Building a GPT-Style Model from Transformer Blocks

Large language models can produce fluent, human-like text, but a model of this kind is not something you can understand by looking at it as one opaque object. The practice goal of this section is to code a GPT-like large language model that can be trained to generate human-like text. To make that goal manageable, the work is broken into smaller building parts, each with its own practice goal.

There are four practice goals. The first names the whole model: a GPT-like large language model that can be trained. The other three name pieces that support building and training a deep network: normalizing layer activations, adding shortcut connections, and implementing transformer blocks. Read these four goals as a checklist of what you should be able to do when the section is complete, rather than as four unrelated topics.

Start from the outside and trace what contains what. The GPT-like model sits at the top. It is built from transformer blocks. Normalization and shortcut connections are techniques that the block-level design relies on. The diagram below is a map of which parts belong to which. It is not a full wiring schematic, because the source for this section names these practices but does not, in this excerpt, specify the exact wiring inside each block.

Notice that the diagram separates the model from its techniques. The model is what you are building. The transformer block is the unit you assemble it from. Normalization and shortcut connections are not separate models; they are practices that make the block-based design work better when it is trained.

The source gives one purpose for normalizing layer activations: to stabilize neural network training. The point is not that normalization makes the model smarter on its own. The point is that the learning process behaves more predictably as the network is trained. When activation values are kept in a more consistent range from one stage to the next, the training process has less erratic behavior to deal with.

The third practice goal is adding shortcut connections in deep neural networks. The word to focus on is adding. A shortcut connection gives information a route that skips over part of the network. The signal entering a stage of the network can then be carried forward alongside what that stage computes.

This matters in a deep network because, without a shortcut, the signal would have to pass through every layer in sequence. A shortcut gives it a second route. The flowchart below shows the two paths and the point where they come back together.

The source includes a conceptual trace rather than a numeric calculation. The goal is to practice one habit: at each step, ask what state the data is in and which practice goal is responsible for that state. The steps below are generated for this article to practice that habit. They do not compute any numbers.

The source describes implementing transformer blocks to create GPT models of various sizes. The important idea is that a GPT-style model is assembled from transformer blocks, and the same kind of block can be used to produce models of different sizes. Size is therefore a matter of how the building unit is composed and repeated, not a separate design for each model.

The next step in the chapter is to examine attention itself. You now know where attention sits: inside a model built from transformer blocks, supported by normalization and shortcut connections, and oriented toward generating human-like text. Keeping that picture in mind will help you place attention within the whole model rather than studying it in isolation. Continue to ask, at each step, what state the data is in and which practice goal explains it.