Qlora is an innovative technique focused on efficiently fine-tuning language learning models with limited computational resources. It utilizes quantization, a method that reduces the memory and computational requirements by representing numbers with fewer bits. This process allows large-scale models to be more resource-efficient, making them accessible for smaller devices without sacrificing performance. Its advantages highlight the importance of efficiency in model deployment, juxtaposing approaches like full-precision training.