Generating Text with a GPT Model
max_new_tokens controls how much new text is added, while context_size controls how much of the sequence is considered at once. Neither setting changes the weights; both shape the run that tests them.
Predicting a Healthy Output
Once a pretrained GPT model has been loaded, a natural question follows: did the weights actually arrive in the model, or is the model holding values that do not match what was trained? Reading the loading code does not settle this. The loading code might run without errors and still leave the model in a state that is not useful. A direct test is more convincing than an assumption.
The test is simple to describe. You give the model a short piece of text, ask it to continue that text by generating new tokens, and then read what it produced. Before you look at any executed result, decide what a healthy model should produce when it is handed the prompt 'Every effort moves you'. Keep your answer in mind, because the comparison between your expectation and the real output is the point of the exercise.
What do you think happens?
What should a correctly loaded model produce when given the prompt 'Every effort moves you'?
Reveal answer
A continuation that reads as ordinary, sensible English, beginning with the original prompt.
The source reports that the printed output is the original prompt followed by a sentence that reads as ordinary English. Coherent language is the signal that the verification treats as meaningful.
Why Coherent Text Is a Useful Signal
A GPT model predicts the next token from the tokens it has already seen. The weights determine how those predictions are made. When the weights have been loaded correctly, the predictions tend to produce sensible language, so a continuation that reads as ordinary English is a sign that the weights are doing what they should.
The reasoning runs in one direction. Correct weights lead to coherent text. Coherent text is therefore evidence that the weights were loaded correctly. The source is careful about the word evidence: coherent output is a strong indicator, not a guarantee. A model could produce fluent text for reasons unrelated to the check you intended, so the result should be read as support for a conclusion rather than as a full proof.
The Four-Stage Verification Pipeline
The verification script is a chain of four operations, and each arrow in the chain is a handoff. The text is not what the model works on directly. The model receives numbers and returns numbers. Only the final step turns those numbers back into words.
- 1Convert the prompt text into token IDs with
text_to_token_ids, using the tokenizer. - 2Pass those token IDs to
generate_text_simpletogether with the loaded model. - 3Receive a new sequence of token IDs that includes the original prompt followed by the generated tokens.
- 4Convert that sequence back into readable text with
token_ids_to_textso it can be printed.
Tracing One Run Through the Stages
Following the Prompt 'Every effort moves you'
Trace what happens to the prompt from the moment it is given to the model until the continuation is printed.
- 1Stage 1: text to token IDs
text_to_token_idsuses the tokenizer to convert the prompt string into a list of token IDs. From this point on, the model is working with numbers rather than words. - 2Stage 2: generation
generate_text_simplereceives those token IDs together with the loaded model and generates new tokens after the prompt. - 3Stage 3: returned sequence
The function returns a new sequence of token IDs. It contains the original prompt IDs followed by the generated IDs, so the prompt is not discarded.
- 4Stage 4: back to text
token_ids_to_textconverts the whole sequence into readable text. The printed result is the prompt followed by the continuation.
The printed output is the original prompt followed by a sentence that reads as ordinary English. Because the decoding step is the last stage, any garbling seen at this point could have begun at an earlier handoff.
Every effort moves you [a continuation of new generated words]- The prompt is converted to token IDs before the model sees it.
max_new_tokens=15 limits how many new tokens are added after the prompt.context_sizecomes from the configured context length and sets how much of the sequence is considered at once.- The decoded output includes the original prompt, because the returned sequence starts with the prompt IDs.
Settings That Shape the Run
The call to generate_text_simple includes two settings that shape how the run behaves. The max_new_tokens argument is set to 15, which limits how many new tokens are added after the prompt. The context_size argument is taken from the configuration value for context length, which tells the function how much of the sequence to consider at once.
max_new_tokens controls how much new text is added, while context_size controls how much of the sequence is considered at once. Neither setting changes the weights; both shape the run that tests them.
Locating Where a Failure First Appears
Suppose the printed text is not coherent. The pipeline has several stages, so the useful question is which stage first produced a result that differs from what you expected. The flow below treats the coherence check as the gate. A passing result means the verification succeeded. A failing result sends you back to the loading step, because coherent output is tied to correctly loaded weights.
The flow is a starting point, not a full diagnosis. Within the pipeline you can ask whether the prompt was converted to token IDs as expected, whether the function received the model you intended, and whether the decoded output matches what the function returned. Narrowing the location of the first mismatch is usually faster than changing several things at once.
Common mistake Assuming the loading code is correct because it ran without errors
Why it failsThe source notes that loading code can run without errors and still leave the model in a state that is not useful.
Do this insteadTest the model directly by generating text and reading the output.
Common mistake Treating coherent output as complete proof that everything is correct
Why it failsCoherent text is evidence that the weights were loaded correctly, not complete proof.
Do this insteadRead a passing check as strong support, and keep verifying when stakes are high.
Common mistake Changing several stages at once when the output is wrong
Why it failsMultiple simultaneous changes make it impossible to tell which stage caused the mismatch.
Do this insteadFind the first stage whose result differs from expectation and fix only that stage.
Common mistake Blaming the final printed text when the problem began earlier
Why it failsEach handoff passes numbers, so a failure can appear as garbled text at the end even though it started earlier in the chain.
Do this insteadWork backward through the stages, starting from the loading step if the continuation is not coherent.
Practice Tracing the Pipeline
A verification run prints text that is not coherent. Using the four-stage pipeline, name the first checkpoint you would examine, and explain which earlier stages you would consider already ruled out by that choice. Then state what the first checkpoint you examine would tell you about the next one.
Hints
- Recall that the model only receives and returns token IDs.
- The source ties coherent output to correctly loaded weights, so the loading step is the starting point when the continuation is not coherent.
- Consider which stages come before the first mismatch you find.
Key Takeaways
- Generating text from a prompt such as 'Every effort moves you' tests whether the model weights were loaded correctly, which reading the loading code cannot confirm.
- Coherent, sensible English continuation is evidence that the weights are doing their job, but it is not complete proof.
- The verification runs as four stages: text to token IDs, generation with the loaded model, a returned sequence of token IDs, and decoding back to text.
- When the output is wrong, find the first stage whose result differs from expectation, starting from the loading step if the continuation is not coherent.