Parameter-Efficient Fine-Tuning (PEFT)
AI Infrastructure
The total number of trainable variables in a neural network model. This metric indicates model size and computational requirements.
Detailed Explanation
Parameter Count refers to the total number of trainable variables, such as weights and biases, within a neural network model. It serves as a vital measure of the model's size, complexity, and computational demands. A higher parameter count generally indicates a more expressive model but requires increased memory, processing power, and training time, affecting scalability and deployment considerations.
Use Cases
- •Assess model scalability by comparing parameter counts to ensure deployment feasibility within resource constraints.
Related Terms
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