Pretraining an LLM from Scratch

Most people first meet large language models as finished products: you type text in and a response comes out. The internals stay hidden. Building an LLM from scratch means doing the opposite. You implement the model step by step, starting from its basic components and ending with a working model whose behavior you can trace back to code you wrote yourself. This article explains what that build involves, why it teaches more than reading about the model does, and where learners commonly lose the benefit.

The phrase from scratch is the important part. A prebuilt system hides its components behind a single call, so you can use the model without knowing how any piece of it works. A from-scratch build removes that hiding place. Each component is something you wrote, so when the model behaves a certain way, you can point to the code responsible for that behavior.

Before looking at individual stages, it helps to see the overall order. A from-scratch build starts with basic components. Within the implementation, two training functions carry the model from raw capability to a more specific behavior: pretraining and fine-tuning. Pretraining comes first. The model is trained on broad data. Fine-tuning comes after and adjusts the model for more specific behavior. The diagram below shows this sequence as a chain of stages, each one handing its result to the next.

Read the diagram as a question you can answer at each arrow: what does this stage receive, and what does it hand to the next stage? If you cannot answer that for a stage, you have not yet understood it, no matter how many steps you have ticked off.

Pretraining is the stage where the model is first trained on broad data. Writing this function yourself means the training stage is visible in your own code rather than hidden inside a library call. You decide what the function receives, what it returns, and how its result feeds into the next step.

Training a model is usually a repeated loop rather than a single action. The loop shown below is the general pattern: the model makes a prediction for the next token, the error of that prediction is measured, the weights are updated to reduce the error, and the cycle repeats. Each pass changes the weights a little, so the model's behavior shifts gradually over many repetitions.

The reason to build this loop yourself is that a concept which looks clear in a diagram becomes concrete only when you write the code that performs each step. Once you have done that, you can check whether the weights really change after each pass and whether the error really falls. Those checks are part of the build, not an optional extra.

Fine-tuning is the second training stage in the build. After pretraining has trained the model on broad data, fine-tuning adjusts it for more specific behavior. As with pretraining, writing this function yourself keeps the adjustment visible in your code. You can see exactly which data it receives and how it changes what the model does.

A diagram or a written description of an LLM gives you a summary of how the parts relate. A from-scratch build forces you to make each relationship concrete. Every part has to be written, connected, and run before it does anything. That requirement is why a hands-on build is described as the most effective way to gain a deep understanding of how LLMs work. The table below compares the two approaches on what you control, what you can inspect, and what stays hidden.

The benefit also extends past the project. The insights come from making the pieces work together, so they carry over when you read other LLM material, debug a training run, or decide how to adapt a model to a new task.