This project provides a user-friendly, visual interface for training GPT models, abstracting away complex command-line operations. It targets deep learning beginners, researchers, and developers interested in easily experimenting with and training their own GPT models, offering a simplified path to large model development.

How It Works

Mini-NanoGPT builds upon the karpathy/nanoGPT framework, introducing a Gradio-based graphical user interface. This allows users to perform data processing, tokenization (character-level or GPT-2), model training, and text generation through a few clicks. It supports multi-processing and distributed training for efficiency, with real-time feedback on training progress and parameter visualization.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Launch project: python main.py
• Access interface: http://localhost:7860 (browser)
• Prerequisites: Python 3.7+

Highlighted Details

• Visual interface for one-click data processing, training, and generation.
• Supports character-level and GPT-2 tokenization.
• Real-time loss curve visualization and parameter adjustment.
• Option to resume previous training sessions.

Maintenance & Community

The project welcomes contributions via issues and pull requests. Community engagement is encouraged through sharing usage experiences.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

Performance is significantly impacted by hardware; GPU usage is highly recommended for faster training. The README notes potential "Dataset too small" errors if validation block size exceeds validation data size, requiring parameter adjustment.