Instructions to use Ethanliang99/Zoom-IQA-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ethanliang99/Zoom-IQA-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Ethanliang99/Zoom-IQA-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Ethanliang99/Zoom-IQA-7B")
model = AutoModelForMultimodalLM.from_pretrained("Ethanliang99/Zoom-IQA-7B", 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 Ethanliang99/Zoom-IQA-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ethanliang99/Zoom-IQA-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": "Ethanliang99/Zoom-IQA-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/Ethanliang99/Zoom-IQA-7B

SGLang

How to use Ethanliang99/Zoom-IQA-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 "Ethanliang99/Zoom-IQA-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": "Ethanliang99/Zoom-IQA-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 "Ethanliang99/Zoom-IQA-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": "Ethanliang99/Zoom-IQA-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 Ethanliang99/Zoom-IQA-7B with Docker Model Runner:
```
docker model run hf.co/Ethanliang99/Zoom-IQA-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Zoom-IQA-7B

Official model for Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning.

Paper · Project page · Base model

Zoom-IQA-7B performs no-reference image quality assessment through two-round, region-aware reasoning. This specific version is our enhanced open-source release, trained on 8× NVIDIA H200 GPUs to achieve stronger generalization capabilities. This repository contains the inference weights; training data are released separately in the companion GR-IQA repository.

Quick start

Use the companion Zoom-IQA evaluator, which implements the prompts, crop routing, parsing, and evaluation metrics used by the model. In its Transformers environment, run:

zoomiqa-score \
  --model-path /path/to/Zoom-IQA-7B \
  --image /path/to/your_image.jpg \
  --device 0

For benchmark evaluation, use zoomiqa-eval; a separate zoomiqa-eval-vllm entrypoint is available for faster inference. See the code repository for installation, annotation format, Python usage, and complete command-line options.

The bundled generation_config.json is checkpoint metadata. The official evaluator supplies its protocol settings explicitly.

Limitations

Outputs are sampled and may occasionally be malformed. Results can change with different prompts, decoding settings, preprocessing, or runtime versions. The model is intended for image-quality research, not high-stakes decisions.

License

Released under the Apache License 2.0. Benchmark images and GR-IQA source images retain their original licenses.

Citation

@article{liang2026zoomiqa,
  title={Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning},
  author={Liang, Guoqiang and Wang, Jianyi and Wu, Zhonghua and Zhou, Shangchen and Loy, Chen Change},
  journal={arXiv preprint arXiv:2601.02918},
  year={2026}
}