LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning approach specifically designed for large language models (LLMs), as covered in NVIDIA’s Generative AI and LLMs course. It fine-tunes LLMs by updating a small subset of parameters through low-rank matrix factorization, significantly reducing memory and computational requirements compared to full fine-tuning. This makes LoRA ideal for adapting large models to specific tasks while maintaining efficiency. Option A, TensorRT, is incorrect, as it is an inference optimization library, not a fine-tuning method. Option B, NeMo, is a framework for building AI models, not a specific fine-tuning technique. Option C, Chinchilla, is a model, not a fine-tuning approach. The course emphasizes: “Parameter-efficient fine-tuning methods like LoRA enable memory-efficient adaptation of LLMs by updating low-rank approximations of weight matrices, reducing resource demands while maintaining performance.”