# minGPT — Minimal PyTorch GPT Implementation for Learning

> minGPT by Andrej Karpathy is a clean, readable re-implementation of GPT in about 300 lines of PyTorch, designed for educational use and as a starting point for GPT-based research experiments.

## Install

Save as a script file and run:

# minGPT — Minimal PyTorch GPT Implementation for Learning

## Quick Use
```bash
git clone https://github.com/karpathy/minGPT.git
cd minGPT
pip install torch
python demo.py
```

## Introduction
minGPT is a minimal re-implementation of the GPT architecture in PyTorch by Andrej Karpathy. It strips away production complexity to expose the core transformer mechanics in clean, well-commented code, making it a go-to educational resource for understanding how GPT models work from the ground up.

## What minGPT Does
- Implements GPT-2 architecture in roughly 300 lines of PyTorch
- Supports training from scratch on custom text datasets
- Includes character-level and token-level language modeling demos
- Provides a clean reference for the transformer decoder stack
- Ships with example notebooks for sorting, math, and text generation

## Architecture Overview
minGPT implements a standard decoder-only transformer with causal self-attention, layer normalization, and a feedforward MLP block at each layer. The model class handles token and positional embeddings, the stack of transformer blocks, and the final language model head. Training logic is separated into a Trainer class that manages the optimization loop.

## Self-Hosting & Configuration
- Clone the repository and install PyTorch
- Configure model size (number of layers, heads, embedding dim) via a simple config dict
- Train on any text file with the included dataset utilities
- Adjust learning rate, batch size, and context length as needed
- Supports GPU training with standard PyTorch device placement

## Key Features
- Extremely readable codebase ideal for learning transformers
- Faithful GPT-2 architecture with no unnecessary abstractions
- Supports loading pre-trained GPT-2 weights from Hugging Face
- Includes interactive Jupyter notebooks with training demos
- Written by one of the original architects of modern deep learning education

## Comparison with Similar Tools
- **nanoGPT** — Karpathy's faster successor focused on training speed; minGPT prioritizes readability
- **Hugging Face Transformers** — production library with hundreds of models; minGPT is a single-model educational tool
- **GPT-2 (OpenAI)** — original TensorFlow implementation; minGPT is a clean PyTorch rewrite
- **x-transformers** — modular transformer library; minGPT is intentionally minimal

## FAQ
**Q: Can minGPT train large models?**
A: It can train small to medium GPT models. For large-scale training, nanoGPT or Hugging Face is more appropriate.

**Q: Does it support fine-tuning pre-trained models?**
A: Yes, it can load GPT-2 weights from Hugging Face and fine-tune on custom data.

**Q: What Python version is required?**
A: Python 3.7 or later with PyTorch 1.x or 2.x.

**Q: Is this suitable for production use?**
A: No, it is designed for education and experimentation. Use production frameworks for deployment.

## Sources
- https://github.com/karpathy/minGPT

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