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", 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 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

RobinY99

Upload MR-IQA 2B final model

e8526dd about 1 month ago

1.47 kB

license: mit
library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3-VL-2B-Instruct
tags:
  - image-quality-assessment
  - iqa
  - qwen3-vl
  - mr-iqa

MR-IQA

MR-IQA is a 2B vision-language model for no-reference image quality assessment. It predicts a perceptual quality score from an input image and a short instruction prompt.

This repository contains the final checkpoint from the original June 17, 2026 MR-IQA 2B training run. The training data used KONIQ-style image quality supervision, and the model was validated on a 200-image KONIQ validation split with fixed data sampling.

Validation

Final checkpoint validation on the 200-image KONIQ validation split:

Split	Valid samples	SRCC	PLCC	Shards
KONIQ validation	200	0.9250992954	0.9420850686	8

Files

The repository includes only inference-ready assets:

-

Training-only artifacts such as optimizer state, scheduler state, RNG state, trainer state, and distributed checkpoint helpers are intentionally excluded.

Usage

Use a standard Transformers vision-language loading flow with and the Qwen3-VL model class. Provide an image and ask the model to assess overall perceptual quality. The expected score range is 1 to 5, rounded to two decimal places.

Example prompt:

The model is released as MR-IQA. Paths used during training and evaluation are not required for loading or inference.