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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Drivers and constraints associated with full fine-tuning
• Overview of PEFT: objectives and advantages
• Real-world industrial applications and use cases

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuitive understanding of LoRA
• Implementation of LoRA 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 the fine-tuning process
• Comparative strengths and limitations versus LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Evaluation and Comparison of PEFT Methods

• Key metrics for assessing performance and efficiency
• Balancing trade-offs in training speed, memory consumption, and accuracy
• Conducting benchmark experiments and interpreting outcomes

Deployment of Fine-Tuned Models

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

Best Practices and Advanced Extensions

• Combining PEFT with quantization and distillation techniques
• Application in low-resource and multilingual environments
• Emerging trends and active research frontiers