OpenCUA: Open Foundations for Computer-Use Agents
Paper • 2508.09123 • Published • 33
World Model LoRA Adapter
LoRA adapter fine-tuned on ageppert/world-model-transitions for predicting GUI state transitions in desktop computer-use tasks.
Base Model
Usage
from peft import PeftModel
from transformers import AutoModelForVision2Seq, AutoProcessor
base_model = AutoModelForVision2Seq.from_pretrained("xlangai/OpenCUA-7B", trust_remote_code=True)
model = PeftModel.from_pretrained(base_model, "ageppert/world-model-7b-lora")
processor = AutoProcessor.from_pretrained("ageppert/world-model-7b-lora")
Training
- LoRA rank: 16, alpha: 32
- Target modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
- Learning rate: 0.0002
- Epochs: 3
- Train loss: 4.954768469485616
- Eval loss: 0.5774359703063965
Citation
Based on OpenCUA (arXiv:2508.09123).
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