Hyperparameters in LLMs define key settings for the model’s architecture and training process, significantly impacting performance and efficiency. Architectural hyperparameters, such as the number of layers, attention heads, and hidden dimensions, determine the model's capacity to learn complex patterns. For example, increasing the number of layers enhances the model's ability to capture deeper relationships but also raises computational requirements.
Training hyperparameters, like learning rate, batch size, and dropout rate, control how the model learns from data. The learning rate governs the speed of parameter updates, while dropout prevents overfitting by randomly omitting parts of the network during training. Proper tuning of these parameters ensures stable and efficient training.
In inference, task-specific hyperparameters like temperature and max tokens influence the model’s output behavior. Developers use techniques like grid search or Bayesian optimization to identify the best hyperparameter combinations, optimizing the model for specific applications.