Instructions to use extreme1228/ScaleCUA-qwen3.5-osworld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use extreme1228/ScaleCUA-qwen3.5-osworld with Transformers:

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

pipe = pipeline("image-text-to-text", model="extreme1228/ScaleCUA-qwen3.5-osworld")
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("extreme1228/ScaleCUA-qwen3.5-osworld")
model = AutoModelForMultimodalLM.from_pretrained("extreme1228/ScaleCUA-qwen3.5-osworld", 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 extreme1228/ScaleCUA-qwen3.5-osworld with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "extreme1228/ScaleCUA-qwen3.5-osworld"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "extreme1228/ScaleCUA-qwen3.5-osworld",
		"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/extreme1228/ScaleCUA-qwen3.5-osworld

SGLang

How to use extreme1228/ScaleCUA-qwen3.5-osworld 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 "extreme1228/ScaleCUA-qwen3.5-osworld" \
    --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": "extreme1228/ScaleCUA-qwen3.5-osworld",
		"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 "extreme1228/ScaleCUA-qwen3.5-osworld" \
        --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": "extreme1228/ScaleCUA-qwen3.5-osworld",
		"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 extreme1228/ScaleCUA-qwen3.5-osworld with Docker Model Runner:
```
docker model run hf.co/extreme1228/ScaleCUA-qwen3.5-osworld
```

ScaleCUA-qwen3.5-osworld

A Qwen3.5-9B computer-use agent from ScaleCUA, trained for OSWorld. Stage: online-RL (RLVR).

📄 Paper: SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL
💻 Code: https://github.com/THUDM/SCALE-CUA
🤗 Collection: https://huggingface.co/collections/extreme1228/scalecua-6a4cad5e3c80a230622eaff3 · 🤖 ModelScope

What is this

This is the online-RL checkpoint (the released agent). Its SFT start point is extreme1228/ScaleCUA-qwen3.5-osworld-sft. It reaches 68.7% on OSWorld (open-source SoTA).

ScaleCUA scales online RL for computer-use agents by pairing VeriGen (verifiable GUI-task synthesis) with Frontier Sampling and Visual Context Segmentation. See the repo for details.

Usage

Serve with an OpenAI-compatible server (e.g. vLLM) and drive it with the evaluation runtime in osworld_eval:

pip install -U vllm
python -m vllm.entrypoints.openai.api_server --model extreme1228/ScaleCUA-qwen3.5-osworld \
  --served-model-name scalecua --trust-remote-code --port 8000

Then follow REPRODUCE.md to run OSWorld evaluation.

Results

68.7% on OSWorld (open-source SoTA) (see the paper for the full table).

License

Weights are released for research; the model inherits the license of its base model (Qwen3.5-9B). Repository code is under the licenses in the GitHub repo.

Citation

@misc{lv2026scalecua,
  title  = {ScaleCUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL},
  author = {Bowen Lv and Xiao Liu and Yanyu Ren and Hanyu Lai and Bohao Jing and Hanchen Zhang and Yanxiao Zhao and Shuntian Yao and Jie Tang and Yuxiao Dong},
  year   = {2026},
  url    = {https://github.com/THUDM/SCALE-CUA}
}