This LoRA (Low-Rank Adaptation) template visualizes how parameter-efficient fine-tuning works in large language models (LLMs). The diagram highlights key components such as multi-head attention, feed-forward networks, and LoRA adapters integrated into pre-trained layers. It also explains how low-rank matrices are used to update model weights during backpropagation while keeping the original parameters frozen. This slide is ideal for AI, machine learning, and data science presentations, helping teams clearly explain model optimization techniques. Fully editable in PowerPoint and Google Slides.
