Nanogpt

--- name: nanogpt description: An educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning about transformers. By Andrej Karpathy. Perfect for understanding the 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: ``bash pip install torch numpy transformers datasets tiktoken wandb tqdm **Train on Shakespeare** (CPU-friendly): bash # 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**: bash # 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/trainshakespearechar.py): python # 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**: bash # 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): python # 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**: python # 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): python # 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**: python # 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**: bash 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