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

Generative Pre-trained Transformer (GPT) has gained a lot of attention in the field of natural language processing. It is a state-of-the-art language model that has been pre-trained on a large corpus of text data and can generate human-like text. In this article, we will explore how to build a basic version of GPT from scratch using code.

Understanding GPT

GPT is based on the Transformer architecture, which uses self-attention mechanisms to process input sequences and generate output sequences. The model consists of multiple layers of self-attention and feedforward neural networks, allowing it to capture long-range dependencies in the input data.

Building GPT from Scratch

To build a basic version of GPT from scratch, we will need to implement the core components of the Transformer architecture, such as self-attention layers, positional encoding, and feedforward networks. We will also need to pre-train the model on a large text corpus to capture the underlying structure of natural language.

Self-attention Layers

The self-attention mechanism allows the model to assign different weights to different parts of the input sequence, capturing the relationships between words and capturing context. We will need to implement the self-attention layers using matrix multiplications and softmax operations.

Positional Encoding

Since the Transformer architecture does not have any inherent notion of positional information, we will need to add positional encoding to the input sequences to preserve the order of words in the input data.

Feedforward Networks

The feedforward networks in the Transformer architecture are responsible for capturing non-linear relationships within the input data. We will need to implement these networks using activation functions and weight matrices.

Pre-training the Model

After building the core components of GPT, we will need to pre-train the model on a large text corpus. This involves feeding the model with input sequences and training it to predict the next word in the sequence. This will allow the model to learn the underlying structure of natural language and generate human-like text.

Conclusion

Building GPT from scratch can be a challenging but rewarding task. By implementing the core components of the Transformer architecture and pre-training the model on a large text corpus, we can create a basic version of GPT that is capable of generating human-like text.