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1{"action": "tool", "tool": "open_app", "args": {"app": "notepad"}}
2{"action": "plan", "steps": [{"tool": "open_app", "args": {"app": "notepad"}},
3 {"tool": "type_text", "args": {"text": "hello"}}]}
4{"action": "chat"}🔗 Full project, desktop app, and demos on GitHub: github.com/AlfatihRabbani/pc-agent
1git clone https://github.com/AlfatihRabbani/pc-agent
2cd pc-agent
3scripts\setup.bat :: venv + CUDA torch + deps
4python scripts\download_models.py :: base E2B (~10 GB)
5.venv\Scripts\hf download onevloth/pc-agent-dispatcher-gemma4-e2b --local-dir models\dispatcher-final
6PC-Agent.vbs :: launch the desktop apphuihui-ai/Huihui-gemma-4-E2B-it-abliterated (load in 4-bit / NF4).1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
3from peft import PeftModel
4
5base = "huihui-ai/Huihui-gemma-4-E2B-it-abliterated"
6q = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
7 bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
8tok = AutoTokenizer.from_pretrained(base)
9model = AutoModelForCausalLM.from_pretrained(base, quantization_config=q, device_map="auto")
10model = PeftModel.from_pretrained(model, "onevloth/pc-agent-dispatcher-gemma4-e2b")