Enroll Course: https://www.coursera.org/learn/generative-ai-advanced-fine-tuning-for-llms
In the rapidly evolving landscape of Artificial Intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of transforming industries. However, to truly harness their potential, businesses need to tailor these models to their specific needs. This is where the art and science of fine-tuning come into play, and Coursera’s ‘Generative AI Advance Fine-Tuning for LLMs’ course offers a comprehensive and practical guide for aspiring Gen AI engineers.
This course is an absolute must for anyone looking to gain in-demand skills in the generative AI space. It meticulously breaks down the complex process of fine-tuning LLMs, making it accessible yet thorough. The curriculum is structured to build a strong foundation, starting with the fundamentals of instruction-tuning. You’ll learn how to load datasets, set up text generation pipelines, and understand crucial training arguments. A significant portion of the early modules is dedicated to reward modeling, covering essential steps like dataset preprocessing and the application of Low-Rank Adaptation (LoRA) configurations. The ability to quantify response quality, guide model optimization, and incorporate reward preferences are skills that directly translate into tangible business value.
The course truly shines in its exploration of advanced techniques. You’ll delve into fine-tuning causal LLMs using human feedback and direct preference optimization (DPO). Understanding how to generate policies and probabilities for LLM responses is key, and this course explains the intricate relationship between the policy and the language model. Practical applications are emphasized, with detailed explanations on calculating rewards using human feedback, training response samples, and evaluating an agent’s performance. The course also introduces you to scoring functions for sentiment analysis using Proximal Policy Optimization (PPO) with Hugging Face, and provides insights into PPO configuration classes and learning rates. The hands-on labs are invaluable, allowing you to directly apply these concepts to instruction-tuning, reward models, human feedback, and DPO.
While the course acknowledges the complexity of methods like PPO and reinforcement learning, it strikes a perfect balance by providing the necessary knowledge without overwhelming learners. The focus remains on equipping you with practical, employable skills. If you’re looking to enhance LLM accuracy, optimize performance, and gain precise, actionable insights for your business, this course is an excellent investment in your professional development.
Enroll Course: https://www.coursera.org/learn/generative-ai-advanced-fine-tuning-for-llms