NanoGPT Training

Overview

Training GPT-2 scale models (~124M parameters) efficiently on a single GPU. It provides:

• GPT-124M Architecture: Standard transformer with RoPE and modern optimizations
• Tokenized Datasets: Loading pre-tokenized shards from HuggingFace Hub or local files
• Modern Optimizers: Muon optimizer with Newton-Schulz orthogonalization
• Mixed Precision: bfloat16 training on A100 for 2x speedup

Training options:

• Baseline GPT: Standard residual connections
• Experimental residual variants: Optional alternative residual schemes for stability/efficiency

Quick Reference