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k4ng
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Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
k4ng
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
Install vLLM:
bash
pip install -U vllm
Launch an OpenAI-compatible inference server:
bash
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
| 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 |
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.
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.
If you use SCOPE-RL, SCOPE-SFT, SATraj-OS, or SCOPE-Gen, please cite:
bibtex
@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}}
| 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 |