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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The motivation behind PEFT and the limitations of full fine-tuning
• An 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 using Hugging Face and PyTorch
• Practical session: Fine-tuning a model using LoRA

Adapter Tuning

• Mechanisms and functionality of adapter modules
• Integrating adapters into transformer-based models
• Practical session: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Utilizing soft prompts for the fine-tuning process
• Advantages and constraints relative to LoRA and adapters
• Practical session: Applying Prefix Tuning to an LLM task

Evaluating and Comparing PEFT Methods

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

Deploying Fine-Tuned Models

• Processes for saving and loading fine-tuned models
• Deployment strategies specific to PEFT-based models
• Integration into production applications and pipelines

Best Practices and Extensions

• Combining PEFT with quantization and knowledge distillation
• Applicability in low-resource and multilingual environments
• Future trends and current research directions