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Build advanced, job-ready skills in fine-tuning using Hugging Face, reinforcement learning using proximal preference optimization (PPO), and optimal solutions for direct preference optimization (DPO).
Employers are actively hunting for AI engineers who know how to fine-tune transformers for gen AI applications. This Mastering Generative AI - Advanced Fine-Tuning for LLMs course is designed to give AI engineers and other AI specialists the highly sought-after skills employers need.
AI engineers use advanced fine-tuning skills for LLMs to tailor pre-trained models for specific tasks to ensure accuracy and relevance in applications like chatbots, translation, and content generation.
During this course, you’ll explore the basics of instruction-tuning with Hugging Face, reward modeling, and training a reward model. You’ll look at proximal policy optimization (PPO) with Hugging Face and its configuration, large language models (LLMs) as distributions, and reinforcement learning from human feedback (RLHF). Plus, you’ll investigate direct performance optimization (DPO) with Hugging Face using the partition function.
As you progress through the course, you’ll also build your practical hands-on experience in online labs where you’ll work on reward modeling, PPO, and DPO.
If you’re keen to extend your gen AI engineering skills to include advanced fine-tuning for LLMs so you can catch the eye of an employer, ENROLL TODAY and power up your resume in just 2 weeks!
Prerequisites: To take this course, you need knowledge of LLMs, instruction-tuning, and reinforcement learning. Familiarity with machine learning and neural network concepts is useful too.
Basic knowledge of LLMs, instruction-tuning, and reinforcement learning. Familiarity with machine learning and neural network concepts.
Module 0: Welcome
Module 1: Different Approaches to Fine-Tuning
Module 2: Fine-Tuning Causal LLMs with Human Feedback and Direct Preference
Course Wrap-Up
Who uses advanced fine-tuning techniques for large language models (LLMs)?
AI engineers, machine learning specialists, and NLP developers use fine-tuning techniques to adapt LLMs like GPT for tasks such as chatbots, content generation, and language translation, ensuring applications are tailored to specific needs.
How are tools like Hugging Face used in fine-tuning LLMs?
Hugging Face provides libraries and frameworks that simplify fine-tuning by enabling techniques like instruction-tuning, reward modeling, and reinforcement learning, essential for optimizing models for real-world applications.
Why do AI engineers need to fine-tune LLMs?
AI engineers fine-tune LLMs to adapt pre-trained models to specific tasks, ensuring greater accuracy and relevance for applications like chatbots, translation, and content generation, while reducing computational costs compared to training from scratch.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
