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).
npxskills add davila7/claude-code-templates--skill nanogptLoading…
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.
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)
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)
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
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
Use nanoGPT when:
Simplicity advantages:
model.pytrain.pyUse alternatives instead:
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
Model architecture: See references/architecture.md for GPT block structure, multi-head attention, and MLP layers explained simply.
Training loop: See references/training.md for learning rate schedule, gradient accumulation, and distributed data parallel setup.
Data preparation: See references/data.md for tokenization strategies (character-level vs BPE) and binary format details.
Shakespeare (char-level):
GPT-2 (124M):
GPT-2 Medium (350M):
Performance:
compile=True: 2× speedupdtype=bfloat16: 50% memory reduction
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).