Parameter Efficient Fine Tuning and other LLM model compression techniques

A study guide on optimizing Large Language Models (LLMs) for efficiency and managing their operational ecosystem for safety and scalability. It covers Parameter-Efficient Fine-Tuning (PEFT) methods, various model compression techniques including pruning and knowledge distillation, and the "Meta-ML" layer encompassing intelligent routing, dynamic guardrails, and efficient fact-checking systems