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When fine-tuning a language model, choosing the right hyperparameters is crucial for achieving optimal performance. Hyperparameters control various aspects of the training process including learning speed, model stability, and final performance. Each task type has its own set of hyperparameters that can be tuned. You can find all of the available hyperparameters for each task type in the Fine-Tuning Configuration Reference. Don’t see a hyperparameter you need for any of the above task types? Contact us and we’ll add it. For guidance on choosing and tuning hyperparameters effectively, see our Hyperparameter Tuning Best Practices Guide.