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

Libraries

How to use UI-MOPD/Qwen3-VL-8B-Thinking-Desktop-SFT 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-Desktop-SFT")
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-Desktop-SFT")
model = AutoModelForMultimodalLM.from_pretrained("UI-MOPD/Qwen3-VL-8B-Thinking-Desktop-SFT")
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-Desktop-SFT 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-Desktop-SFT"
# 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-Desktop-SFT",
		"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-Desktop-SFT

SGLang

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

Qwen3-VL-8B-Thinking-Desktop-SFT

This is the Desktop SFT model from the UI-MOPD project — an 8B model supervised-fine-tuned on desktop GUI interaction data. This checkpoint is released as an auxiliary artifact to demonstrate the quality of the Uni-GUI SFT data on the 8B tier and to enable community work such as Model-Merge (TIES / DARE) with other 8B GUI agents.

Note: This model is not used in the UI-MOPD student training path. In the UI-MOPD pipeline, the 8B student is cold-started directly from the Qwen3-VL-8B-Thinking base and trained via on-policy distillation from the 32B teachers.

Model Description

Qwen3-VL-8B-Thinking-Desktop-SFT is fine-tuned from Qwen3-VL-8B-Thinking on desktop interaction trajectories from the Uni-GUI dataset. It is trained independently on the same desktop data used by the 32B Desktop Teacher, but at the 8B scale.

Key Highlights

Base Model: Qwen3-VL-8B-Thinking
Training Data: Desktop subset of Uni-GUI (interaction trajectories from OSWorld-style desktop environments)
Training: Supervised fine-tuning (SFT) on desktop GUI interaction trajectories
Role: Auxiliary artifact — demonstrates Uni-GUI data quality on the 8B tier and enables Model-Merge experiments. Not used in the UI-MOPD student training path.

Relationship to the UI-MOPD Pipeline

The actual UI-MOPD training pipeline is:

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: The 8B student is cold-started from Qwen3-VL-8B-Thinking base (not from any SFT checkpoint) and trained via DAPO (reinforcement learning) with multi-teacher on-policy distillation using platform-conditioned routing from the 32B teachers.

This 8B SFT model sits outside the distillation pipeline. It is trained independently on the same Uni-GUI desktop subset and released separately for the community.

Intended Use

This model is designed to:

Be used as a standalone desktop GUI agent for executing desktop tasks (e.g., file management, web browsing, application control)
Serve as a reference for evaluating the quality of the Uni-GUI SFT data on the 8B scale
Provide a base for Model-Merge / TIES / DARE experiments with other 8B GUI agents
Offer a baseline for comparing SFT-only vs. distillation-enhanced performance

How to Use

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

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

Citation

@misc{lian2026uimopdmultiplatformonpolicydistillation,
      title={UI-MOPD: Multi-Platform On-Policy Distillation for Continual GUI Agent Learning}, 
      author={Niu Lian and Alan Chen and Zhehao Yu and Chengzhen Duan and Fazhan Liu and Hui Liu and Pei Fu and Jian Luan and Yaowei Wang and Shu-Tao Xia and Jinpeng Wang},
      year={2026},
      eprint={2607.04425},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.04425}, 
}