Fine-Tuning vs Distillation vs Transfer Learning: What’s The Difference?

Fine-tuning, distillation, and transfer learning are different techniques used in training AI models, including large language models (LLMs). Fine-tuning involves further training a pre-trained model on a smaller, task-specific dataset to enhance its performance in specialized tasks. Distillation refers to creating a smaller, more efficient model that retains the performance of a larger model. Transfer learning involves reusing a pre-trained model on new but related tasks. This post explores the distinctions between these methods and their optimal use cases.