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title GPT Open Source - Deep Dive Tutorial
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GPT Open Source: Deep Dive Tutorial

A comprehensive guide to understanding, building, and deploying open-source GPT implementations -- from nanoGPT to GPT-NeoX and beyond.

Open-Source GPT Architectures and Implementations

nanoGPT GPT-NeoX minGPT

Why This Track Matters

GPT Open Source is increasingly relevant for developers working with modern AI/ML infrastructure. A comprehensive guide to understanding, building, and deploying open-source GPT implementations -- from nanoGPT to GPT-NeoX and beyond, and this track helps you understand the architecture, key patterns, and production considerations.

This track focuses on:

  • understanding getting started -- understan
  • understanding transformer archite
  • understanding tokenization & emb
  • understanding training pipeline -- data

What You Will Learn

This tutorial provides a deep dive into the open-source GPT ecosystem. You will learn how GPT models work at every level -- from raw transformer math to production-scale inference optimization. Whether you are training a small character-level model with nanoGPT or deploying a billion-parameter model with GPT-NeoX, this guide has you covered.

The Open-Source GPT Landscape

Project Parameters Purpose Language
nanoGPT ~124M Educational, minimal GPT-2 reproduction Python/PyTorch
minGPT ~124M Clean, readable GPT implementation Python/PyTorch
GPT-J 6B Open alternative to GPT-3 JAX/PyTorch
GPT-NeoX 20B Large-scale training framework Python/PyTorch
GPT-Neo 1.3B-2.7B First open GPT-3 replication effort Python/TensorFlow
Cerebras-GPT 111M-13B Compute-optimal GPT models Python/PyTorch
OpenLLaMA 3B-13B Open reproduction of LLaMA Python/PyTorch

Mental Model

flowchart TD
    A[Open-Source GPT Ecosystem] --> B[Educational]
    A --> C[Research]
    A --> D[Production]

    B --> B1[nanoGPT]
    B --> B2[minGPT]
    B --> B3[x-transformers]

    C --> C1[GPT-J / 6B]
    C --> C2[GPT-NeoX / 20B]
    C --> C3[GPT-Neo / 2.7B]

    D --> D1[vLLM Serving]
    D --> D2[TensorRT-LLM]
    D --> D3[ONNX Runtime]

    classDef edu fill:#e8f5e9,stroke:#2e7d32
    classDef research fill:#e3f2fd,stroke:#1565c0
    classDef prod fill:#fff3e0,stroke:#ef6c00

    class B1,B2,B3 edu
    class C1,C2,C3 research
    class D1,D2,D3 prod
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Current Snapshot (auto-updated)

Chapter Guide

This tutorial is organized into 8 chapters that progressively build your understanding:

Chapter Title What You Will Learn
Chapter 1 Getting Started Open-source GPT landscape, nanoGPT setup, first training run
Chapter 2 Transformer Architecture Self-attention, multi-head attention, feed-forward networks
Chapter 3 Tokenization & Embeddings BPE, vocabulary construction, positional encodings
Chapter 4 Training Pipeline Data loading, loss computation, gradient accumulation, mixed precision
Chapter 5 Attention Mechanisms Causal masking, KV-cache, multi-query attention, Flash Attention
Chapter 6 Scaling & Distributed Training Model parallelism, data parallelism, ZeRO, FSDP
Chapter 7 Fine-Tuning & Alignment LoRA, QLoRA, RLHF, DPO, instruction tuning
Chapter 8 Production Inference Quantization, batching, speculative decoding, deployment

Prerequisites

Before starting this tutorial, you should have:

  • Python 3.8+ with a working PyTorch installation
  • Basic understanding of neural networks and backpropagation
  • GPU access (recommended): NVIDIA GPU with CUDA support, or cloud GPU instance
  • Familiarity with the command line and git
# Recommended environment setup
conda create -n gpt-oss python=3.10
conda activate gpt-oss
pip install torch torchvision torchaudio
pip install transformers datasets tiktoken wandb

Quick Start

Clone nanoGPT and run your first training:

git clone https://github.com/karpathy/nanoGPT.git
cd nanoGPT
pip install -r requirements.txt

# Prepare Shakespeare dataset
python data/shakespeare_char/prepare.py

# Train a small character-level model
python train.py config/train_shakespeare_char.py

Who This Tutorial Is For

  • ML Engineers wanting to understand GPT internals beyond API calls
  • Researchers exploring transformer architectures and training strategies
  • Students looking for a hands-on path from theory to implementation
  • Practitioners who need to fine-tune or deploy open-source GPT models

Source References

Ready to begin? Start with Chapter 1: Getting Started.

Built with insights from open-source GPT implementations.

Related Tutorials

Navigation & Backlinks

Full Chapter Map

  1. Chapter 1: Getting Started -- Understanding the Open-Source GPT Landscape
  2. Chapter 2: Transformer Architecture -- Self-Attention, Multi-Head Attention, and Feed-Forward Networks
  3. Chapter 3: Tokenization & Embeddings -- BPE, Vocabulary Construction, and Positional Encodings
  4. Chapter 4: Training Pipeline -- Data Loading, Loss Computation, Gradient Accumulation, and Mixed Precision
  5. Chapter 5: Attention Mechanisms -- Causal Masking, KV-Cache, Multi-Query Attention, and Flash Attention
  6. Chapter 6: Scaling & Distributed Training -- Model Parallelism, Data Parallelism, ZeRO, and FSDP
  7. Chapter 7: Fine-Tuning & Alignment -- LoRA, QLoRA, RLHF, DPO, and Instruction Tuning
  8. Chapter 8: Production Inference -- Quantization, Batching, Speculative Decoding, and Deployment

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