Fine-tuning is a machine learning technique that adapts a pre-trained model to a specific task or domain. Instead of training a model from scratch, fine-tuning uses a pre-trained model as a starting point and further trains it on a smaller, task-specific dataset. This process can improve the model's performance on specialized tasks, reduce computational costs, and broaden its applicability across various fields. The goal of fine-tuning can be knowledge injection or alignment, or both. Fine-tuning is often used in natural language processing. There are many ways to approach fine-tuning, including supervised fine-tuning, few-shot learning, transfer learning, and domain-specific fine-tuning ...