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Zero To Mastery

AI Engineering: Fine-Tuning LLMs (with QLoRA, AWS, and Open Source)

via Zero To Mastery Path

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Overview

Master the in-demand AI skill that businesses want: to build and deploy customized LLMs. Learn to fine-tune open-source LLMs on proprietary data and deploy your customized LLM models using AWS SageMaker and Streamlit.
  • Fine-tune open-source LLMs for custom business purposes
  • Deploy and scale models for enterprise purposes using AWS SageMaker and Streamlit
  • Understand and implement QLoRA from theory to code
  • Learn to preprocess proprietary datasets with chunking, tokenization, and attention masking
  • Monitor training and performance to ensure optimal business results
  • Manage cloud resources and optimize for cost
  • Apply advanced AI engineering techniques including quantization and more

Syllabus

  •   Introduction
    • Course Introduction (What We're Building)
    • Exercise: Meet Your Classmates and Instructor
    • Course Resources
    • ZTM Plugin + Understanding Your Video Player
    • Set Your Learning Streak Goal
  •   Setting up our AWS Account
    • Signing in to AWS
    • Creating an IAM User
    • Using our new IAM User
    • What To Do In Case You Get Hacked!
  •   Setting Up AWS Sagemaker Environment
    • Creating a SageMaker Domain
    • Logging in to our SageMaker Environment
    • Introduction to JupyterLab
    • Let's Have Some Fun (+ More Resources)
  •   Gathering, Chunking, Tokenizing and Uploading our Dataset
    • Sagemaker Sessions, Regions, and IAM Roles
    • Examining Our Dataset from HuggingFace
    • Tokenization and Word Embeddings
    • HuggingFace Authentication with Sagemaker
    • Applying the Templating Function to our Dataset
    • Attention Masks and Padding
    • Star Unpacking with Python
    • Chain Iterator, List Constructor and Attention Mask example with Python
    • Understanding Batching
    • Slicing and Chunking our Dataset
    • Creating our Custom Chunking Function
    • Tokenizing our Dataset
    • Running our Chunking Function
    • Understanding the Entire Chunking Process
    • Uploading the Training Data to AWS S3
    • Course Check-In
  •   Understanding LoRA and Setting up HuggingFace Estimator
    • Setting Up Hyperparameters for the Training Job
    • Creating our HuggingFace Estimator in Sagemaker
    • Introduction to Low-rank adaptation (LoRA)
    • LoRA Numerical Example
    • LoRA Summarization and Cost Saving Calculation
    • (Optional) Matrix Multiplication Refresher
    • Understanding LoRA Programatically Part 1
    • Understanding LoRA Programatically Part 2
    • Unlimited Updates
  •   Improving Training Speed with Bfloat 16
    • Bfloat16 vs Float32
    • Comparing Bfloat16 Vs Float32 Programatically
    • Implement a New Life System - at end of 3rd section
  •   Setting up the QLoRA Training Script with Mixed Precision & Double Quantization
    • Setting up Imports and Libraries for the Train Script
    • Argument Parsing Function Part 1
    • Argument Parsing Function Part 2
    • Understanding Trainable Parameters Caveats
    • Introduction to Quantization
    • Identifying Trainable Layers for LoRA
    • Setting up Parameter Efficient Fine Tuning
    • Implement LoRA Configuration and Mixed Precision Training
    • Understanding Double Quantization
    • Creating the Training Function Part 1
    • Creating the Training Function Part 2
    • Exercise: Imposter Syndrome
    • Finishing our Sagemaker Script
    • Gaining Access to Powerful GPUs with AWS Quotas
    • Final Fixes Before Training
  •   Running our Fine Tuning Script for our LLM
    • Starting our Training Job
    • Inspecting the Results of our Training Job and Monitoring with Cloudwatch
  •   Deploying our Fine Tuned LLM
    • Deploying our LLM to a Sagemaker Endpoint
    • Testing our LLM in Sagemaker Locally
    • Creating the Lambda Function to Invoke our Endpoint
    • Creating API Gateway to Deploy the Model Through the Internet
    • Implementing our Streamlit App
    • Streamlit App Correction
  •   Cleaning up Resources
    • Congratulations and Cleaning up AWS Resources
  •   Where To Go From Here?
    • Thank You!
    • Review This Course!
    • Become An Alumni
    • Learning Guideline
    • ZTM Events Every Month
    • LinkedIn Endorsements

Taught by

Patrik Szepesi

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