Instructions to use UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher 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-32B-Thinking-Desktop-Teacher")
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-32B-Thinking-Desktop-Teacher")
model = AutoModelForMultimodalLM.from_pretrained("UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher")
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-32B-Thinking-Desktop-Teacher 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-32B-Thinking-Desktop-Teacher"
# 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-32B-Thinking-Desktop-Teacher",
		"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-32B-Thinking-Desktop-Teacher

SGLang

How to use UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher 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-32B-Thinking-Desktop-Teacher" \
    --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-32B-Thinking-Desktop-Teacher",
		"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-32B-Thinking-Desktop-Teacher" \
        --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-32B-Thinking-Desktop-Teacher",
		"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-32B-Thinking-Desktop-Teacher with Docker Model Runner:
```
docker model run hf.co/UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher
```

Qwen3-VL-32B-Thinking-Desktop-Teacher

This is the Desktop Teacher model from the UI-MOPD project — a platform-specific expert trained for desktop GUI interaction tasks.

Model Description

Qwen3-VL-32B-Thinking-Desktop-Teacher is fine-tuned from Qwen3-VL-32B-Thinking on desktop interaction trajectories from the Uni-GUI dataset. It serves as the desktop-platform teacher in the UI-MOPD multi-teacher on-policy distillation framework.

Key Highlights

Base Model: Qwen3-VL-32B-Thinking
Training Data: Desktop subset of Uni-GUI (~160K interaction steps across ~11.5K trajectories)
Role: Platform-specific teacher for desktop environments in the UI-MOPD distillation pipeline
OSWorld Performance: 46.3% task success rate (vs. 41.0% base model)

Training Details

This model is obtained in Stage 1 of the UI-MOPD training pipeline:

Stage 1 (This Model): Supervised fine-tuning of Qwen3-VL-32B-Thinking on desktop GUI interaction trajectories from Uni-GUI to produce a platform-specific desktop expert.
Stage 2: The desktop teacher (this model) and a mobile teacher jointly guide a shared 8B student policy via multi-teacher on-policy distillation with platform-conditioned routing.

Performance

Method	OSWorld
Qwen3-VL-32B-Thinking (base)	41.0%
Desktop Teacher (this model)	46.3%

Intended Use

This model is designed to:

Serve as a teacher model in the UI-MOPD distillation framework for training cross-platform GUI agents
Be used as a standalone desktop GUI agent for executing computer tasks (e.g., web browsing, file management, application control)

How to Use

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher",
    torch_dtype="auto",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("UI-MOPD/Qwen3-VL-32B-Thinking-Desktop-Teacher")

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

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Safetensors

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33B params

Tensor type

BF16

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