Nanogpt

NanoGPT is a community skill for training and fine-tuning small GPT language models using the nanoGPT codebase, covering model configuration, data preparation, training loops, checkpoint management, and text generation for language model experimentation and education.

What Is This?

Overview

NanoGPT provides tools for working with a minimal GPT implementation designed for learning and experimentation. It covers model configuration that defines transformer architecture parameters including layer count, attention heads, and embedding dimensions, data preparation that tokenizes and formats text corpora into training batches with proper sequence handling, training loops that run gradient descent with learning rate scheduling and loss monitoring, checkpoint management that saves and loads model weights during and after training, and text generation that produces completions from trained models using sampling strategies. The skill enables researchers to understand GPT training from minimal code.

Who Should Use This

This skill serves ML students studying transformer architectures through hands-on training, researchers prototyping language model experiments at small scale, and educators teaching deep learning with readable reference implementations.

Why Use It?

Problems It Solves

Full-scale language model frameworks are too complex for understanding core training mechanics. Production training codebases obscure fundamental concepts behind layers of optimization and infrastructure code. Setting up training from scratch requires implementing attention, positional encoding, and training utilities from raw components. Large model training costs are prohibitive for educational exploration.

Core Highlights

Model builder configures GPT architecture parameters with readable defaults. Data processor prepares text datasets into tokenized training batches. Trainer runs optimization loops with configurable scheduling and logging. Generator produces text from trained checkpoints using temperature and top-k sampling.

How to Use It?

Basic Usage

from dataclasses import (
  dataclass)

@dataclass
class GPTConfig:
  block_size: int = 256
  vocab_size: int = 50304
  n_layer: int = 6
  n_head: int = 6
  n_embd: int = 384
  dropout: float = 0.2
  bias: bool = False

@dataclass
class TrainConfig:
  batch_size: int = 64
  max_iters: int = 5000
  learning_rate:\
    float = 3e-4
  weight_decay:\
    float = 0.1
  warmup_iters:\
    int = 100
  lr_decay_iters:\
    int = 5000
  min_lr: float = 3e-5
  eval_interval:\
    int = 250
  eval_iters: int = 200
  log_interval:\
    int = 10
  ckpt_dir: str =\
    'checkpoints'

Real-World Examples

import torch

def train(model, config,
  train_data, val_data):
  optimizer = model\
    .configure_optimizers(
      config.weight_decay,
      config.learning_rate)

  for step in range(
    config.max_iters):
    # Learning rate decay
    lr = get_lr(
      step, config)
    for pg in optimizer\
        .param_groups:
      pg['lr'] = lr

    # Get batch
    x, y = get_batch(
      train_data,
      config.batch_size,
      model.config\
        .block_size)

    # Forward pass
    logits, loss = model(
      x, targets=y)

    # Backward pass
    optimizer.zero_grad(
      set_to_none=True)
    loss.backward()
    torch.nn.utils\
      .clip_grad_norm_(
        model.parameters(),
        1.0)
    optimizer.step()

    if (step %
        config.eval_interval
        == 0):
      val_loss = evaluate(
        model, val_data,
        config)
      print(
        f'step {step}: '
        f'train {loss:.4f}'
        f' val {val_loss:.4f}')

Advanced Tips

Start with a small model configuration to verify training pipeline correctness before scaling up parameters and dataset size. Use gradient accumulation to simulate larger batch sizes on hardware with limited GPU memory. Monitor both training and validation loss curves to detect overfitting early and adjust dropout or training duration accordingly.

When to Use It?

Use Cases

Train a character-level GPT on a specific text corpus to study language model behavior at small scale. Fine-tune a pre-trained checkpoint on domain-specific text for specialized generation. Compare model configurations by training multiple small variants with different layer counts and embedding sizes.

Related Topics

GPT, transformer architecture, language model training, nanoGPT, deep learning, text generation, and model fine-tuning.

Important Notes

Requirements

PyTorch with CUDA support for GPU-accelerated training. Training dataset prepared as tokenized binary files. Sufficient GPU memory for the selected model configuration size.

Usage Recommendations

Do: start with proven hyperparameter defaults before experimenting with custom configurations. Save checkpoints at regular intervals during training to enable recovery from interruptions. Compare training runs using logged loss values to evaluate configuration changes.

Don't: train large model configurations without first validating the pipeline on a small model that converges. Skip validation loss evaluation since training loss alone does not indicate generalization quality. Use learning rates from large model papers directly since optimal rates vary with model size.

Limitations

NanoGPT is designed for education and experimentation rather than production language model deployment. Training quality on small datasets does not predict performance at larger scales with different data distributions. The minimal codebase omits optimizations like mixed precision and distributed training that larger frameworks provide.