Text Generation in GPT Models

A GPT model is a large language model, and its internal design is built from a repeated unit called the transformer block. When people say a GPT model has many layers, they are pointing at this repetition. Before looking inside a single block, it helps to ask a concrete question: if a model is made of one repeated unit, what does that unit actually do to the data passing through it?

This article answers that question in stages. First you will look at what one transformer block contains. Then you will see how many of these blocks are stacked to form a GPT model, how text is produced from the model's output tensors, and why training is needed before the output becomes coherent.

The definition names two parts. Data passes through these parts in order within the block. Think of the block as a pipeline with two stations: the input tensor arrives at the attention station first, and its output then moves on to the feed-forward station, which produces the output tensor of the block.

The diagram traces one block from input tensor to output tensor. The attention module is the first sub-module the data meets, and the feed-forward network that uses GELU is the second. Because the order is fixed, each block always transforms its input the same way: attention first, then the feed-forward step.

A GPT model is an LLM with many repeated transformer blocks. Each block has the same two-part structure described above, and the model is formed by repeating that structure many times. The source describes these blocks as having millions to billions of parameters, which means the parameters of a GPT model are spread across this repeated structure rather than living in one large monolithic unit.

The pipeline shows the overall flow. Input context enters the repeated transformer blocks, the blocks produce output tensors, and those tensors are decoded into text. The repeated blocks sit in the middle of the pipeline, so their outputs are what the decoding step works from. Depth in a GPT model therefore means more repetitions of the same unit, not a different design at each level.

GPT models also come in various sizes, measured by their number of parameters. The source gives four example sizes, and it notes that all of them can be implemented with the same GPTModel Python class. The size of a model is therefore set by configuration rather than by writing a different class for each size.

Text generation is the step that turns numbers into language. The source describes the capability as decoding output tensors into human-readable text by sequentially predicting one token at a time from a given input context. Output tensors are numeric, so generation is what converts those numbers into words a person can read.

The key phrase is one token at a time. The model does not produce a whole sentence in a single step. It predicts one token from the input context, that token is decoded, and the process then repeats. The process is sequential, and each step depends on the context that the previous steps built up.

Notice what the loop does with its own output. Each decoded token becomes part of the context for the next prediction. This is why the sequential nature matters: the model works forward one step at a time, and the text it produces is always the accumulated result of those steps.

Having the right structure is not enough for useful output. The source states that without training, a GPT model generates incoherent text, which underscores the importance of model training for coherent text generation. The architecture defines how data flows through the blocks, and the stack of blocks produces output tensors, but training is what makes the generated text coherent.