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)

# 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=40)
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 14 days ago

Commit

dc1167a

verified ·

1 Parent(s): 0546ff4

Update README.md

Browse files

Files changed (1) hide show

README.md +20 -12

README.md CHANGED Viewed

@@ -93,10 +93,10 @@ pip install accelerate
 ```
 ```python
-from transformers import AutoProcessor, AutoModelForMultimodalLM
 import torch
-model = AutoModelForMultimodalLM.from_pretrained(
     "prithivMLmods/VisionGuardrail-9B",
     torch_dtype="auto",
     device_map="auto"
@@ -118,25 +118,33 @@ messages = [
     }
 ]
-inputs = processor.apply_chat_template(
     messages,
-    add_generation_prompt=True,
-    tokenize=True,
-    return_dict=True,
-    return_tensors="pt",
-).to(model.device)
 generated_ids = model.generate(
     **inputs,
     max_new_tokens=256
 )
-output_text = processor.decode(
-    generated_ids[0][inputs["input_ids"].shape[-1]:],
-    skip_special_tokens=True
 )
-print(output_text)
 ```
 ## **Training Details**

 ```
 ```python
+from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
 import torch
+model = Qwen3_5ForConditionalGeneration.from_pretrained(
     "prithivMLmods/VisionGuardrail-9B",
     torch_dtype="auto",
     device_map="auto"
     }
 ]
+text = processor.apply_chat_template(
     messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+inputs = processor(
+    text=[text],
+    padding=True,
+    return_tensors="pt"
+).to("cuda")
 generated_ids = model.generate(
     **inputs,
     max_new_tokens=256
 )
+output_text = processor.batch_decode(
+    [
+        out[len(inp):]
+        for inp, out in zip(inputs.input_ids, generated_ids)
+    ],
+    skip_special_tokens=True,
+    clean_up_tokenization_spaces=False
 )
+print(output_text[0])
 ```
 ## **Training Details**