Let's build GPT: from scratch, in code, spelled out.

Introduction

Introduction

In recent years, GPT (Generative Pre-trained Transformer) has made significant waves in the AI community and beyond, with its capability to understand and generate human-like text. In this article, we will take a deep dive into creating a simplified version of a GPT model from scratch, utilizing the Transformer architecture as proposed in the landmark paper "Attention Is All You Need."

We will base our model on a basic dataset, the Tiny Shakespeare dataset, which consists of lines from Shakespeare's works. This hands-on programming guide will help clarify how the inner workings of GPT function, showcasing important concepts like tokenization, attention mechanisms, and layer normalization.

Understanding the Basics

What is GPT?

GPT stands for Generative Pre-trained Transformer. It is a language model that focuses on generating text that resembles human writing. The model's structure is built upon the Transformer architecture, which is adept at processing sequences of data (like text) and understanding context through attention mechanisms.

The Tiny Shakespeare Dataset

The initial dataset we'll use is the Tiny Shakespeare dataset, a small compilation of Shakespeare's works. This dataset will help us build a character-level language model, which predicts the next character based on a sequence of input characters.

Tokenization

Tokenization is a critical step in preparing text for processing. In this case, we will implement a character-level tokenizer that converts individual characters into numerical format—essentially mapping characters to integers for easier processing.

Building the Transformer Model

Underlying Neural Architecture

At the core of the GPT model lies the Transformer architecture. The key breakthrough here is the attention mechanism, which allows the model to weigh the importance of different words based on their context.

The Transformer consists of several components:

• Self-Attention Mechanism: This allows tokens to attend to each other based on their relationships.
• Feed Forward Networks: These perform additional transformations on the output of the self-attention.
• Layer Normalization: This normalizes the output to stabilize the learning process.

Code Implementation

The code implementation entails various steps:

1. Import Libraries: Use PyTorch as our framework for building and training the model.
2. Prepare Data: Load the Tiny Shakespeare dataset and tokenize it, converting characters to their respective integers.
3. Train the Model: Define the architecture of the Transformer model using classes for the attention block, feed forward layers, and normalization.
4. Generate Text: After training the model, generate new text that resembles Shakespeare's writing by predicting characters sequentially.

Sample Code Structure

Here’s a simplified breakdown of the code structure we follow:

## Introduction
import torch
import torch.nn as nn
import torch.optim as optim

## Introduction
class GPT(nn.Module):
    ...
    # Implement methods for tokenization, attention mechanism, training, etc.

Training and Generating Text

After we finish training the model, we can assess its capability by generating text based on given input characters. The generated output will give us an idea of how well the model learned the patterns in Shakespeare's writing.

Fine-tuning Practices

With a working model, we can explore additional fine-tuning practices, like reinforcement learning from human feedback (RLHF), which is often involved in creating advanced models like ChatGPT.

Conclusion

In this article, we have embarked on a journey to build a simplified version of the GPT model from scratch. By unraveling the underlying components of the Transformer architecture and understanding various key concepts, we have gleaned insights into how large-scale language models like GPT operate. While we created a basic model, the principles apply widely across different NLP applications.

---

Keywords

• GPT
• Transformer
• Tiny Shakespeare
• Tokenization
• Self-Attention
• Feed Forward Networks
• Layer Normalization
• Reinforcement Learning

---

FAQ

1. What is GPT?

   • GPT stands for Generative Pre-trained Transformer, a model designed to generate text based on learned structures and patterns from large datasets.

2. What is the Tiny Shakespeare dataset?

   • It is a small dataset containing lines from Shakespeare's works, often used for text generation tasks.

3. What is tokenization?

   • Tokenization is the process of converting text into numerical representations, which can be processed by machine learning models.

4. What are the main components of the Transformer architecture?

   • The main components include self-attention mechanisms, feed-forward networks, and layer normalization.

5. How is fine-tuning done in models like GPT?

   • Fine-tuning can involve further training on specific datasets, coupled with methods like reinforcement learning from human feedback to align model responses with desired outcomes.