Advanced fine-tuning methods on Amazon SageMaker AI

This post provides the theoretical foundation and practical insights needed to navigate the complexities of LLM development on Amazon SageMaker AI, helping organizations make optimal choices for their specific use cases, resource constraints, and business objectives.

We also address the three fundamental aspects of LLM development: the core lifecycle stages, the spectrum of fine-tuning methodologies, and the critical alignment techniques that provide responsible AI deployment. We explore how Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA have democratized model adaptation, so organizations of all sizes can customize large models to their specific needs. Additionally, we examine alignment approaches such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), which help make sure these powerful systems behave in accordance with human values and organizational requirements. Finally, we focus on knowledge distillation, which enables efficient model training through a teacher/student approach, where a smaller model learns from a ...