Advanced Certificate in Applied Artificial Intelligence (AI) Programming Module 10: Fine-tuning Large Language Models (LLM)
About This Course
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.
What You'll Learn
• 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
Entry Requirements
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)