README.md

metadata

title: Gemma Fine-tuning UI
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.30.0
app_file: app.py
pinned: false

Gemma Fine-tuning UI

A web-based user interface for fine-tuning Google's Gemma models using Hugging Face infrastructure.

Features

• Dataset Upload: Upload and preprocess your custom training data in CSV, JSON, or JSONL format
• Model Configuration: Configure Gemma model version and hyperparameters
• Training Management: Start, monitor, and manage fine-tuning jobs
• Evaluation: Test your fine-tuned model with interactive generation
• Export Options: Use your model directly from Hugging Face or export in various formats

Installation

1. Clone this repository:

git clone https://github.com/yourusername/gemma-finetuning-ui.git
cd gemma-finetuning-ui

2. Create a virtual environment and install dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Run the application:

streamlit run app.py

Usage

1. Authentication: Provide your Hugging Face API token with write permissions
2. Dataset Preparation: Upload your dataset and configure column mappings
3. Model Selection: Choose between Gemma 2B or 7B and customize training parameters
4. Training: Start the fine-tuning process and monitor progress
5. Evaluation: Test your fine-tuned model with custom prompts
6. Deployment: Export or directly use your model from Hugging Face

Hugging Face Spaces Deployment

This application is designed to be deployed easily on Hugging Face Spaces:

1. Create a new Space on Hugging Face Spaces
2. Select Streamlit as the SDK
3. Connect your GitHub repository or upload the files directly
4. The Space will automatically detect and install the requirements

Requirements

• Python 3.8+
• Streamlit 1.30.0+
• Hugging Face Account with API token
• For training: GPU access (recommended)

Project Structure

.
├── app.py              # Main Streamlit application
├── pages/              # Multi-page app components
│   ├── 01_Dataset_Upload.py
│   ├── 02_Model_Configuration.py
│   ├── 03_Training_Monitor.py
│   └── 04_Evaluation.py
├── utils/              # Utility functions
│   ├── auth.py         # Authentication utilities
│   ├── huggingface.py  # Hugging Face API integration
│   ├── training.py     # Training utilities
│   └── ui.py           # UI components and styling
├── data/               # Sample data and uploads
└── requirements.txt    # Project dependencies

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Google Gemma Models
• Hugging Face Transformers
• Streamlit
• PEFT - Parameter-Efficient Fine-Tuning

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Developed as a simplified interface for fine-tuning Gemma models with Hugging Face.