Generative AI Advance Fine-Tuning for LLMs
Master advanced LLM fine-tuning techniques, from instruction-tuning to reinforcement learning. Perfect for AI engineers seeking to optimize model performance.
Master advanced LLM fine-tuning techniques, from instruction-tuning to reinforcement learning. Perfect for AI engineers seeking to optimize model performance.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Generative AI Engineering with LLMs Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
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What you'll learn
Master instruction-tuning techniques for LLM optimization
Implement reward modeling with Hugging Face frameworks
Develop proficiency in reinforcement learning from human feedback
Apply proximal policy optimization (PPO) for model training
Create optimal solutions using direct preference optimization
Configure and train advanced language models
Skills you'll gain
This course includes:
1.57 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 2 modules in this course
This comprehensive course focuses on advanced fine-tuning techniques for Large Language Models. Students learn crucial skills in instruction-tuning, reward modeling, and reinforcement learning from human feedback. The curriculum covers direct preference optimization, proximal policy optimization, and practical implementation using Hugging Face. Through hands-on labs, learners gain experience in optimizing LLMs for specific business needs and enhancing model performance.
Different Approaches to Fine-Tuning
Module 1 · 3 Hours to complete
Fine-Tuning Causal LLMs with Human Feedback and Direct Preference
Module 2 · 4 Hours to complete
Fee Structure
Instructors
Ashutosh Sagar: Expert in Generative AI and Fine-Tuning at IBM
Ashutosh Sagar is an instructor at IBM, offering courses in English focused on advanced topics in generative AI. His courses include "Generative AI Advanced Fine-Tuning for LLMs" and "Generative AI Engineering and Fine-Tuning Transformers." These courses delve into the techniques of fine-tuning large language models (LLMs) and transformers, equipping learners with the skills necessary to enhance model performance for specific applications.
Ph.D. Candidate in Health Informatics and Data Scientist at IBM
Fateme is a fourth-year Ph.D. candidate in Health Informatics at McMaster University, where she specializes in applying machine learning to detect behavior abnormalities in sensor data streams. In addition to her academic work, she is a data scientist at IBM. Fateme has published research in esteemed journals like ACM Transactions of Computing for Healthcare and has presented her work at leading institutions, including Mayo Clinic and the Duke Center for Health Informatics. Her contributions are advancing the field of data-driven healthcare solutions.
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