Fine-Tuning and Distillation: Optimizing Large Language Models

Medium Article: https://medium.com/@jsmith0475/a-detailed-technical-comparison-of-fine-tuning-and-distillation-in-large-language-models-cccbe629dcba The article compares two primary strategies for optimizing Large Language Models (LLMs): fine-tuning and distillation. Fine-tuning adapts a pre-trained model to a specific task, while distillation compresses a large model into a smaller, more efficient one. The source explores the architectures, training dynamics, and trade-offs associated with each technique, highlighting parameter-efficient methods like QLoRA. Hybrid approaches, which combine fine-tuning and distillation, are also examined for their potential to balance adaptability and efficiency. The article concludes by discussing future research directions, including intelligent loss-balancing strategies and self-distilling models, to further enhance LLM optimization.