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Mastering Generative AI: Advanced Fine-Tuning for LLMs

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IntermediateCourse

Advance your skills in fine-tuning language models with our course on Generative AI. You will explore reinforcement learning techniques, such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO), to enhance model security. Learn how to effectively use Hugging Face for instruction tuning. This course is designed for intermediate learners eager to enhance their AI expertise securely.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Instruction Tuning, Hugging Face, Reinforcement Learning, Proximal Policy Optimization, Direct Preference Optimization

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 2 Weeks 5 hrs

Platform

  • edX

Last Update

  • February 5, 2025
About this Course
  1. Welcome to the Mastering Generative AI: Advanced Fine-Tuning for LLMs course!

  2. This course will take you through the advanced techniques for fine-tuning generative large language models (LLMs). Throughout this journey, you will explore instruction-tuning with Hugging Face, delve into reward modeling, and gain hands-on experience in training a reward model. Moreover, you will learn about proximal policy optimization (PPO) and its application using Hugging Face, understand LLMs as policies, and explore reinforcement learning from human feedback (RLHF). Finally, the course will guide you through direct performance optimization (DPO) using Hugging Face and the partition function.

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  2. Prerequisites
  3. To get the most out of this course, you should be comfortable with the following topics and technologies:

    • A solid understanding of basic Generative AI concepts and models.
    • Experience with Python programming, particularly in AI/ML contexts.
    • Familiarity with Hugging Face and reinforcement learning concepts.
  4. Congratulations on taking this step to advance your skills in generative Al! Enjoy your learning journey!


Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Rav Ahuja

Global Program Director, IBM Skills Network

Rav Ahuja is a Global Program Director at IBM. He leads growth strategy, curriculum creation, and partner programs for the IBM Skills Network. Rav co-founded Cognitive Class, an IBM led initiative to democratize skills for in demand technologies. He is based out of the IBM Canada Lab in Toronto and specializes in instructional solutions for AI, Data, Software Engineering and Cloud. Rav presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in managing and analyzing data. Rav holds B. Eng. from McGill University and MBA from University of Western Ontario.

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