B

SGLang

How to use prithivMLmods/VisionGuardrail-9B 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 "prithivMLmods/VisionGuardrail-9B" \
    --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": "prithivMLmods/VisionGuardrail-9B",
		"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 "prithivMLmods/VisionGuardrail-9B" \
        --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": "prithivMLmods/VisionGuardrail-9B",
		"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 prithivMLmods/VisionGuardrail-9B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/VisionGuardrail-9B
```

prithivMLmods commited on Sep 5

Commit

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@@ -25,16 +25,13 @@ datasets:
 ![9B](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KOvD2MxMrdFmbEL0wrKMW.png)
-<div style="background-color: #FF4500; border-radius: 0; padding: 12px 16px; max-width: 600px; margin: 0 auto 20px; font-family: 'Courier New', Courier, monospace;">
-  <p style="margin: 0; color: #FFFFFF; font-size: 14px;">
-    hf.co/blog — <a href="https://huggingface.co/blog/prithivMLmods/vision-guardrail-mini-blog" style="color: #FFFFFF; text-decoration: underline;">vision-guardrail-mini-blog</a>
-  </p>
-</div>
 # **VisionGuardrail-9B**
 > **VisionGuardrail-9B** is a multimodal content-safety classifier based on **Qwen/Qwen3.5-9B** and trained on the **[ImageShield-Guardrail-Pro](https://huggingface.co/datasets/prithivMLmods/ImageShield-Guardrail-Pro)** content-safety guardrail dataset. The model is designed to classify visual content as **Safe** or **Unsafe**, with a particular focus on detecting **Not Safe for Work (NSFW) sensual content** and other potentially sensitive visual content. VisionGuardrail-9B excels at detecting **explicit and sensitive visual content**, with detailed analysis of **dress codes, clothing exposure, poses, framing, and different visual settings** to support conservative content-safety classification.
 > [!NOTE]
 > **This model is an experimental release and may generate unexpected behaviors or reasoning artifacts in certain scenarios.**

 ![9B](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KOvD2MxMrdFmbEL0wrKMW.png)
 # **VisionGuardrail-9B**
 > **VisionGuardrail-9B** is a multimodal content-safety classifier based on **Qwen/Qwen3.5-9B** and trained on the **[ImageShield-Guardrail-Pro](https://huggingface.co/datasets/prithivMLmods/ImageShield-Guardrail-Pro)** content-safety guardrail dataset. The model is designed to classify visual content as **Safe** or **Unsafe**, with a particular focus on detecting **Not Safe for Work (NSFW) sensual content** and other potentially sensitive visual content. VisionGuardrail-9B excels at detecting **explicit and sensitive visual content**, with detailed analysis of **dress codes, clothing exposure, poses, framing, and different visual settings** to support conservative content-safety classification.
+> [!TIP]
+> hf.co/blog — [vision-guardrail-mini-blog](https://huggingface.co/blog/prithivMLmods/vision-guardrail-mini-blog)
 > [!NOTE]
 > **This model is an experimental release and may generate unexpected behaviors or reasoning artifacts in certain scenarios.**