LLM From Scratch Guide

LLM From Scratch Guide

Overview

LLMs-from-scratch is a comprehensive educational repository with over 87,000 stars on GitHub that teaches you how to build a ChatGPT-like large language model from the ground up using PyTorch. Created by Sebastian Raschka, a machine learning researcher and author, the project provides a complete pipeline covering data preparation, tokenization, attention mechanisms, pretraining, and instruction finetuning.

Unlike tutorials that treat LLMs as black boxes, this project demystifies every component by walking through the full implementation. Each chapter corresponds to a Jupyter notebook with clear explanations, diagrams, and runnable code. The repository accompanies the book "Build a Large Language Model (From Scratch)" and serves as a standalone learning resource for researchers and engineers who want deep understanding of transformer-based language models.

The project is particularly valuable for academic researchers who need to understand the internals of LLMs for their own research, whether that involves modifying architectures, running ablation studies, or developing domain-specific language models for scientific applications.

Installation and Setup

Clone the repository and set up a Python environment with the required dependencies:

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

# Create a virtual environment
python -m venv llm-env
source llm-env/bin/activate

# Install dependencies
pip install -r requirements.txt

The project requires Python 3.10+ and PyTorch 2.0+. For GPU-accelerated training, ensure you have CUDA installed. The notebooks can also run on CPU for smaller model configurations, though training times will be significantly longer.

Key dependencies include:

• PyTorch >= 2.0 for model implementation and training
• tiktoken for BPE tokenization compatible with OpenAI models
• matplotlib for training visualization
• jupyter for interactive notebook execution

Core Learning Pipeline

The project is organized into sequential chapters that build on each other:

Chapter 1: Understanding Large Language Models

Covers the conceptual foundations of LLMs, including the transformer architecture, the difference between encoder and decoder models, and how pretraining and finetuning work at a high level.

Chapter 2: Working with Text Data

Implements text tokenization from scratch, including byte-pair encoding (BPE). You build a custom tokenizer and learn how text is converted to numerical representations:

# Tokenization example from the project
import tiktoken

tokenizer = tiktoken.get_encoding("gpt2")
text = "Large language models are fascinating."
token_ids = tokenizer.encode(text)
decoded = tokenizer.decode(token_ids)

Chapter 3: Coding Attention Mechanisms

Implements self-attention, multi-head attention, and causal (masked) attention from scratch. This is the core computational primitive of transformers:

# Simplified multi-head attention
class MultiHeadAttention(nn.Module):
    def __init__(self, d_in, d_out, context_length, num_heads, dropout=0.0):
        super().__init__()
        self.W_query = nn.Linear(d_in, d_out, bias=False)
        self.W_key = nn.Linear(d_in, d_out, bias=False)
        self.W_value = nn.Linear(d_in, d_out, bias=False)
        self.out_proj = nn.Linear(d_out, d_out)
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads

Chapter 4: Implementing a GPT Model

Assembles the full GPT architecture using the attention mechanism, layer normalization, feed-forward networks, and positional embeddings.

Chapter 5: Pretraining on Unlabeled Data

Trains the GPT model on a text corpus using next-token prediction. Covers the training loop, loss computation, learning rate scheduling, and gradient clipping.