Fine Tune Model

Fine-tuning is the process of taking a pre-trained model and continuing its training on a smaller, domain-specific dataset. This targeted dataset is usually more focused and tailored to a particular task or application. The primary goal of fine-tuning is to adapt the model to perform more effectively within specific contexts, especially in scenarios that were not extensively addressed during the original pre-training phase. Rather than expanding the model’s general knowledge, fine-tuning enhances its performance and accuracy for specialized use cases.