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Fine-tuning refers to the process of adjusting certain parameters or weights of a pre-trained model in order to improve its performance or effectiveness. It is commonly used in the field of deep learning, where a pre-trained model is trained on new data to adapt it to specific tasks. This process is known as transfer learning. Fine-tuning allows for better results than prompting and enables training on a larger and more diverse dataset. In theoretical physics, fine-tuning refers to the precise adjustment of model parameters to fit observations. It involves making small alterations or revisions to improve the functionality or performance of a system, such as fine-tuning a TV set to optimize reception. The concept of fine-tuning also extends to technological devices, where the functionality relies on the precise arrangement, shape, and properties of their constituents. Overall, fine-tuning involves making precise adjustments to achieve optimal performance or effectiveness in various contexts.