The initial post of the LoRA series introduces the concept of low-rank adaptation (LoRA) in machine learning, focusing on parameter-efficient fine-tuning methods. It discusses how LoRA allows for effective training of deep learning models by reducing the number of trainable parameters while maintaining performance, thereby addressing challenges associated with fine-tuning large models. This approach leverages the intrinsic quality of large models to adapt effectively with minimal data manipulation. The series aims to explore LoRA in detail, including its implementation and implications for model complexity and training processes in subsequent parts.