A3AIARC102: AI Fine Tuning and Data Preparation for Pre-Trained Models

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About this Course

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.

Audience Profile

• Project Managers
• Architects
• Developers
• Data Acquisition Specialists

At Course Completion

• 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

Outline

Data Curation for AI
• Lecture: Curating Data for AI
• Lecture + Lab: Gathering Raw Data
• Lecture + Lab: Data Cleaning and Preparation
• Lecture + Lab: Data Labeling
• Lecture + Lab: Data Organization
• Lecture: Premade Datasets for Fine Tuning
• Lecture + Lab: Obtain and Prepare Premade Datasets
Deep Learning
• Lecture: What is Intelligence?
• Lecture: Generative AI
• Lecture: The Transformer Model
• Lecture: Feed Forward Neural Networks
• Lecture + Lab: Tokenization
• Lecture + Lab: Word Embeddings
• Lecture + Lab: Positional Encoding
Pre-trained LLM
• Lecture: A History of Neural Network Architectures
• Lecture: Introduction to the LLaMa.cpp Interface
• Lecture: Preparing A100 for Server Operations
• Lecture + Lab: Operate LLaMa3 Models with LLaMa.cpp
• Lecture + Lab: Selecting Quantization Level to Meet Performance and Perplexity Requirements
Fine Tuning
• Lecture: Fine-Tuning a Pre-Trained LLM
• Lecture: PyTorch
• Lecture + Lab: Basic Fine Tuning with PyTorch
• Lecture + Lab: LoRA Fine-Tuning LLaMa3 8B
• Lecture + Lab: QLoRA Fine-Tuning LLaMa3 8B
Operating Fine-Tuned Model
• Lecture: Running the llama.cpp Package
• Lecture + Lab: Deploy Llama API Server
• Lecture + Lab: Develop LLaMa Client Application
• Lecture + Lab: Write a Real-World AI Application using the Llama API

Prerequisites

• Python or Equivalent Experience
• Familiarity with Linux