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---
title: Gemma Fine Tuning
emoji: 🐠
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 5.20.1
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
---
# Gemma Fine-Tuning UI
A user-friendly web interface for fine-tuning Google's Gemma models on custom datasets.
## Features
- **Easy Dataset Upload**: Support for CSV, JSONL, and plain text formats
- **Intuitive Hyperparameter Configuration**: Adjust learning rates, batch sizes, and other parameters with visual controls
- **Real-time Training Visualization**: Monitor loss curves, evaluation metrics, and sample outputs during training
- **Flexible Model Export**: Download your fine-tuned model in PyTorch, GGUF, or Safetensors formats
- **Comprehensive Documentation**: Built-in guidance for fine-tuning process
## Getting Started
### Prerequisites
- Python 3.8 or later
- PyTorch 2.0 or later
- Hugging Face account with access to Gemma models
### Installation
1. Clone this repository:
```bash
git clone https://github.com/yourusername/gemma-fine-tuning.git
cd gemma-fine-tuning
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Launch the application:
```bash
python app.py
```
4. Open your browser and navigate to `http://localhost:7860`
## Usage Guide
### 1. Dataset Preparation
Prepare your dataset in one of the supported formats:
**CSV format**:
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