Instructions to use andyc03/Qwen2-VL-PRISM-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andyc03/Qwen2-VL-PRISM-DPO with Transformers:

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

pipe = pipeline("image-text-to-text", model="andyc03/Qwen2-VL-PRISM-DPO")
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("andyc03/Qwen2-VL-PRISM-DPO")
model = AutoModelForMultimodalLM.from_pretrained("andyc03/Qwen2-VL-PRISM-DPO", 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 andyc03/Qwen2-VL-PRISM-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andyc03/Qwen2-VL-PRISM-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andyc03/Qwen2-VL-PRISM-DPO",
		"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/andyc03/Qwen2-VL-PRISM-DPO

SGLang

How to use andyc03/Qwen2-VL-PRISM-DPO 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 "andyc03/Qwen2-VL-PRISM-DPO" \
    --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": "andyc03/Qwen2-VL-PRISM-DPO",
		"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 "andyc03/Qwen2-VL-PRISM-DPO" \
        --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": "andyc03/Qwen2-VL-PRISM-DPO",
		"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 andyc03/Qwen2-VL-PRISM-DPO with Docker Model Runner:
```
docker model run hf.co/andyc03/Qwen2-VL-PRISM-DPO
```

Add comprehensive model card for PRISM (Qwen2-VL)

by nielsr HF Staff - opened Aug 31, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+33

-0

nielsr

Aug 31, 2025

This PR adds a comprehensive model card for the PRISM model, ensuring it is properly documented on the Hugging Face Hub.

Specifically, this PR:

Links the model to its paper: PRISM: Robust VLM Alignment with Principled Reasoning for Integrated Safety in Multimodality.
Adds the license (MIT), library_name (transformers), and pipeline_tag (image-text-to-text) to the metadata, making the model discoverable on the Hub via relevant filters and enabling the automated 'How to use' widget.
Includes the paper's abstract for a quick overview of the model's capabilities and purpose.
Provides a direct link to the GitHub repository for easy access to the code and further details.

Please review and merge this PR to enhance the visibility and information quality of the PRISM model on the Hugging Face Hub.

Add comprehensive model card for PRISM (Qwen2-VL)88e921cf

andyc03 changed pull request status to merged Sep 1, 2025

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