Instructions to use hollow404/VQR1-7B-YouTubeUGC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hollow404/VQR1-7B-YouTubeUGC with Transformers:

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

pipe = pipeline("image-text-to-text", model="hollow404/VQR1-7B-YouTubeUGC")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("hollow404/VQR1-7B-YouTubeUGC")
model = AutoModelForImageTextToText.from_pretrained("hollow404/VQR1-7B-YouTubeUGC")
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

vLLM

How to use hollow404/VQR1-7B-YouTubeUGC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hollow404/VQR1-7B-YouTubeUGC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hollow404/VQR1-7B-YouTubeUGC",
		"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/hollow404/VQR1-7B-YouTubeUGC

SGLang

How to use hollow404/VQR1-7B-YouTubeUGC 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 "hollow404/VQR1-7B-YouTubeUGC" \
    --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": "hollow404/VQR1-7B-YouTubeUGC",
		"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 "hollow404/VQR1-7B-YouTubeUGC" \
        --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": "hollow404/VQR1-7B-YouTubeUGC",
		"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 hollow404/VQR1-7B-YouTubeUGC with Docker Model Runner:
```
docker model run hf.co/hollow404/VQR1-7B-YouTubeUGC
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VQR1-7B-YouTubeUGC

VQR1-7B-YouTubeUGC is the base video quality assessment (VQA) model used in MDS-VQA: Model-Informed Data Selection for Video Quality Assessment. It instantiates the base quality model f(·) in the MDS-VQA pipeline and is trained on the YouTube-UGC source dataset to predict perceptual video quality scores.

MDS-VQA augments this base model with a separate failure predictor g(·) and a diversity-aware selection module. This repository contains the base VQA model checkpoint, not the MDS-VQA failure predictor or an actively fine-tuned target-domain model.

Paper: arXiv:2603.11525
Project/code: Multimedia-Analytics-Laboratory/MDS-VQA

Model Details

Model type: no-reference video quality assessment vision-language model
Backbone family: Qwen2.5-VL / VisualQuality-R1-style VLM
Architecture in this repository: Qwen2_5_VLForConditionalGeneration
Parameters: approximately 8.29B BF16 parameters
Training data: YouTube-UGC, used as the labeled source-domain dataset in MDS-VQA
Input: a video plus a VQA prompt
Output: a quality score on a 1 to 5 scale, typically inside <answer>...</answer> tags
License: Apache 2.0

Intended Use

This model is intended for research on no-reference video quality assessment and data selection for VQA. Typical uses include:

predicting a baseline quality score for user-generated or streaming videos;
serving as the base quality model f(·) in the MDS-VQA pipeline;
generating baseline predictions for failure-prediction training and model-informed data selection;
comparing active data selection or fine-tuning methods for VQA.

It is not intended as a universal production QoE monitor without domain-specific validation. Quality scores can shift across datasets, display conditions, content types, and distortion families.

Prompt Format

The model follows the VisualQuality-R1-style scoring prompt used in MDS-VQA:

You are doing the video quality assessment task.
Here is the question: What is your overall rating on the quality of this video? 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.
First output the thinking process in <think> </think> tags and then output the final answer with only one score in <answer> </answer> tags.

For automatic evaluation, parse the scalar value inside the final <answer> tag.

Example Usage

Please refer to the src/inference.py with the former prompt format.

MDS-VQA Context

MDS-VQA is a model-informed data selection mechanism for VQA. Given an unlabeled video pool, it selects videos that are both:

Difficult for the base VQA model: estimated by a failure predictor trained to rank videos by the base model's prediction errors.
Diverse in content: estimated from frame-level semantic video features, using a Chamfer-distance-based diversity term.

Citation

If you use this model, please cite MDS-VQA:

@article{zou2026mds,
  title={MDS-VQA: Model-Informed Data Selection for Video Quality Assessment},
  author={Zou, Jian and Xu, Xiaoyu and Wang, Zhihua and Wang, Yilin and Adsumilli, Balu and Ma, Kede},
  journal={arXiv preprint arXiv:2603.11525},
  year={2026}
}