Introduction to Building nanoGPT

In this tutorial, we will guide you through the process of building a smaller, more accessible version of a Generative Pre-trained Transformer (GPT), referred to as nanoGPT. This project is inspired by Andrej Karpathy’s work and aims to simplify the complex architecture of GPT models into manageable steps.

Getting Started

In this section, we look at how to prepare your environment for developing nanoGPT. We will begin by setting up the necessary tools and dependencies. First, ensure you have Python and PyTorch installed. If not, you can follow the installation instructions from the PyTorch website. Additionally, we will be using a small dataset called “Tiny Shakespeare” to train our model.

Understanding Tokenization and Encoding

Next, we delve into the basics of tokenization and encoding. In this section, we will explain how to convert text data into a format suitable for the model. You will learn how to create a vocabulary from the dataset, assign unique numerical values (tokens) to each character, and map these tokens back to characters for text generation.

Building the Embedding Layer

In this part, we look at constructing the embedding layer, a crucial component that converts tokens into dense vectors. These vectors capture the semantic meaning of the tokens, which the model uses for further processing. We will walk you through the implementation of a token embedding table and how to use it in our model.

Adding Positional Embeddings

Since our model needs to understand the order of tokens, we introduce positional embeddings. In this section, we will add positional information to our token embeddings to help the model recognize the sequence of tokens. You will learn how to implement and integrate positional embeddings into your model.

Implementing the Attention Mechanism

Here, we look at the core of the Transformer architecture: the attention mechanism. We will explain the concept of self-attention and how it allows the model to focus on relevant parts of the input sequence. You will learn how to implement a simple attention mechanism and see it in action.

Introducing Multi-Head Self-Attention

To improve the model’s ability to capture different aspects of the input data, we introduce multi-head self-attention. This section will guide you through the implementation of multiple attention heads and how they work together to enhance the model’s performance.

Building the Feedforward Neural Network

In this section, we look at adding a feedforward neural network to process the output of the attention mechanism. This network helps the model make sense of the information gathered during the attention phase. You will learn how to construct and integrate this network into your nanoGPT model.

Training and Evaluating nanoGPT

We will be looking at several variants of models. For each version of the model, we look at how to train and evaluate it.

By following this tutorial, you will gain a comprehensive understanding of the fundamental components of GPT models and how to implement them from scratch. Let’s get started on building your very own nanoGPT!