Class 19: LLM Fine-Tuning and Customization

Summary: Techniques for adapting pre-trained language models to specific domains and organizational needs.

Learning Objectives:

• Understand fine-tuning methodologies and trade-offs
• Implement parameter-efficient fine-tuning techniques
• Evaluate fine-tuned model performance

Key Topics:

• Transfer learning and model adaptation strategies
• Full fine-tuning versus parameter-efficient methods (LoRA, QLoRA)
• Dataset preparation for fine-tuning
• Model evaluation and validation frameworks

Activities:

• Fine-tune a model for domain-specific tasks
• Dataset curation workshop
• Performance benchmarking exercise