Skip to Scheduled Dates
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.
Who Should Attend
This course is recommended for Developers, Data Acquisition Specialists, Architects, and Project Managers
Course 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
Course Outline
- Introduction
- Learning Your Environment
- Data Curation for AI
- Deep Learning
- Pre-Trained LLM
- Fine Tuning
- Operating Fine-Tuned Models