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Fine-Tuning Transformers and Gen AI Models
Course
Fine-Tuning Transformers and Gen AI Models
Advance your AI career with practical skills in fine-tuning transformers and Gen AI models.
Get hands-on with Hugging Face, PyTorch, LoRA, and QLoRA to stand out in interviews.
Flexible Schedule
Intermediate Level
Mentor Support
Estimated Time
8 hours
The demand for technical generative AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large language models (LLMs). This Fine-Tuning Transformers and Gen AI Models course builds job-ready skills to advance your AI career.
In this course, youll explore transformers, model frameworks, and platforms such as Hugging Face and PyTorch. Youll begin with a general framework for optimizing LLMs and quickly move on to fine-tuning generative AI models. Further, youll learn about PEFT, low-rank adaptation (LoRA), quantized low-rank adaptation (QLoRA), and prompting.
Additionally, youll get valuable hands-on experience in online labs that you can talk about in interviews, including loading, pretraining, and fine-tuning models with Hugging Face and PyTorch.
If youre keen to take your AI career to the next level and boost your resume with in-demand Gen AI competencies that catch the eye of an employer. ENROLL today and have job-ready skills you can use straight away within 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 be able to:
- Adapt large language models for custom tasks.
- Fine-tune LLMs efficiently with minimal resources.
- Build strong NLP models through transformer pretraining.
- Train and optimize deep learning models with PyTorch.
- Use Hugging Face to build, fine-tune, and deploy models.
- Intermediate learners comfortable with Python, PyTorch, and transformer architectures.
- Developers or AI practitioners seeking hands-on experience with fine-tuning LLMs using LoRA, QLoRA, and Hugging Face toolkits.
- Professionals aiming to build job-ready generative AI skills and boost their resume with practical lab work and credentials.
- Learners who want to pretrain and fine-tune transformers efficiently and effectively.
Basic knowledge of Python, PyTorch, and transformer architecture. You should also be familiar with machine learning and neural network concepts.
Course Outline
Video: Course Introduction (3:11)
Reading: Specialization Overview
Reading: General Information
Reading: Learning Objectives and Syllabus
Reading: Grading Scheme
Reading: Helpful Tips for Course Completion
Reading: Module Introduction and Learning Objectives
Video: Hugging Face vs. PyTorch (6:16)
Lab: Loading Models and Inference with Hugging Face
Video: Using Pre-Trained Transformers and Fine-Tuning (7:15)
[Optional] Pre-training LLMs with Hugging Face
Video: Fine-Tuning with PyTorch (7:06)
Video: Fine-Tuning with Hugging Face (5:22)
Lab: Pre-Training and Fine-Tuning with PyTorch
Lab: Fine-Tuning Transformers with PyTorch and Hugging Face
Reading: Summary and Highlights: Transformers and Fine-Tuning
Practice Quiz: Transformers and Fine-Tuning
Graded Quiz: Transformers and Fine-Tuning (5 Questions)
Reading: Module Introduction and Learning Objectives
Video: Introduction to PEFT (7:48)
Lab: Adapters with PyTorch
Video: Low-Rank Adaptation (LoRA) (5:39)
Video: LoRA with Hugging Face and PyTorch (7:27)
Lab: LoRA with PyTorch
Video: From Quantization to QLoRA (6:17)
[Optional] Lab: QLoRA with Hugging Face
Reading: Soft Prompts
Reading: Summary and Highlights: Transformers and Fine-Tuning
Practice Quiz: Transformers and Fine-Tuning
Graded Quiz: Transformers and Fine-Tuning (5 Questions)
Reading: Cheat Sheet: Generative AI Engineering and Fine-tuning Transformers
Reading: Course Glossary: Generative AI Engineering and Fine-Tuning Transformers
Reading: Course Conclusion
Reading: Congratulations and Next Steps
Reading: Teams and Acknowledgements
Reading: Copyrights and Trademarks
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
Youll gain hands-on skills in Fine-Tuning Transformers and Gen AI Fine-Tuning using Python and PyTorch. You will learn to perform parameter-efficient fine-tuning (PEFT) with LoRA and QLoRA, work with pretrained transformers for specific tasks, and optimize large language models for real-world applications.
Yes, the course focuses on fine-tuning transformers using Python and PyTorch, including practical labs for loading, pretraining, and fine-tuning models. Youll also apply Gen AI Fine-Tuning techniques for real-world tasks.
Hugging Face transformer models are central to the curriculum. Youll learn to load pretrained transformers, fine-tune them with PyTorch, and leverage Hugging Face tools to optimize performance for generative AI applications.
PEFT allows you to fine-tune models efficiently without retraining all parameters. The course covers PEFT in depth, including hands-on labs with LoRA and QLoRA methods to adapt large language models quickly.
Yes, both Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) are included. Youll apply these methods to pretrained transformers in practical labs for Gen AI Fine-Tuning.
Absolutely. Labs guide you through loading transformers, running inferences, and fine-tuning models for custom tasks using Hugging Face and PyTorch.
Basic knowledge of Python, PyTorch, and neural network principles is recommended. Familiarity with transformers and machine learning concepts will help you maximize hands-on learning.
Generative AI model fine-tuning adapts pretrained models to specific tasks, improving accuracy, relevance, and efficiency. Youll practice this with PEFT, soft prompts, and low-rank adaptation techniques.
Yes, the course includes fine-tuning LLMs using Hugging Face and PyTorch, preparing you for practical applications in generative AI engineering.
Yes, youll earn an industry-recognized IBM Certificate showcasing your skills in fine-tuning transformers, Gen AI fine-tuning, and generative AI model optimization.
Yes, the course includes multiple labs and exercises for loading models, pretraining, and fine-tuning transformers using Hugging Face and PyTorch, giving you job-ready practical experience.
Yes, mastering fine-tuning transformers and Gen AI fine-tuning equips you with in-demand skills sought by employers, making you ready for roles in generative AI development and LLM-based applications.
Type of certificate
IBM Certificate
About this course
02 Modules
05 Skills
Includes
Discussion space
06 Hands-on labs
02 Practice quizzes
02 Graded quizzes
Create
Loading Models and Inference with Hugging
Pre-Training and Fine-Tuning with PyTorch
Fine-Tuning Transformers with PyTorch and Hugging Face
Exercises to explore
Adapters with PyTorch
LoRA with PyTorch
QLoRA with Hugging Face
This course has been created by
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