Quick Start Guide to Large Language Models (LLMs): Unit 3

This course explores building novel architectures tailored to unique challenges. You’ll gain hands-on experience in building custom multimodal models that integrate visual and textual data, and learn to implement reinforcement learning for dynamic response refinement. Through practical case studies, you’ll learn advanced fine-tuning techniques, such as mixed precision training and gradient accumulation, optimizing open-source models like BERT and GPT-2.

Transitioning from theory to practice, the course also covers the complexities of deploying LLMs to the cloud, utilizing techniques like quantization and knowledge distillation for efficient, cost-effective models. By the end of this course, you’ll be equipped with the skills to evaluate LLM tasks and deploy high-performing models.