Text Generation with GPT Model

Once pretrained weights are loaded into a GPTModel, the loading step gives you very little feedback. The call either finishes or raises an error, and nothing on the screen tells you whether the model now behaves the way the trained weights should make it behave. Before you spend effort fine-tuning anything, you need a fast way to confirm that the loaded model is in a usable state.

The check used in this course is simple. After the weights are loaded, the text generation utility from earlier chapters is reused, and the model is asked to continue a short prompt. If the continuation reads as coherent text, that is taken as evidence that the weights were loaded correctly. Everything in this article is about how that test works, what each of its pieces does, and how to read its result.

The test is a round trip. The prompt starts as a string. It is converted into token IDs by text_to_token_ids. Those IDs, together with the model, a max_new_tokens limit of 15, and a context_size taken from the configuration, are passed to generate_text_simple. That function returns a new list of token IDs. Finally, token_ids_to_text converts the returned list back into a string that can be printed.

Read the diagram as three stages with a clear hand-off between each. Text is converted into IDs on the way in. The model extends the IDs in the middle. The IDs are converted back into text on the way out. Each stage has its own inputs and outputs, so each one can be reasoned about on its own.

A key point is easy to miss: the model never sees the prompt as text. The text is turned into a list of integers before the model is called, and the model works only with those integers. This is why the walkthrough asks you to watch how the prompt changes shape at each step. The string at the start, the list of IDs in the middle, and the printed string at the end are three different representations of the same prompt.

Because the model's input is a list of integers, a problem in the conversion from text to IDs affects everything downstream, even when the model's weights are correct. A correct model given a badly encoded prompt can still produce poor output. This is one reason the stages must be inspected separately rather than judged only by the final printed result.

The result you check is the printed text. The source's run of this test, using the prompt 'Every effort moves you', produces a continuation in which the added words form the sentence 'The first step is to understand the importance of your work'. The source reads the output as coherent because the added words follow naturally from the prompt and form grammatical English.

Judging coherence is a human reading step. The test does not assign a score to the output. A continuation that is fluent and follows the prompt is the signal the test is looking for, and a continuation that is garbled, unrelated, or ungrammatical is the signal that something needs investigating.

When the printed text is not coherent, the round trip gives you a way to narrow the search. Because the test has distinct stages, you can ask a sequence of questions. Did the prompt encode into sensible IDs? Did generate_text_simple receive the model and the context size you intended? Did the decoded IDs come back as the text you expected? Each answer rules out part of the pipeline. The test does not name the faulty stage for you, but its structure tells you what to inspect.

The flowchart is a suggested order of inspection, not a fixed procedure. Start by checking how the prompt was encoded, because the model can only work with what it receives. Then confirm that generation received the model and context size you intended. Finally, confirm that the decoded IDs produce the text you expected. A failure at any of these points means the problem lies in that stage or in the inputs passed to it.