This project provides a PyTorch implementation of OpenAI's GPT-2 language model, targeting researchers and developers interested in training, fine-tuning, and deploying GPT-2 for text generation tasks. It offers a comprehensible and optimized codebase for unsupervised multitask learning.

How It Works

The implementation focuses on core GPT-2 architecture components, enabling users to train models from scratch on custom corpora or fine-tune existing checkpoints. It supports standard training loops, evaluation metrics, and text generation with nucleus sampling. Performance optimizations include optional automatic mixed-precision (AMP) and gradient checkpointing via NVIDIA Apex.

Quick Start & Requirements

• Install: pip install -r requirements.txt (assuming requirements.txt exists, otherwise manual install of dependencies).
• Prerequisites: PyTorch, NumPy, Matplotlib, Regex, Tqdm. NVIDIA Apex is recommended for AMP and fused CUDA layers.
• Training: Requires tokenized training/evaluation datasets and a vocabulary file.
• Demo: Google Colab notebooks are available for text generation and evaluation, including a specific notebook for a Korean GPT-2 model.

Highlighted Details

• Supports training from scratch or resuming from checkpoints.
• Offers text generation with nucleus sampling.
• Includes model evaluation and metric visualization.
• Optional integration with NVIDIA Apex for performance gains (AMP, fused CUDA layers).
• Configurable model parameters (layers, heads, dimensions, sequence length).

Maintenance & Community

No specific information on contributors, sponsorships, or community channels (Discord/Slack) is provided in the README.

Licensing & Compatibility

• License: Apache-2.0.
• Compatibility: Permissive license suitable for commercial use and integration with closed-source projects.

Limitations & Caveats

The README does not detail specific limitations, known bugs, or deprecation status. The setup for training requires manual corpus preparation and tokenization.