nanogpt

Q: How do I install nanogpt?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill nanogpt in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nanogpt/ (personal) or .claude/skills/nanogpt/ (project).

Q: Which AI agents can use nanogpt?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Q: Is nanogpt safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Q: Who maintains nanogpt?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 961 recorded installs.

by orchestra-researchProductivity & Automation

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).

Installs: 961
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/orchestra-research/ai-research-skills --skill nanogpt

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads nanogpt automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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 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.

Hardware requirements

Shakespeare (char-level):
- CPU: 5 minutes
- GPU (T4): 1 minute
- VRAM: <1GB
GPT-2 (124M):
- 1× A100: ~1 week
- 8× A100: ~4 days
- VRAM: ~16GB per GPU
GPT-2 Medium (350M):
- 8× A100: ~2 weeks
- VRAM: ~40GB per GPU

Performance:

With compile=True: 2× speedup
With dtype=bfloat16: 50% memory reduction

Resources

GitHub: https://github.com/karpathy/nanoGPT ⭐ 48,000+
Video: "Let's build GPT" by Andrej Karpathy
Paper: "Attention is All You Need" (Vaswani et al.)
OpenWebText: https://huggingface.co/datasets/Skylion007/openwebtext
Educational: Best for understanding transformers from scratch

FAQ

What does the nanogpt skill do?

How do I install nanogpt?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill nanogpt in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nanogpt/ (personal) or .claude/skills/nanogpt/ (project).

Which AI agents can use nanogpt?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is nanogpt safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains nanogpt?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 961 recorded installs.