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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The rationale behind PEFT and the constraints of full fine-tuning
• Core objectives and advantages of the PEFT framework
• Real-world industrial applications and use cases

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuition behind LoRA
• Implementation of LoRA using Hugging Face and PyTorch
• Practical session: Fine-tuning a model via LoRA

Adapter Tuning

• Mechanics of adapter modules
• Integrating adapters with transformer-based architectures
• Practical session: Implementing Adapter Tuning on a transformer model

Prefix Tuning

• Leveraging soft prompts for model adaptation
• Advantages and constraints relative to LoRA and adapters
• Practical session: Applying Prefix Tuning to an LLM task

Assessing and Comparing PEFT Methods

• Key metrics for measuring performance and efficiency
• Balancing training speed, memory consumption, and accuracy
• Conducting benchmark experiments and interpreting outcomes

Deploying Fine-Tuned Models

• Procedures for saving and loading adapted models
• Strategic considerations for deploying PEFT-based solutions
• Incorporating models into broader applications and pipelines

Best Practices and Advanced Extensions

• Integrating PEFT with quantization and distillation techniques
• Applicability in low-resource and multilingual contexts
• Emerging trends and active areas of research