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

Topic	Reference
Model Architecture	GPT Architecture
Data Loading	Tokenized Data
Optimizers	Optimizers
Training Loop	Training Loop
Hyperparameters	Hyperparameters

Installation

pip install torch einops numpy huggingface_hub

Minimal Example

import modal

app = modal.App("gpt-training")

image = modal.Image.debian_slim(python_version="3.11").pip_install(
    "torch", "einops", "numpy", "huggingface_hub"
)

@app.function(gpu="A100", image=image, timeout=3600)
def train():
    import torch
    from dataclasses import dataclass

    @dataclass
    class GPTConfig:
        block_size: int = 1024
        vocab_size: int = 50257
        n_layer: int = 12
        n_head: int = 12
        n_embd: int = 768
        dropout: float = 0.0
        bias: bool = False

    # Download data, build model, train
    # ... (see references for full implementation)

    return {"final_loss": final_loss}

@app.local_entrypoint()
def main():
    results = train.remote()
    print(results)

Common Imports