NorthCUA puts three open computer-use models on Hugging Face under Apache 2.0, from 9B to 397B
All three are post-trained on Qwen3.5: Nano (9B dense), Mini (35B, 3B active) and Pro (397B, 17B active). The project's report puts Pro at 88.64% on OSWorld-Verified, but the model card says those scores aren't tied to the files you download.
The NorthCUA project published its model family on Hugging Face under beipo-ai: NorthCUA-Nano (9B, dense), NorthCUA-Mini (35B total, 3B active) and NorthCUA-Pro (397B total, 17B active), all Apache 2.0. Each takes a request, screenshots and interaction history, and returns reasoning, replies and tool calls for a desktop or browser runtime. It's independent post-training on Qwen3.5, not an official Qwen release. According to AITrending's read of the upload history, Nano's weights finished uploading on November 2.
The model doesn't click anything by itself. The card says to run it with the official NorthCUA Harness on GitHub, which supplies the tools, runs the actions and sends back new screenshots. Coordinates are a strict contract: x and y sit in a 0 to 1000 space relative to the screenshot, and the harness turns them into pixels.
The report's numbers, all self-reported: on OSWorld-Verified (361 tasks, 100-step limit) Nano scores 78.90%, Mini 85.30% and Pro 88.64%. On WebArena-Verified the three get 67.16%, 70.43% and 73.89%. The card says plainly that these came from research checkpoints and aren't new measurements of the exported files. Training used more than 100,000 GUI trajectories and 4 million interaction steps in mid-training, then about 3,000 verified long trajectories.
Nano's files come to about 18.84 GB, mostly BF16 weights, per its card as summarized by AITrending.
Why it matters for builders
Open computer-use weights at 9B mean you can test screen automation on your own hardware instead of paying per screenshot to a hosted model. The coordinate contract is the first thing that'll break if you wire it into your own loop.
Try this
pip install -U huggingface_hub, then hf download beipo-ai/NorthCUA-Nano --local-dir ./NorthCUA-Nano and run shasum -a 256 -c checksums.sha256 inside the folder. Drive it only through the NorthCUA Harness, in a disposable VM, and keep coordinates in its 0 to 1000 space.
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