Advanced Certificate in Applied Artificial Intelligence (AI) Programming Module 10: Fine-tuning Large Language Models (LLM)
- Analytics & Tech
- Artificial Intelligence
- Innovation & Business Improvement
2 Full Days (Weekdays)
Who Should Attend
- Data Scientists and Machine Learning Practitioners
- NLP Engineers and Researchers
- Software Developers and Engineers
- AI Enthusiasts
- AI Product Managers and Business Analysts
- Professionals in Linguistics and Language Processing
PREREQUISITES
- Basic programming experience using Python
- Knowledge of NumPy and Pandas (covered in Module 2)
- Recommended to have knowledge of Machine Learning (covered in Module 3)
- Recommended to have knowledge of Deep Learning (covered in Module 4)
- Recommended to have knowledge of AI applications development (covered in Module 5)
Overview
Fine-tuning Large Language Models (LLMs) has emerged as a transformative approach in natural language processing, allowing practitioners to adapt pre-trained models to specific tasks and domains with remarkable performance. In this comprehensive 2-day module, participants will embark on a deep dive into the art and science of fine-tuning LLMs, gaining practical skills and insights to leverage the full potential of these powerful models.
Over the duration of the module, participants will explore the intricacies of fine-tuning techniques, including data preparation, model selection, hyperparameter tuning, and evaluation strategies. Through a combination of theoretical lectures, hands-on exercises, and real-world case studies, participants will learn how to fine-tune LLMs effectively for a wide range of applications, including text classification, language modeling, sentiment analysis, and more.
Whether you're a seasoned data scientist looking to optimise model performance or a newcomer eager to harness the power of LLMs for your projects, this module offers a comprehensive roadmap to mastering fine-tuning techniques and unlocking the full potential of Large Language Models.
Learning Objectives
At the end of the 2-day module, participants will be able to:
- Gain a deep understanding of the principles and methodologies behind fine-tuning Large Language Models (LLMs), including the transfer learning paradigm, model architecture selection, and the importance of task-specific adaptation
- Learn effective strategies for data preprocessing, augmentation, and formatting to optimise LLMs for specific tasks and domains, ensuring high-quality input data for fine-tuning
- Explore different pre-trained LLM architectures and variants, understand their strengths and weaknesses, and learn how to select and configure the most suitable model for fine-tuning tasks
- Master fine-tuning techniques for a diverse range of natural language processing (NLP) tasks, including text classification, language modeling, named entity recognition, sentiment analysis, and more, adapting pre-trained models to specific task requirements
- Develop proficiency in hyperparameter tuning and optimisation techniques to maximise the performance and efficiency of fine-tuned LLMs, including learning rates, batch sizes, and regularisation strategies
- Learn how to effectively evaluate the performance of fine-tuned LLMs using appropriate evaluation metrics and techniques, ensuring robust and reliable model performance across different tasks and datasets
- Identify common challenges and pitfalls encountered during the fine-tuning process, such as overfitting, data imbalance, and domain shift, and develop strategies to mitigate them effectively
- Gain hands-on experience in fine-tuning LLMs for real-world NLP applications and use cases, through practical exercises and case studies covering a variety of domains and tasks
Assessment
As part of the requirement for SkillsFuture Singapore, there will be an assessment conducted at the end of the course. The mode of assessment, which is up to the trainer’s discretion, may be an online quiz, a presentation or based on classroom exercises.
Participants are required to attain a minimum of 75% attendance and pass the associated assessment in order to receive a digital Certificate of Completion issued by Singapore Management University.
Calculate Programme Fee
Fee Table
| COMPANY-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$654 (After SSG Funding 70%) |
$254 (After SSG Funding 70% |
$654 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$254 (After SSG Funding 70% |
$254 (After SSG Funding 70% |
$254 (After SSG Funding 70% |
|
International Participant |
$2,180 (No Funding) |
$2,180 (No Funding) |
$2,180 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Employer Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees are eligible for ETSS funding only if their company's SME status is approved prior to the course commencement date. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Employers who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
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