Advanced Fine-Tuning for Large Language Models (LLMs)
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Course
Advanced Fine-Tuning for Large Language Models (LLMs)
Master the art of fine-tuning LLMs to boost accuracy, align with business goals, and unlock actionable insights that drive innovation.
Self-Paced
Mentored
Intermediate
Duration
Fine-tuning your LLM is crucial for aligning it with specific business needs, enhancing accuracy, and optimizing its performance. In turn, this gives businesses precise, actionable insights that drive efficiency and innovation. This course gives aspiring generative AI engineers valuable fine-tuning skills employers are actively seeking.
During this intermediate-level course, youll explore different approaches to fine-tuning and casual LLMs with human feedback and direct preference. Youll look at LLMs as policies for probability distributions for generating responses and the concepts of instruction-tuning with Hugging Face. Youll learn to calculate rewards using human feedback and reward modeling with Hugging Face. Plus, youll explore reinforcement learning from human feedback (RLHF), proximal policy optimization (PPO) and PPO Trainer, and optimal solutions for direct preference optimization (DPO) problems.
As you learn, youll get valuable hands-on experience in online labs where youll work on reward modeling, PPO, and DPO.
If youre looking to add in-demand capabilities in fine-tuning LLMs to your resume, ENROLL TODAY and build the job-ready skills employers are looking for in just two weeks!
This course comprises two purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 8 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will learn:
- In-demand generative AI engineering skills in fine-tuning LLMs employers are actively looking for in just 2 weeks .
- Instruction-tuning and reward modeling with Hugging Face, plus LLMs as policies and RLHF.
- Direct preference optimization (DPO) with partition function and Hugging Face and how to create an optimal solution to a DPO problem.
- How to use proximal policy optimization (PPO) with Hugging Face to create a scoring function and perform dataset tokenization.
- AI/ML practitioners with basic LLM knowledge
- Developers building or fine-tuning LLMs
- Data scientists exploring RLHF, PPO, or DPO
- Intermediate learners seeking hands-on LLM tuning skills
- Basic knowledge of LLMs, instruction-tuning, and reinforcement learning.
- Familiarity with machine learning and neural network concepts is also beneficial.
Course Outline
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
The course is ideal for AI/ML engineers, data scientists, software developers, and professionals looking to strengthen their generative AI engineering skills. If you want to understand how to fine-tune LLMs for real-world deployment, this program is a strong fit.
Youll gain skills in instruction-tuning LLMs, reward modeling with Hugging Face, direct preference optimization (DPO), and PPO reinforcement learning in Hugging Face. Beyond technical expertise, youll also develop job-ready generative AI skills employers are actively seeking.
Yes. A dedicated module focuses on instruction-tuning LLMs using Hugging Face, where youll practice aligning models to specific tasks and datasets through guided labs.
DPO is an approach for aligning model outputs with human preferences without requiring full reward modeling. In this course, youll learn both the theory of DPO and its application using Hugging Face model fine tuning.
Yes. This LLM fine-tuning course emphasizes practical application. Youll work in online labs covering reward modeling with Hugging Face, PPO reinforcement learning, and DPO optimization workflows.
Yes. Upon successful completion, youll receive an IBM Certificate that validates your expertise in optimizing LLMs. This credential demonstrates your readiness for AI engineer and ML roles.
The course is structured to be completed in two weeks, making it a fast-paced fine tuning training program designed to quickly build practical skills.
Youll not only learn the theory but also practice in hands-on LLM training labs. The focus is on fine-tuning AI models for business, ensuring you can optimize accuracy and performance for practical deployment.
Industries such as finance, healthcare, e-commerce, and enterprise software are looking for professionals skilled in LLM fine-tuning to build domain-specific AI solutions.
Career paths include AI engineer, machine learning engineer, generative AI specialist, and NLP engineer. These roles increasingly require fine-tuning LLMs as a core competency.
While salaries vary by region, AI engineers with advanced LLM fine-tuning skills often earn 2030% higher than general ML engineers, reflecting the demand for this expertise.
Yes. This LLM fine-tuning course strengthens your profile with in-demand AI skills training, giving you an edge in advancing to senior-level AI and ML engineering roles.
No. The program is designed for intermediate-level learners who already have basic exposure to ML or LLMs. It bridges the gap between foundational knowledge and advanced LLM optimization techniques.
Fine-tuning aligns models with domain-specific data, improves output precision, and ensures responses are business-relevant. This results in AI systems that deliver measurable impact for organizations.
Course Offering
Type of certificate
IBM Certificate
About this course
02 Modules
04 Skills
Includes
Discussion space
04 Hands-on labs
02 Practice quizzes
02 Graded quizzes
Create
Reinforcement Learning from Human Feedback Using PPO
Direct Preference Optimization (DPO) Using Hugging Face
Exercises to explore
Instruction Fine-Tuning LLMs
Reward Modeling
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