In this presentation, we will explore the fascinating realm of Low-Rank Adaptation (LoRA) technique in the context of large language models.
As the demand for efficient and versatile language models continues to surge, understanding how to optimize their performance becomes paramount.
Leveraging low-rank adaptations offer a promising avenue to achieve both efficiency and generalization, enabling language models to excel across diverse domains and contexts.
At Salted CX, my role as a Machine Learning engineer revolves around specializing in Natural Language Processing. Specifically, I utilize Transformer models to gain insights into the operations of contact centers, enhancing visibility and understanding. I hold an Engineering Doctoral (EngD) degree in Data Science from the Technical University of Eindhoven.
