LLM hyperparameters are configurations set before training begins and influence how the model learns. Key hyperparameters include:
Learning rate: Controls how much to adjust model weights per update.
Batch size: Number of training samples used in one iteration.
Sequence length: Maximum token length for inputs.
Number of epochs: Full passes through the training data.
Dropout rate: Prevents overfitting by randomly dropping neurons during training. Tuning these parameters is critical for optimizing model performance.