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.
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
Who Should Attend?
This course is recommended for Developers, Data Acquisition Specialists, Architects, and Project Managers
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- Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
- Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
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Agenda
- Introduction
- Learning Your Environment
- Data Curation for AI
- Deep Learning
- Pre-Trained LLM
- Fine Tuning
- Operating Fine-Tuned Models