Fine-Tuning Large Language Models

Course Overview

You will develop the skills to gather, clean, and organize data for fine-tuning pre-trained LLMs and Generative AI models. Through a combination of lectures and hands-on labs, you will use Python to fine-tune open-source Transformer models. Gain practical experience with LLM frameworks, learn essential training techniques, and explore advanced topics such as quantization. During the hands-on labs, you will access a GPU-accelerated server for practical experience with industry-standard tools and frameworks.

Objectives

• Clean and Curate Data for AI Fine-Tuning
• Establish guidelines for obtaining RAW Data
• Go from Drowning in Data to Clean Data
• Fine-Tune AI Models with PyTorch
• Understand AI architecture: Transformer model
• Describe tokenization and word embeddings
• Install and use AI frameworks like Llama-3
• Perform LoRA and QLoRA Fine-Tuning
• Explore model quantization and fine-tuning
• Deploy and Maximize AI Model Performance

Audience

• Developers
• Data Acquisition Specialists
• Architects
• Project Managers

Prerequisites

• Python – PCEP Certification or Equivalent Experience
• Familiarity with Linux