This repository provides an implementation of OpenAI's GPT-2 model for pre-training and sequence generation using TensorFlow 2.0. It is designed for researchers and developers interested in replicating or extending GPT-2's capabilities within the TensorFlow ecosystem.

How It Works

The project implements the GPT-2 architecture, including the transformer decoder blocks, attention mechanisms, and positional encodings. It supports pre-training on custom datasets and generating text sequences based on provided context. The implementation leverages TensorFlow 2.0's eager execution and Keras API for a more Pythonic development experience.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python >= 3.6, TensorFlow-GPU == 2.3.0, NumPy, setuptools, ftfy, tqdm, Click, sentencepiece.
• Setup: Requires cloning the repository and installing dependencies. Pre-training involves data preprocessing.
• Links: OpenAI GPT-2 Paper, OpenWebText

Highlighted Details

• Supports distributed training across multiple GPUs.
• Includes a sequence_generator.ipynb notebook for text generation.
• Offers command-line arguments for configuring pre-training and training parameters.
• Provides TensorBoard logging for monitoring training progress.

Maintenance & Community

• Author: Abhay Kumar (akanyaani@gmail.com)
• Contributions via issues and pull requests are welcome.

Licensing & Compatibility

• License: MIT
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The project lists "Parallel Preprocessing" and a "Fine-Tuning wrapper" as future tasks, indicating these features are not yet implemented. The TensorFlow version is pinned to 2.3.0, which may limit compatibility with newer TensorFlow releases.