Generating text with generate_text_simple

Training a language model produces a stream of numbers, and those numbers are useful. They tell you how training is progressing, and they make it easy to compare one stage of training with another. But a number does not show what kind of text the model is actually producing. Two very different sets of outputs can produce estimates that look similar on a chart, so a number on its own leaves a gap.

The helper function generate_and_print_sample closes that gap. It takes a short piece of starting text, asks the model to continue it, and prints the continuation so that a person can read it and judge it. In this article you will follow the function step by step, keeping track of what it does to the model, to the input, and to the output at each stage.

The function has four stages that matter for understanding its behavior: preparing the input, generating new tokens, turning tokens back into text, and printing. Each stage hands its result to the next stage. This means an error in one stage does not stay in that stage. It shows up later as a strange result in the printed text, which is why it helps to know the order in which the stages run.

The function takes a starting text and encodes it into token IDs. Those IDs are what the model actually reads. The model then asks for a continuation of up to 50 new tokens. Once generation is finished, the resulting token IDs are decoded back into readable text, and that text is printed.

Read the diagram from left to right. Notice that the text changes form twice: it becomes token IDs before generation and becomes readable text again after generation. The model only ever works with the token form in the middle of the pipeline, so the printed sample is always the product of a round trip through encoding and decoding.

A model that is being trained is in training mode. The function does not leave the model in that state while it generates text. Before generating, it switches the model to evaluation mode, and before returning, it switches the model back to training mode. Because of this, the training process that surrounds the call continues as it was before the sample was printed.

The first stage prepares the input. The starting text is encoded into token IDs, and these IDs are the form the model works with. The output of generation is also a set of token IDs. Only at the end of the process are those IDs turned back into text that a person can read.

This round trip matters for debugging. A sample that looks odd may have been caused by how the input was encoded, by what the model generated, or by how the generated IDs were decoded. Knowing that these conversions exist is the first step toward locating the stage where the result went wrong.

The generation stage asks the model to continue the encoded input with up to 50 new tokens. The continuation is built one token at a time. After each new token is produced, it is added to the sequence, and the extended sequence is what the model sees when it produces the next token. The loop stops once the 50-token limit is reached.

The limit is a ceiling, not a target. Generation stops at up to 50 new tokens, so the printed continuation is bounded in length. When you read a sample, the continuation is the text that the model added after your starting text, and it is that continuation you should judge.

The function is designed to sit beside evaluate_model rather than replace it. The evaluation helper produces a numeric estimate of training progress, which is convenient for comparing one training stage with another. The generated sample answers a different question. A number says how the model is scoring, and the sample says what the model is writing.

Used together, the two signals give a fuller picture. The number tells you the trend, and the sample tells you whether the text behind that trend reads the way you expected.