Instructions to use CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1")
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("CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1")
model = AutoModelForMultimodalLM.from_pretrained("CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1", 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 CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1",
		"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/CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1

SGLang

How to use CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1 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 "CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1" \
    --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": "CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1",
		"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 "CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1" \
        --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": "CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1",
		"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 CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1 with Docker Model Runner:
```
docker model run hf.co/CodeGoat24/UniGenBench-EvalModel-qwen-72b-v1
```

UniGenBench-EvalModel-qwen-72b-v1 / README.md

CodeGoat24

Update README.md

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	---
	library_name: transformers
	license: mit
	datasets:
	- CodeGoat24/UniGenBench-Eval-Images
	base_model:
	- CodeGoat24/UnifiedReward-2.0-qwen-72b
	---


	# UniGenBench-EvalModel-qwen-72b-v1

	This model is tailored for offline T2I model evaluation on [UniGenBench](https://github.com/CodeGoat24/UniGenBench), which achieves an average accuracy of 94% compared to evaluations by Gemini 2.5 Pro.

	Feel free to use this model to assess and compare the performance of your models.

	![image](https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/654c6845bac6e6e49895a5b5/gsE1RuzJq2lAlPh8bLSWf.png)
	![image](https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/654c6845bac6e6e49895a5b5/k9ZzcWY6zGM0mUmml8lnS.png)


	For further details, please refer to the following resources:
	- 📰 Paper: https://arxiv.org/pdf/2508.20751
	- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/Pref-GRPO
	- 🤗 UniGenBench: https://github.com/CodeGoat24/UniGenBench
	- 🤗 Leaderboard: https://huggingface.co/spaces/CodeGoat24/UniGenBench_Leaderboard
	- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)


	## Citation

	```bibtex
	@article{UniGenBench++,
	title={UniGenBench++: A Unified Semantic Evaluation Benchmark for Text-to-Image Generation},
	author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Bu, Jiazi and Zhou, Yujie and Xin, Yi and He, Junjun and Wang, Chunyu and Lu, Qinglin and Jin, Cheng and others},
	journal={arXiv preprint arXiv:2510.18701},
	year={2025}
	}

	@article{UniGenBench,
	title={Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning},
	author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Zhou, Yujie and Bu, Jiazi and Wang, Chunyu and Lu, Qinglin, and Jin, Cheng and Wang, Jiaqi},
	journal={arXiv preprint arXiv:2508.20751},
	year={2025}
	}
	```