"Fine-tuning large language models (LLMs) is essential for aligning them with specific business needs, improving accuracy, and optimizing performance. In today’s AI-driven world, organizations rely on fine-tuned models to generate precise, actionable insights that drive innovation and efficiency. This course equips aspiring generative AI engineers with the in-demand skills employers are actively seeking.
You’ll explore advanced fine-tuning techniques for causal LLMs, including instruction tuning, reward modeling, and direct preference optimization. Learn how LLMs act as probabilistic policies for generating responses and how to align them with human preferences using tools such as Hugging Face. You’ll dive into reward calculation, reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO), the PPO trainer, and optimal strategies for direct preference optimization (DPO).
The hands-on labs in the course will provide real-world experience with instruction tuning, reward modeling, PPO, and DPO, giving you the tools to confidently fine-tune LLMs for high-impact applications.
Build job-ready generative AI skills in just two weeks! Enroll today and advance your career in AI!"