Let's reproduce GPT-2 (124M)

Summary

This detailed tutorial demonstrates how to reproduce OpenAI’s GPT-2 (124M parameter version) from scratch using modern tools and techniques. The guide covers:

• Complete implementation of GPT-2 architecture in PyTorch
• Optimization techniques for efficient training:
  • Tensor Float 32 (TF32) precision
  • Mixed precision training with bfloat16
  • Flash Attention
  • Distributed training across multiple GPUs
  • Gradient accumulation
• Training on the Fine-web EDU dataset
• Performance evaluation using:
  • Validation loss
  • HellaSwag benchmark
  • Text generation samples

Key Achievements

• Successfully matched GPT-2 124M performance with only 10B tokens (vs original 100B)
• Achieved 33.24% accuracy on HellaSwag (surpassing original GPT-2 124M)
• Efficient training completion in 2-8 hours on modern hardware
• Implementation of proper weight initialization and learning rate scheduling

Technical Stack

• PyTorch with distributed data parallel (DDP)
• CUDA optimization
• Hugging Face transformers and datasets
• Custom data loading and processing pipeline

The guide demonstrates how modern hardware and optimization techniques can reproduce GPT-2’s performance more efficiently than the original 2019 training process.