Post-Training Large Language Models

Are you looking to make Large Language Models perform specific tasks or exhibit particular behaviors beyond their initial training? This course will equip you with the foundational knowledge and practical understanding to effectively post-train LLMs, enabling you to customize their responses and achieve precise outcomes for various applications. This course will transform your understanding of LLM customization, empowering you to fine-tune models to meet specific requirements for performance and ethical deployment. What you'll learn: * Understand the core concepts and necessity of LLM post-training for adaptation * Apply Supervised Fine-Tuning (SFT) to teach LLMs new skills or response styles * Implement Direct Preference Optimization (DPO) for aligning LLMs with human feedback * Explore Reinforcement Learning principles for fine-tuning LLM behaviors and alignment * Evaluate the performance and alignment of fine-tuned LLMs using relevant metrics * Address ethical considerations and ensure responsible deployment of adapted LLMs Beginning with fundamental terminology and concepts, the course progresses through practical methods for adapting LLMs, concluding with essential techniques for evaluating their performance and ensuring responsible deployment. This course is designed for beginners interested in customizing Large Language Models, with no prior experience in advanced LLM training required. Start your journey to mastering LLM customization today.