LLMs From Scratch Tutorial 2026: Build ChatGPT-Like Models in PyTorch

What is LLMs From Scratch?

LLMs From Scratch is an open-source educational project that teaches you how to build large language models similar to ChatGPT from the ground up using PyTorch. Rather than treating LLMs as black boxes, this project breaks down the complete architecture—from tokenization to attention mechanisms to training loops—so you understand exactly how these systems work.

The problem it solves is clear: most developers know how to use LLMs through APIs, but few truly understand how they're built. This project eliminates that knowledge gap by providing step-by-step implementations with clear explanations, making it ideal for AI developers, researchers, and founders who want to move beyond API consumption to genuine model understanding.

Key Features

• Complete implementation — From tokenization and embeddings to multi-head attention and the full transformer architecture
• Interactive Jupyter notebooks — Run and modify code directly, experiment with different components
• Step-by-step progression — Start with fundamentals and build toward a full GPT-style model
• Production-focused — Learn about batch processing, efficient training, and practical optimization techniques
• Accompanying book — The Build a Large Language Model (From Scratch) book provides deeper context and theory
• PyTorch foundation — All code uses PyTorch, the framework of choice for most LLM research and development

Getting Started

Prerequisites

Before diving in, ensure you have:

• Python 3.8 or later
• Basic familiarity with PyTorch (or willingness to learn alongside)
• GPU access recommended (NVIDIA CUDA or Apple Silicon) for reasonable training times
• Jupyter Notebook or JupyterLab installed

Installation

Start by cloning the LLMs From Scratch repository:

git clone https://github.com/rasbt/LLMs-from-scratch.git
cd LLMs-from-scratch

Create a virtual environment and install dependencies:

python -m venv llm_env
source llm_env/bin/activate  # On Windows: llm_env\Scripts\activate
pip install --upgrade pip
pip install torch torchvision torchaudio
pip install jupyter numpy matplotlib

For GPU support on NVIDIA systems, adjust the PyTorch installation command based on your CUDA version. Check pytorch.org for the correct command.

Your First Notebook

Navigate to the notebooks directory and start Jupyter:

jupyter notebook

Open one of the starter notebooks. A typical first snippet will involve importing PyTorch and loading sample text data:

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset

# Check GPU availability
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")

# Load and examine sample data
with open('sample_text.txt', 'r', encoding='utf-8') as f:
    text = f.read()
print(f"Text length: {len(text)} characters")

From here, the notebooks guide you through tokenization, creating embeddings, implementing the attention mechanism, and assembling these into a transformer-based language model.

When to Use It

Use Case 1: Upskilling Your Team

If you're a technical founder or team lead and your developers primarily work with LLM APIs, this project is an excellent way to deepen organizational knowledge. Understanding transformer internals helps your team make better decisions about model selection, fine-tuning, and prompt engineering. A week of working through the notebooks can transform how your team approaches LLM integration.

Use Case 2: Building Custom or Fine-Tuned Models

When off-the-shelf models don't quite fit your needs—whether due to domain requirements, cost constraints, or latency demands—building from scratch becomes relevant. This project teaches you the exact patterns you'll use when customizing models for production. You'll understand attention patterns, layer configurations, and training strategies that let you adapt pre-trained models or train smaller ones efficiently.

Use Case 3: AI Research and Publication

Researchers proposing novel architectures or training methods need a solid foundation in LLM implementation. This project provides a clean, well-documented baseline. Rather than reinventing tokenization or basic training loops, you can focus on your novel contribution while grounding it in proven fundamentals.

Who It's Best For

LLMs From Scratch is ideal for:

• AI developers and engineers who want to move beyond API consumption
• ML researchers exploring novel architectures or training approaches
• Technical founders building AI-native products who need deep model understanding
• Educators teaching deep learning and NLP concepts
• Career changers entering AI with strong programming foundations

Takeaway

LLMs From Scratch closes a critical gap in AI education: the space between using LLMs and understanding how they work. By working through this project, you'll gain practical skills in PyTorch, transformer architecture, and model training that transfer directly to production work. The combination of well-organized notebooks, clear progression, and accompanying book resources makes it one of the most accessible ways to truly understand modern language models. If you're serious about AI development in 2026, investing a few weeks here will pay dividends.