Prerequisites

  • Strong understanding of AI and machine learning concepts
  • Familiarity with natural language processing (NLP) techniques and tools
  • Experience in Python programming and working knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch)

Skills Gained

  • Understand the principles and benefits of fine-tuning large language models like Chat-GPT
  • Prepare data sets and choose appropriate models for fine-tuning tasks
  • Implement best practices for training and optimizing fine-tuned models
  • Evaluate model performance, fairness, and safety in custom AI applications
  • Apply fine-tuning techniques to create AI solutions for various use cases and domain

Who Can Benefit?

Data scientists, AI/ML engineers, software developers, and professionals interested in developing custom AI applications using large language models like Chat-GPT

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

    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

    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.