README.md

---
license: gemma
base_model: huihui-ai/Huihui-gemma-4-E2B-it-abliterated
library_name: peft
pipeline_tag: text-generation
tags:
  - lora
  - peft
  - qlora
  - function-calling
  - tool-use
  - windows
  - agent
  - gemma
---

# PC-Agent Dispatcher — Gemma 4 E2B (QLoRA LoRA adapter)

A LoRA adapter that turns **Gemma 4 E2B** into the *dispatcher* (router) for
[**PC-Agent**](https://github.com/AlfatihRabbani/pc-agent): it reads a natural-language
request and emits one JSON tool-call for controlling a Windows PC.

```json
{"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](https://github.com/AlfatihRabbani/pc-agent)**

## Install & run the full app

```bat
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](https://github.com/AlfatihRabbani/pc-agent#installation).

## 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

```python
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](https://ai.google.dev/gemma/terms). The PC-Agent code is MIT.