Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The motivations behind and constraints of full fine-tuning
• Overview of PEFT: objectives and advantages
• Industry applications and real-world use cases

LoRA (Low-Rank Adaptation)

• The core concepts and intuition behind LoRA
• Implementing LoRA via Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model using LoRA

Adapter Tuning

• Understanding the mechanics of adapter modules
• Integrating adapters into transformer-based architectures
• Practical exercise: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Utilizing soft prompts for the fine-tuning process
• Comparative strengths and limitations relative to LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Evaluating and Comparing PEFT Methods

• Key metrics for assessing performance and efficiency
• Trade-offs concerning training speed, memory usage, and accuracy
• Conducting benchmarking experiments and interpreting results

Deploying Fine-Tuned Models

• Procedures for saving and loading fine-tuned models
• Strategic considerations for deploying PEFT-based models
• Integration into existing applications and pipelines

Best Practices and Advanced Extensions

• Combining PEFT with quantization and knowledge distillation
• Applications in low-resource and multilingual contexts
• Emerging trends and areas of active research