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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The rationale and constraints associated with full fine-tuning
• PEFT overview: objectives and key advantages
• Real-world industrial applications and use cases

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuition behind LoRA
• Implementing LoRA with Hugging Face and PyTorch
• Practical session: Fine-tuning a model using LoRA

Adapter Tuning

• Functionality of adapter modules
• Integration strategies with transformer-based architectures
• Practical session: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for model adaptation
• Comparative strengths and limitations versus LoRA and adapters
• Practical session: Prefix Tuning on an LLM task

Evaluation and Comparison of PEFT Methods

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

Deployment of Fine-Tuned Models

• Procedures for saving and loading fine-tuned models
• Deployment considerations specific to PEFT-based models
• Integration into applications and existing pipelines

Best Practices and Advanced Extensions

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