Text Generation
PEFT
Safetensors
lora
qlora
function-calling
tool-use
windows
agent
gemma
conversational
Instructions to use onevloth/pc-agent-dispatcher-gemma4-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use onevloth/pc-agent-dispatcher-gemma4-e2b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("huihui-ai/Huihui-gemma-4-E2B-it-abliterated") model = PeftModel.from_pretrained(base_model, "onevloth/pc-agent-dispatcher-gemma4-e2b") - Notebooks
- Google Colab
- Kaggle
PC-Agent Dispatcher — Gemma 4 E2B (QLoRA LoRA adapter)
A LoRA adapter that turns Gemma 4 E2B into the dispatcher (router) for PC-Agent: it reads a natural-language request and emits one JSON tool-call for controlling a Windows PC.
{"action": "tool", "tool": "open_app", "args": {"app": "notepad"}}
{"action": "plan", "steps": [{"tool": "open_app", "args": {"app": "notepad"}},
{"tool": "type_text", "args": {"text": "hello"}}]}
{"action": "chat"}
🔗 Full project, desktop app, and demos on GitHub: github.com/AlfatihRabbani/pc-agent
Install & run the full app
git clone https://github.com/AlfatihRabbani/pc-agent
cd pc-agent
scripts\setup.bat :: venv + CUDA torch + deps
python scripts\download_models.py :: base E2B (~10 GB)
.venv\Scripts\hf download onevloth/pc-agent-dispatcher-gemma4-e2b --local-dir models\dispatcher-final
PC-Agent.vbs :: launch the desktop app
Full instructions (incl. the optional 12B chat model via Ollama) are in the repo README.
Details
- Base:
huihui-ai/Huihui-gemma-4-E2B-it-abliterated(load in 4-bit / NF4). - Method: QLoRA, r=16, α=32, attn+MLP projections; 2 epochs (~3,196 steps), seq 512, batch 1 × accum 16, on a single RTX 3080 Ti (12 GB).
- Data: ~25.8k examples — function-calling (xLAM/Hermes-style) + ~2k synthetic Windows-action examples generated from the agent's own tool registry.
- Use it for routing; chat with the base model (adapter disabled).
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
base = "huihui-ai/Huihui-gemma-4-E2B-it-abliterated"
q = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, quantization_config=q, device_map="auto")
model = PeftModel.from_pretrained(model, "onevloth/pc-agent-dispatcher-gemma4-e2b")
License
Derivative of Google Gemma 4 — governed by the Gemma Terms of Use. The PC-Agent code is MIT.
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Model tree for onevloth/pc-agent-dispatcher-gemma4-e2b
Base model
google/gemma-4-E2B Finetuned
google/gemma-4-E2B-it