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: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment

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/.

Quick Start

Load the model with a standard Transformers vision-language workflow. Provide one image and ask for an overall perceptual quality score in the 1 to 5 range. For easiest downstream parsing, request the final numeric score inside <answer>...</answer>.

Recommended prompt:

Assess the overall perceptual quality of this image. Briefly explain your assessment, then output a final quality score as a float between 1 and 5, rounded to two decimal places, inside <answer>...</answer>.

Output Format

A typical response contains a short assessment followed by a final score:

The image has generally clear structure and acceptable perceptual quality, with some visible degradation in fine detail.
<answer>3.74</answer>

Citation

Citation information will be added with the accompanying paper release.

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Safetensors

Model size

2B params

Tensor type

BF16