parameter

A parameter is a learned internal value, such as a weight or bias, in a model that determines how inputs are mapped to outputs.

Parameters are initialized before training and updated from data via optimisation, such as gradient descent and back-propagation. They’re typically represented as tensors and may be fully trainable, frozen during fine-tuning, or modified via parameter-efficient adapters.

Unlike hyperparameters, which configure the model or training process but are not learned from data, parameters are learned.