GPT Model Training Repository

Overview

This repository contains code for training a GPT-based model. The project is structured to handle the configuration, data preparation, model definition, and training processes for a GPT model.

File Structure

• config.py: Contains configuration details for the model and training process, such as hyperparameters, file paths, and other settings.
• dataset.py: Manages dataset loading and preprocessing. This script is responsible for preparing the data pipeline required for model training.
• distributed.py: Provides functionalities for distributed training, allowing the model to be trained across multiple GPUs or machines.
• download_data.py: A utility script for downloading the necessary datasets or external files for training.
• model.py: Defines the architecture of the GPT model, including layers, forward passes, and other components.
• train.py: The main script for training the model. It includes code to initialize the model, load data, and handle training loops.
• utils.py: Contains various utility functions that are used throughout the project, such as logging, checkpoint saving, or performance metrics.

Usage

1. Install dependencies: Ensure you have the required libraries installed by running:

   pip install -r requirements.txt

2. Download data: Use the download_data.py script to download the necessary datasets:

   python download_data.py

3. Configure settings: Adjust settings in config.py to suit your specific training environment, such as modifying hyperparameters, data paths, or training options.

4. Train the model: Run the train.py script to begin training:

   python train.py

5. Distributed training: If you are training the model across multiple GPUs or machines, ensure that distributed.py is properly configured.

Contributing

Feel free to contribute to this project by submitting a pull request or opening an issue to report bugs or suggest features.

License

This project is licensed under the MIT License.