Tokenizers on the How to Make AI Models track. A tokenizer splits text into ids. If production text does not match the tokenizer's training, the model sees garbage pieces. New languages and code need a tokenizer that actually contains those pieces.
This lesson assumes you already worked through Decoders and generation.
The idea in practice
Lock the tokenizer with the model. Unknown tokens should be rare. Log the rate.
A concrete check
goal = {
'track': 'How to Make AI Models',
'lesson': 'Tokenizers',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
Changing the tokenizer without retraining. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Explain why the tokenizer and the weights must ship together.
Self-check
- Say Tokenizers in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.