/model-architecture-nanogpt

name: nanogpt
description: Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Model Architecture, NanoGPT, GPT-2, Educational, Andrej Karpathy, Transformer, Minimalist, From Scratch, Training]
dependencies: [torch, transformers, datasets, tiktoken, wandb]

nanoGPT - Minimalist GPT Training

Quick start

nanoGPT is a simplified GPT implementation designed for learning and experimentation.

**Installation**:

pip install torch numpy transformers datasets tiktoken wandb tqdm

**Train on Shakespeare** (CPU-friendly):

# Prepare data
python data/shakespeare_char/prepare.py

# Train (5 minutes on CPU)
python train.py config/train_shakespeare_char.py

# Generate text
python sample.py --out_dir=out-shakespeare-char

**Output**:

ROMEO:
What say'st thou? Shall I speak, and be a man?

JULIET:
I am afeard, and yet I'll speak; for thou art
One that hath been a man, and yet I know not
What thou art.

Common workflows

Workflow 1: Character-level Shakespeare

**Complete training pipeline**:

# Step 1: Prepare data (creates train.bin, val.bin)
python data/shakespeare_char/prepare.py

# Step 2: Train small model
python train.py config/train_shakespeare_char.py

# Step 3: Generate text
python sample.py --out_dir=out-shakespeare-char

**Config** (`config/train_shakespeare_char.py`):

# Model config
n_layer = 6          # 6 transformer layers
n_head = 6           # 6 attention heads
n_embd = 384         # 384-dim embeddings
block_size = 256     # 256 char context

# Training config
batch_size = 64
learning_rate = 1e-3
max_iters = 5000
eval_interval = 500

# Hardware
device = 'cpu'  # Or 'cuda'
compile = False # Set True for PyTorch 2.0

**Training time**: ~5 minutes (CPU), ~1 minute (GPU)

Workflow 2: Reproduce GPT-2 (124M)

**Multi-GPU training on OpenWebText**:

# Step 1: Prepare OpenWebText (takes ~1 hour)
python data/openwebtext/prepare.py

# Step 2: Train GPT-2 124M with DDP (8 GPUs)
torchrun --standalone --nproc_per_node=8 \
  train.py config/train_gpt2.py

# Step 3: Sample from trained model
python sample.py --out_dir=out

**Config** (`config/train_gpt2.py`):

# GPT-2 (124M) architecture
n_layer = 12
n_head = 12
n_embd = 768
block_size = 1024
dropout = 0.0

# Training
batch_size = 12
gradient_accumulation_steps = 5 * 8  # Total batch ~0.5M tokens
learning_rate = 6e-4
max_iters = 600000
lr_decay_iters = 600000

# System
compile = True  # PyTorch 2.0

**Training time**: ~4 days (8× A100)

Workflow 3: Fine-tune pretrained GPT-2

**Start from OpenAI checkpoint**:

# In train.py or config
init_from = 'gpt2'  # Options: gpt2, gpt2-medium, gpt2-large, gpt2-xl

# Model loads OpenAI weights automatically
python train.py config/finetune_shakespeare.py

**Example config** (`config/finetune_shakespeare.py`):

# Start from GPT-2
init_from = 'gpt2'

# Dataset
dataset = 'shakespeare_char'
batch_size = 1
block_size = 1024

# Fine-tuning
learning_rate = 3e-5  # Lower LR for fine-tuning
max_iters = 2000
warmup_iters = 100

# Regularization
weight_decay = 1e-1

Workflow 4: Custom dataset

**Train on your own text**:

# data/custom/prepare.py
import numpy as np

# Load your data
with open('my_data.txt', 'r') as f:
    text = f.read()

# Create character mappings
chars = sorted(list(set(text)))
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}

# Tokenize
data = np.array([stoi[ch] for ch in text], dtype=np.uint16)

# Split train/val
n = len(data)
train_data = data[:int(n*0.9)]
val_data = data[int(n*0.9):]

# Save
train_data.tofile('data/custom/train.bin')
val_data.tofile('data/custom/val.bin')

**Train**:

python data/custom/prepare.py
python train.py --dataset=custom

When to use vs alternatives

**Use nanoGPT when**:

• Learning how GPT works
• Experimenting with transformer variants
• Teaching/education purposes
• Quick prototyping
• Limited compute (can run on CPU)

**Simplicity advantages**:

• **~300 lines**: Entire model in `model.py`
• **~300 lines**: Training loop in `train.py`
• **Hackable**: Easy to modify
• **No abstractions**: Pure PyTorch

**Use alternatives instead**:

• **HuggingFace Transformers**: Production use, many models
• **Megatron-LM**: Large-scale distributed training
• **LitGPT**: More architectures, production-ready
• **PyTorch Lightning**: Need high-level framework

Common issues

**Issue: CUDA out of memory**

Reduce batch size or context length:

batch_size = 1  # Reduce from 12
block_size = 512  # Reduce from 1024
gradient_accumulation_steps = 40  # Increase to maintain effective batch

**Issue: Training too slow**

Enable compilation (PyTorch 2.0+):

compile = True  # 2× speedup

Use mixed precision:

dtype = 'bfloat16'  # Or 'float16'

**Issue: Poor generation quality**

Train longer:

max_iters = 10000  # Increase from 5000

Lower temperature:

# In sample.py
temperature = 0.7  # Lower from 1.0
top_k = 200       # Add top-k sampling

**Issue: Can't load GPT-2 weights**

Install transformers:

pip install transformers

Check model name:

init_from = 'gpt2'  # Valid: gpt2, gpt2-medium, gpt2-large, gpt2-xl

Advanced topics

**Model architecture**: See [references/architecture.md](references/architecture.md) for GPT block structure, multi-head attention, and MLP layers explained simply.

**Training loop**: See [references/training.md](references/training.md) for learning rate schedule, gradient accumulation, and distributed data parallel setup.

**Data preparation**: See [references/data.md](references/data.md) for tokenization s