Building Transformer Models from Scratch with PyTorch

A new 10-day mini-course is available that teaches participants how to build and train transformer models using PyTorch. The course emphasizes understanding the architecture and functionality of large language models, which can mimic human-like intelligence. Although creating a full-scale clone of existing large language models is deemed unrealistic, the course aims to provide insights into their workings and limitations.

This project-based guide targets developers with minimal experience, ensuring they can progress from basic knowledge to a confident understanding of transformer model creation. The course is divided into ten parts, with each lesson designed to take approximately 30 minutes. Participants are encouraged to follow a schedule of one lesson per day to fully absorb the material.

Throughout the mini-course, learners will explore various topics related to transformer architecture. The lessons will cover aspects such as data collection, preprocessing, and tokenization, including how to convert text into a numerical format for processing in language models. One of the popular methods for tokenization, Byte-pair encoding (BPE), will also be discussed, along with its implementation in the tokenizer library.

Participants will learn about the importance of collecting diverse text data and how to preprocess it to create a suitable dataset for training a language model. The course highlights that while the initial text collection may seem small, it serves as a valuable starting point for understanding language modeling.

The course aims to demystify transformer models, allowing participants to see beyond the black box of artificial intelligence. By the end of the course, learners will have a comprehensive understanding of how transformer models operate and the foundational skills needed to build their own models.