Fine-Tuning Large Language Models: Maximizing Value and Performance for Custom AI Solutions

Throughout the course, participants will engage in hands-on exercises and case studies to reinforce learning and facilitate the practical application of fine-tuning techniques. Group discussions will encourage collaboration and knowledge sharing among peers. The capstone project at the end of the course will allow participants to demonstrate their fine-tuning skills by developing a custom AI solution that addresses a real-world challenge or opportunity using a large language model like Chat-GPT.

By focusing on the value and use cases of fine-tuned large language models, this course will empower participants to harness the potential of state-of-the-art AI technology for a wide range of applications. Participants will leave the course with a deep understanding of the fine-tuning process and the expertise to create AI solutions tailored to their organization's needs, delivering enhanced performance, increased efficiency, and competitive advantage.

Day 1

Module 1: Introduction to Large Language Models and Fine-Tuning

• Overview of large language models (e.g., GPT-3, Chat-GPT)
• Benefits and challenges of fine-tuning
• Introduction to fine-tuning techniques and tools

Module 2: Data Preparation and Model Selection

• Principles of data selection and annotation for fine-tuning
• Techniques for data preprocessing and cleaning
• Criteria for selecting base models and architectures
• Hands-on exercise: Preparing data sets and selecting models for custom AI applications

Module 3: Training and Optimizing Fine-Tuned Models

• Best practices for training and hyperparameter tuning
• Techniques for model optimization and regularization
• Monitoring model convergence and addressing overfitting
• Hands-on exercise: Training and optimizing a fine-tuned model for a specific use case

Day 2

Module 4: Evaluating Model Performance, Fairness, and Safety

• Metrics and techniques for model evaluation
• Identifying and mitigating biases in fine-tuned models
• Ensuring content safety and adherence to ethical guidelines
• Hands-on exercise: Evaluating and improving fine-tuned model performance, fairness, and safety

Module 5: Fine-Tuning for Various Use Cases and Domains

• Customizing AI solutions for content generation, sentiment analysis, customer service, and more
• Adapting fine-tuning techniques for domain-specific applications
• Hands-on exercise: Fine-tuning a model for a specific use case and domain

Module 6: Capstone Project

• Participants will apply the concepts and techniques learned throughout the course to fine-tune a large language model for a custom AI solution addressing a real-world challenge or opportunity
• Presentation and discussion of capstone projects