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

Libraries

How to use X-GenGroup/PaCo-Reward-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="X-GenGroup/PaCo-Reward-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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("X-GenGroup/PaCo-Reward-7B")
model = AutoModelForImageTextToText.from_pretrained("X-GenGroup/PaCo-Reward-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?"}
        ]
    },
]
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

vLLM

How to use X-GenGroup/PaCo-Reward-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "X-GenGroup/PaCo-Reward-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": "X-GenGroup/PaCo-Reward-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/X-GenGroup/PaCo-Reward-7B

SGLang

How to use X-GenGroup/PaCo-Reward-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 "X-GenGroup/PaCo-Reward-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": "X-GenGroup/PaCo-Reward-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 "X-GenGroup/PaCo-Reward-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": "X-GenGroup/PaCo-Reward-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 X-GenGroup/PaCo-Reward-7B with Docker Model Runner:
```
docker model run hf.co/X-GenGroup/PaCo-Reward-7B
```

Add model card for PaCo-Reward-7B with metadata, paper, project, and code links

by nielsr HF Staff - opened Dec 5, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+51

-0

nielsr

Dec 5, 2025

This PR adds a comprehensive model card for the PaCo-Reward-7B model, a component of the PaCo-RL framework.

It includes:

Relevant metadata: pipeline_tag (image-text-to-text) and library_name (transformers), derived from model configuration and functionality.
Links to the research paper (PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling), the project page (https://x-gengroup.github.io/HomePage_PaCo-RL/), and the GitHub repository (https://github.com/X-GenGroup/PaCo-RL).
A concise description of the model based on the paper abstract.
The 'Overview' and 'Model Zoo' sections from the GitHub README to provide better context and discoverability of related models.
The BibTeX citation for the paper.

The license and a code snippet for sample usage are omitted as no explicit evidence was found in the provided documentation, adhering to the safety guidelines.

Please review and merge if these improvements are satisfactory.

Add model card for PaCo-Reward-7B with metadata, paper, project, and code links4daa2169

Jayce-Ping changed pull request status to merged Dec 6, 2025

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