Instructions to use k4ng/SCOPE-SFT-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use k4ng/SCOPE-SFT-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="k4ng/SCOPE-SFT-9B")
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("k4ng/SCOPE-SFT-9B")
model = AutoModelForMultimodalLM.from_pretrained("k4ng/SCOPE-SFT-9B", 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 k4ng/SCOPE-SFT-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "k4ng/SCOPE-SFT-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "k4ng/SCOPE-SFT-9B",
		"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/k4ng/SCOPE-SFT-9B

SGLang

How to use k4ng/SCOPE-SFT-9B 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 "k4ng/SCOPE-SFT-9B" \
    --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": "k4ng/SCOPE-SFT-9B",
		"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 "k4ng/SCOPE-SFT-9B" \
        --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": "k4ng/SCOPE-SFT-9B",
		"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 k4ng/SCOPE-SFT-9B with Docker Model Runner:
```
docker model run hf.co/k4ng/SCOPE-SFT-9B
```

SCOPE-SFT-9B

SCOPE-SFT-9B is a computer-use agent (CUA) trained under the SCOPE (Safety and Capability Optimization for Policy Execution) framework to balance task-execution capability with safety-aware decision-making. The model is initialized from Qwen3.5-9B and jointly fine-tuned on capability demonstrations, safe-continuation trajectories, and explicit-refusal trajectories.

In our evaluation, SCOPE-SFT-9B achieves a 49.72% task success rate on OSWorld and a 66.30% attack-avoidance rate on OS-BLIND, corresponding to a capability-safety harmonic mean of 56.83%.

SCOPE-SFT-9B is trained on SATraj-OS using joint supervised fine-tuning. Capability demonstrations teach the model to complete benign desktop tasks, while safe-continuation and explicit-refusal trajectories teach it to respond appropriately when a request or execution environment presents a safety risk. SCOPE-SFT-9B also serves as the initialization checkpoint for SCOPE-RL-9B.

Quick Start

Install vLLM:

pip install -U vllm

Launch an OpenAI-compatible inference server:

vllm serve k4ng/SCOPE-SFT-9B \
    --host 0.0.0.0 \
    --port 8000 \
    --tensor-parallel-size 1 \
    --data-parallel-size 2 \
    --trust-remote-code \
    --served-model-name scope-sft

Results

Type	Model	H ↑	OSWorld ↑	OS-BLIND ↑
Closed-source	Claude 4.5 Sonnet	37.78	62.90	27.00
Closed-source	Qwen3.7-Plus	9.36	73.33	5.00
Open-source	EvoCUA-8B	10.62	46.06	6.00
Open-source	EvoCUA-32B	4.42	56.73	2.30
Open-source	OpenCUA-7B	3.21	28.85	1.70
Open-source	OpenCUA-32B	1.94	34.79	1.00
Open-source	OpenCUA-72B	4.38	44.99	2.30
Open-source	UI-TARS-1.5-7B	8.46	27.52	5.00
Open-source	ComputerRL	21.77	48.90	14.00
Open-source	Qwen3.5-9B	8.93	41.80	5.00
Open-source	Qwen3-VL-8B	16.27	33.90	10.70
SCOPE	SCOPE-SFT-9B	56.83	49.72	66.30
SCOPE	SCOPE-RL-9B	58.80	54.17	64.30

All values are percentages, and ↑ indicates that higher is better. (H) is the harmonic mean of the OSWorld task success rate and the OS-BLIND attack-avoidance rate.

License

This model is subject to the license terms of its base model, Qwen3.5-9B. Licensing information for the code and training data is available in the corresponding repositories.

Citation

If you use SCOPE-RL, SCOPE-SFT, SATraj-OS, or SCOPE-Gen, please cite:

@misc{kang2026scope,
  title  = {Beyond Task Completion: Training Capable and Safe Computer-Use Agents},
  author = {Zeyu Kang and Zhenyun Yin and Yang Zhang and Shan He and Shanzhe Lei and Yanjiu Zhong and Xinquan Chen and Xuhong Wang},
  year   = {2026}
}