Mini toolsDefinition
The total number of trainable variables in a neural network model. This metric indicates model size and computational requirements.
Detailed Explanation
Parameter count represents the total number of learned variables in a neural network including weights and biases across all layers. It's a key metric for model complexity and computational requirements. The parameter count affects model capacity training time memory usage and inference speed. Modern large language models can have billions or trillions of parameters.
Use Cases
Model design Resource planning Deployment optimization Architecture selection
