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

Foundations of Parameter-Efficient Fine-Tuning (PEFT)

• The rationale for and constraints of full fine-tuning
• Key objectives and advantages of the PEFT approach
• Industry applications and relevant use cases

LoRA (Low-Rank Adaptation)

• Core concepts and the intuition behind LoRA
• Practical implementation using Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model via LoRA

Adapter Tuning

• Mechanisms of adapter modules
• Integration strategies for transformer-based architectures
• Practical exercise: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for model adaptation
• Comparative strengths and limitations relative to LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Evaluation and Comparison of PEFT Strategies

• Key metrics for assessing performance and efficiency
• Balancing training velocity, memory consumption, and accuracy
• Conducting benchmarking experiments and interpreting outcomes

Deployment of Fine-Tuned Models

• Procedures for saving and retrieving fine-tuned models
• Strategic considerations for deploying PEFT-enhanced models
• Incorporation into broader applications and workflows

Best Practices and Advanced Extensions

• Synergizing PEFT with quantization and distillation
• Application in low-resource and multilingual contexts
• Emerging trends and ongoing research directions