What is finetuning?

Fine-tuning is a transfer learning technique where a pre-trained model (e.g., a large language model trained on a massive dataset) is further trained on a smaller, task-specific dataset. This allows the model to adapt its existing knowledge to the nuances of the new task, often leading to better performance and faster training compared to training a model from scratch. It is commonly used to customize general-purpose models for specific applications like sentiment analysis, text classification, or question answering.