Nano_GPT

A minimalist yet performant GPT implementation built with PyTorch. Train or fine‑tune Transformer language models on a single GPU in just a few hundred lines of code.

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Key Features

Capability	Details
Tiny Code Footprint	Core GPT model fits in ≈ 250 LOC. Clear, instructive implementation that maps 1‑to‑1 with the equations.
One‑Command Training	Opinionated scripts (fineweb.py, hellaswag.py, train_gpt2.py) abstract away boilerplate—just point to a text file or Hugging Face dataset and go.
Single‑GPU Friendly	Out‑of‑the‑box configs run on a consumer‑grade GPU (≥8 GB VRAM) thanks to gradient checkpointing & mixed precision.
Pluggable Tokenizers	Works with byte‑level BPE, SentencePiece, or any transformers tokenizer.
Research Ready	Explicit hooks for custom loss functions, curriculum schedules, or architectural tweaks.

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Project Structure

.
├── fineweb.py        # Train from scratch on the FineWeb corpus
├── hellaswag.py      # Few‑shot / zero‑shot tune on HellaSwag (commonsense QA)
├── train_gpt2.py     # Re‑implement the original GPT‑2 small config
├── play.ipynb        # Notebook playground for rapid experiments
├── input.txt         # Tiny demo corpus (Shakespeare)
├── README.md         # You are here
└── ...

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Quick Start

Prereqs: Python ≥ 3.10 · PyTorch ≥ 2.1 (CUDA 11+) · (optional) transformers, datasets

# 1⃣ Clone & install
$ git clone https://github.com/<your‑org>/Nano_GPT.git && cd Nano_GPT
$ pip install -r requirements.txt  # lightweight: torch, tqdm, sentencepiece, transformers

# 2⃣ Sanity check on the toy corpus
$ python train_gpt2.py --dataset input.txt --max-iter 500 --eval-interval 50

# 3⃣ Generate text
$ python train_gpt2.py --generate "To be, or not to be" --checkpoint ckpt/latest.pt

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Datasets

Script	Dataset	Prep Utility
fineweb.py	FineWeb/Common Crawl	python tools/prepare_fineweb.py
hellaswag.py	HellaSwag	Auto‑downloads via datasets API
Custom text	Any UTF‑8 .txt	python tools/txt2bin.py --vocab‑size 50_000

All preprocessing pipelines yield a binary .bin file of token IDs for maximal I/O throughput.

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Training Recipes

FineWeb (1B tokens) — 125 M param model

python fineweb.py \
  --model_dim 768 \
  --n_layer 12 \
  --n_head 12 \
  --batch_size 4 \
  --block_size 1024 \
  --epochs 3

HellaSwag Few‑Shot

python hellaswag.py --lr 5e‑5 --epochs 5

From‑Scratch GPT‑2 Small Reproduction

python train_gpt2.py --config configs/gpt2_small.yaml

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Evaluation & Inference

# Perplexity on validation split
$ python eval.py --checkpoint ckpt/latest.pt --val data/fineweb/val.bin

# Interactive generation
$ python generate.py --checkpoint ckpt/best.pt --prompt "The meaning of life is"

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Results

Model	Corpus	PPL ↓	Training Time*
GPT‑2‑small (ours)	FineWeb 1B	23.7	7 h RTX 4090
GPT‑2‑small (ref)	OpenAI WebText	29.5	—

* Mixed‑precision, bf16. Your mileage may vary.

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Troubleshooting

Symptom	Likely Cause	Fix
CUDA OOM	batch too big	Lower batch_size or enable --grad‑ckpt
Diverging loss	LR too high	Try cosine scheduler, warmup steps, or lower --lr
Slow dataloading	HDD bottleneck	Store .bin on SSD or ramdisk

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Contributing

PRs welcome — whether it’s bug fixes, new training configs, or dataset loaders. Please run the test suite (pytest) and conform to black formatting.

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License

MIT © 2025 Duy Phúc Lê Nguyễn