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", 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?"}
        ]
    },
]
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
```

RobinY99 commited on 22 days ago

Commit

e8526dd

1 Parent(s): 7cc7be6

Upload MR-IQA 2B final model

Browse files

Files changed (2) hide show

README.md +25 -23
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -8,40 +8,42 @@ tags:
 - iqa
 - qwen3-vl
 - mr-iqa
-- grpo
 ---
-# MR-IQA 2B
-This repository contains the MR-IQA 2B no-thinking model from the original 2026-06-17 training run.
-## Model
-- Base model: Qwen3-VL-2B-Instruct
-- Training run: `visualquality_r1_non_thinking_2b_full_zero3_dmc_rawmargin_l2_noordinal_2b_20260617_1257`
-- Published checkpoint: `best_model`
-- Best validation epoch: 3
-- Validation set: KONIQ 200-sample validation split with dataset seed 42
-- Validation result: SRCC 0.9317891406, PLCC approximately 0.94
 ## Files
-This upload keeps only inference-required resources:
-- `model.safetensors`
-- `config.json`
-- `generation_config.json`
-- `processor_config.json`
-- `tokenizer.json`
-- `tokenizer_config.json`
-- `chat_template.jinja`
-Training-only artifacts such as optimizer states, scheduler states, RNG states, trainer state, DeepSpeed ZeRO folders, and local path metadata are intentionally excluded.
-## Prompt Format
-The training/evaluation prompt asks for an overall image quality score as a float between 1 and 5, rounded to two decimal places, with the final answer in `<answer>...</answer>` tags.
-## Path Hygiene
-The uploaded model files do not require local training paths. Provide image files through your own inference or evaluation pipeline.

 - 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.

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