Fine-Tuning Large Language Models
Learn the core principles of LLMs with a focus on fine-tuning, transfer learning, and methods like prompt tuning, prefix tuning, and LoRA. Gain hands-on practice customizing LLMs using a Llama2 7B quantized model.
Introduction
Learning Objectives
The Rationale and Impact of Fine-Tuning
Transfer Learning
Fine-Tuning
Quiz: Introduction to Fine-Tuning
Importance of Data
Full Fine-Tuning
Quiz: Data and Techniques in LLM Fine-Tuning
Principal Components Analysis
Practice - PCA Visualization Using Plotly
Practice - PCA on the Iris Dataset
Practice - Image Compression Using PCA
Low-Rank Adaptation (LoRA)
Quantization
Llama 2 Quantization
QLoRA
Quiz: Advanced Optimization Techniques
Key Challenges in Fine-Tuning
Best Practices for Fine-Tuning
SFT vs. RL
Quiz: Challenges and Advanced Strategies
Fine-Tuning