Instructions to use UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student with Transformers:

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

pipe = pipeline("image-text-to-text", model="UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student")
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("UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student")
model = AutoModelForMultimodalLM.from_pretrained("UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student")
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 UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student",
		"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/UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student

SGLang

How to use UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student 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 "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student" \
    --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": "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student",
		"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 "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student" \
        --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": "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student",
		"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 UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student with Docker Model Runner:
```
docker model run hf.co/UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student
```

Qwen3-VL-8B-Thinking-UI-MOPD-Student

This is the Student model from the UI-MOPD project — a cross-platform GUI agent trained via multi-teacher on-policy distillation for continual GUI agent learning.

Model Description

Qwen3-VL-8B-Thinking-UI-MOPD-Student is trained from Qwen3-VL-8B-Thinking using the UI-MOPD framework. It learns from two platform-specific 32B teachers (Desktop Teacher and Mobile Teacher) through reinforcement learning with platform-conditioned distillation, achieving balanced cross-platform performance on both desktop and mobile environments.

Key Highlights

Base Model: Qwen3-VL-8B-Thinking
Training Method: Multi-teacher on-policy distillation with DAPO + platform-conditioned KL regularization
Teachers: Qwen3-VL-32B-Thinking-Desktop-Teacher + Qwen3-VL-32B-Thinking-Mobile-Teacher
OSWorld Performance: 38.2% task success rate
MobileWorld Performance: 12.0% task success rate

Training Details

This model is obtained in Stage 2 of the UI-MOPD training pipeline:

Stage 1: Supervised fine-tuning of Qwen3-VL-32B-Thinking on platform-specific data to produce a Desktop Teacher (46.3% on OSWorld) and a Mobile Teacher (16.2% on MobileWorld).
Stage 2 (This Model): The 8B student is trained with reinforcement learning (DAPO) combined with multi-teacher on-policy distillation. A platform-conditioned router selects the appropriate teacher based on the current environment, and adaptive KL masking prevents over-regularization.

Key Training Components

Platform-Conditioned Routing: Routes each rollout to the corresponding platform-specific teacher
K3 Estimator: Efficient single-sample KL divergence estimator
Adaptive KL Masking: Removes teacher penalty when task reward is already sufficient

Performance

Method	OSWorld (Desktop)	MobileWorld (Mobile)
Qwen3-VL-8B-Thinking (base)	33.9%	7.7%
Mixed-SFT	35.0%	6.4%
Model Merge (TIES)	36.8%	0%
UI-MOPD (this model)	38.2%	12.0%

UI-MOPD achieves state-of-the-art balanced cross-platform performance, with +12.7% relative improvement on OSWorld and +55.8% on MobileWorld compared to the base model.

GUI Grounding & Understanding

Model	AndroidControl	ScreenSpot-Pro	ScreenSpotV2	OSWorld-G
Qwen3-VL-8B-Thinking (base)	78.73%	43.71%	91.27%	52.13%
Model Merge (TIES)	74.01%	37.13%	88.60%	47.16%
UI-MOPD (this model)	80.05%	43.14%	90.88%	52.84%

UI-MOPD preserves GUI grounding and visual understanding capabilities while improving interactive task performance.

Intended Use

Cross-platform GUI agent for executing tasks on both desktop (e.g., web browsing, file management) and mobile (e.g., app navigation, settings control) environments
Research on continual learning and multi-platform adaptation for GUI agents

How to Use

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student",
    torch_dtype="auto",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("UI-MOPD/Qwen3-VL-8B-Thinking-UI-MOPD-Student")

Citation

@article{lian2025uimopd,
  title={UI-MOPD: Multi-platform On-Policy Distillation for Continual GUI Agent Learning},
  author={Lian, Niu and Chen, Alan and Yu, Zhehao and Duan, Chengzhen and Liu, Fazhan and Liu, Hui and Fu, Pei and Luan, Jian and Wang, Yaowei and Xia, Shu-Tao and Wang, Jinpeng},
  year={2025}
}

Related Resources

Project Page: https://elisspectre.github.io/UI-MOPD/
Code: https://github.com/EliSpectre/UI-MOPD
Paper: UI-MOPD Paper
Desktop Teacher: UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher

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Safetensors

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Tensor type

BF16

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