Instructions to use RobinY99/MR-IQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RobinY99/MR-IQA with Transformers:

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

pipe = pipeline("image-text-to-text", model="RobinY99/MR-IQA")
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("RobinY99/MR-IQA")
model = AutoModelForMultimodalLM.from_pretrained("RobinY99/MR-IQA")
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 RobinY99/MR-IQA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RobinY99/MR-IQA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RobinY99/MR-IQA",
		"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/RobinY99/MR-IQA

SGLang

How to use RobinY99/MR-IQA 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 "RobinY99/MR-IQA" \
    --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": "RobinY99/MR-IQA",
		"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 "RobinY99/MR-IQA" \
        --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": "RobinY99/MR-IQA",
		"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 RobinY99/MR-IQA with Docker Model Runner:
```
docker model run hf.co/RobinY99/MR-IQA
```

MR-IQA / README.md

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	---
	license: mit
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- image-quality-assessment
	- iqa
	- mr-iqa
	---

	# MR-IQA: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment

	<p align="center">
	<a href="https://arxiv.org/abs/2606.29760">arXiv</a> \|
	<a href="https://github.com/RobinY99/MR-IQA">GitHub</a>
	</p>

	<p align="center">
	<a href="https://github.com/RobinY99/MR-IQA/blob/main/assets/mr_iqa_overview.pdf">
	<img src="https://raw.githubusercontent.com/RobinY99/MR-IQA/main/assets/mr_iqa_overview.png" alt="MR-IQA unified margin view and training pipeline" width="96%">
	</a>
	</p>

	We derive that regression and ranking are approximately equivalent under a unified margin view. Based on this observation, we propose MR-IQA for margin learning in blind image quality assessment.


	## Validation Snapshot

	The released checkpoint was validated after each epoch with an 8-shard setup on a held-out KONIQ split.

	\| Epoch \| Valid samples \| SRCC \| PLCC \| Shards \|
	\| ---: \| ---: \| ---: \| ---: \| ---: \|
	\| 1 \| 200 \| 0.8840 \| 0.8894 \| 8 \|
	\| 2 \| 200 \| 0.9213 \| 0.9302 \| 8 \|
	\| 3 \| 200 \| 0.9318 \| 0.9392 \| 8 \|
	\| 4 \| 200 \| 0.9274 \| 0.9340 \| 8 \|
	\| 5 \| 200 \| 0.9271 \| 0.9409 \| 8 \|
	\| 6 \| 200 \| 0.9249 \| 0.9406 \| 8 \|
	\| 7 \| 200 \| 0.9205 \| 0.9408 \| 8 \|
	\| 8 \| 200 \| 0.9288 \| 0.9465 \| 8 \|
	\| 9 \| 200 \| 0.9307 \| 0.9450 \| 8 \|
	\| 10 \| 200 \| 0.9251 \| 0.9421 \| 8 \|

	Best SRCC was reached at epoch 3. The final released checkpoint corresponds to epoch 10. Sanitized training metadata is available in [`training_guidance/`](training_guidance/).

	## Quick Start

	Load the model with a standard Transformers vision-language workflow. The training and evaluation code use a no-reasoning prompt and parse the final numeric score from `<answer>...</answer>`.

	System prompt:

	```text
	You are an image quality assessment assistant. Output only the final score in <answer> </answer> tags.
	```

	User prompt:

	```text
	What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality. Please only output the final answer with one score in <answer> </answer> tags.
	```

	## Output Format

	The expected response is only one score in answer tags:

	```text
	<answer>3.74</answer>
	```

	The evaluation parser first reads the number inside `<answer>...</answer>` and clamps valid scores to the 1 to 5 range.

	## Citation

	```bibtex
	@misc{li2026mriqaunifiedmarginview,
	title={MR-IQA: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment},
	author={Yuan Li and Youyuan Lin and Zitang Sun and Yung-Hao Yang and Kiyofumi Miyoshi and Chenhui Chu and Shin'ya Nishida},
	year={2026},
	eprint={2606.29760},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2606.29760}
	}
	```