Parameters

Executive Definition: Parameters — The internal variables, including weights and biases, that a machine learning model iteratively adjusts and learns from training data to minimize error and make predictions.

In neural networks, parameters represent the connections between neurons. A model’s scale is typically measured by its parameter count; for example, a 7B model contains 7 billion parameters. While increasing the number of parameters generally increases the model’s theoretical capacity, it also demands proportionally more memory and computational resources for both training and inference.

Frequently Asked Questions

Does a higher parameter count guarantee a better model?

No. While higher parameter counts increase the model’s capacity to learn complex patterns, overall performance heavily depends on the quality of the training data and the model architecture.

How do parameters impact computational cost?

Models with more parameters require greater memory to store and more computational power to train and run, which directly increases hardware costs and inference latency.

Can a model’s size be reduced after training?

Yes. Techniques like quantization reduce the precision of the parameters (e.g., from 32-bit floats to 8-bit integers), and knowledge distillation trains a smaller model to replicate the outputs of a larger one.