Parameters
The adjustable numbers inside an AI model that are tuned during training and store what it has learned.
Parameters are the numbers inside a neural network that are adjusted during training. Each one is small and means little on its own, but together, in the billions or more, they hold what the model has learned. A common comparison is the settings on a vast mixing desk, all tuned until the output sounds right.
When people say a model has “70 billion parameters” they are describing its size. Larger models have generally been more capable, although the link is not exact. A well-trained smaller model can beat a larger, poorly trained one, and newer designs can use only part of their parameters for each answer.
For that reason, parameter counts are a rough guide only. They also say little about how a model behaves, and the companies behind several of the best-known systems do not publish them. Do not confuse parameters with the training data, which is the material the model learns from, or with the settings a user can change in an app, which are sometimes also called parameters in everyday talk.