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

About this course

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.

What you will learn

• Develop custom multimodal models and implement reinforcement learning for dynamic LLM refinement.
• Master advanced fine-tuning techniques, optimizing open-source models for specific tasks.
• Deploy LLMs to the cloud using quantization, pruning, and knowledge distillation for efficient performance.
• Evaluate LLM tasks across various categories, preparing models for real-world applications.