Pretraining a GPT-Style Language Model

Imagine you want a program that can write fluent sentences. One option is to write rules for grammar and word choice by hand. Language is too varied for that approach to work well. A different approach is to let a model learn patterns from a large body of text and then use what it learned to generate new text. The process of building that learned starting point is called pretraining. It is the stage where a model is first trained on text before anyone asks it to do a specific job.

Keep that picture in mind as you read. Pretraining is not the finished product. It produces a base that later stages shape into something useful, and this article follows the path from raw data to a trained model that has been measured and judged.

The overall process of coding an LLM can be organized into three stages. The first stage is building the LLM itself, which corresponds to pretraining. The second stage fine-tunes the pretrained model so that it follows instructions. The third stage performs further fine-tuning for a specific task or for a set of preferences. Preference fine-tuning belongs to this third stage.

Read the diagram as three groups. The first group covers building the model: preparing data, implementing the attention mechanism and the overall architecture, pretraining, running the training loop, and evaluating the result. Its output is a foundation model. The second group starts from pretrained weights, combines them with an instruction dataset, and fine-tunes the model so that it becomes a personal assistant. The third group works from a dataset with class labels and produces a classifier. Preference fine-tuning is a refinement that sits in the third stage, where the model is further adjusted for specific goals.

Follow one model from start to finish and watch what each stage changes. At the start there is nothing useful: the model has only the structure you built and no learned behavior. Pretraining gives it a foundation that can generate new text. Instruction fine-tuning then shapes that foundation so it responds to requests. Preference fine-tuning is the last adjustment, nudging the model toward the answers a particular person or organization favors.

The course organizes its material into two broad stages. The first stage prepares the ingredients: the data preprocessing steps and the attention mechanism at the core of modern LLMs. The second stage puts those ingredients together, showing how to code and pretrain a GPT-like model that can generate new text, and then how to evaluate the result. Keeping these stages in mind helps you see why each topic exists.

There are two routes to a trained model. The first is to pretrain it yourself, starting from random initialization and running the training process on data. The second is to load openly available model weights that someone else already produced. The cost and purpose of these routes differ sharply, so the choice depends on what you need the model for.

Pretraining an LLM from scratch is a significant undertaking. The source states that it demands thousands to millions of dollars in computing costs for GPT-like models. That range is wide, and the practical lesson is that full-scale pretraining is a major commitment rather than a routine exercise. This cost is the reason the course narrows its goal: stage 2 implements training for educational purposes with a small dataset rather than pretraining at full scale.

Evaluation is essential for developing capable NLP systems. A trained model is a set of learned values, and those values alone do not tell you whether the model produces good text. Evaluation measures what the model does, so that you can compare versions and decide what to change.