Fine-tuning

Fine-tuning is a machine learning technique that takes a pre-trained model and adapts it to perform better on specific tasks or domains by training it further on a smaller, specialized dataset. This approach leverages the general knowledge already learned by the base model while customizing it for particular use cases. The fine-tuning process begins with a pre-trained model that has already learned broad patterns from large amounts of data. Rather than training from scratch, which requires enormous computational resources and data, fine-tuning starts with these learned weights and adjusts them using task-specific data. This is much more efficient and often produces better results than training from scratch with limited data. Fine-tuning is particularly valuable when you have domain-specific requirements, such as training a model to understand medical terminology, legal documents, or industry-specific jargon. It can also be used to adapt a model's behavior, tone, or output format to match specific requirements. The technique requires careful attention to hyperparameters like learning rate to avoid catastrophic forgetting, where the model loses previously learned knowledge. Fine-tuning has become a standard practice in modern AI development, enabling organizations to create specialized models without the massive computational costs of training from scratch.