FTLLM-MVPCAI
Fine-Tuning Large Language Models: Maximizing Value and Performance for Custom AI Solutions Training
In this AI Solutions course, participants learn how to fine-tune large language models (LLMs) like Chat-GPT to build custom AI solutions tailored to specific use cases and domains. This course covers fine-tuning fundamentals, including data preparation, model selection, and training best practices. Participants will also learn how to evaluate and optimize fine-tuned models for improved performance, fairness, and safety.
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.
Course Details
Duration
2 days
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
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)
Target Audience
- Data scientists, AI/ML engineers, software developers, and professionals interested in developing custom AI applications using large language models like Chat-GPT.
Course Outline
- 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
- 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
- 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
- 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
- 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
- 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
