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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The drivers behind PEFT and the constraints of full fine-tuning
• An overview of PEFT objectives and advantages
• Industry applications and real-world use cases

LoRA (Low-Rank Adaptation)

• Core concepts and intuitive understanding of LoRA
• Implementation of LoRA using Hugging Face and PyTorch
• Practical session: Fine-tuning a model with LoRA

Adapter Tuning

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

Prefix Tuning

• Leveraging soft prompts for the fine-tuning process
• Comparing strengths and limitations against LoRA and adapters
• Practical session: Executing Prefix Tuning on an LLM task

Assessing and Comparing PEFT Methods

• Key metrics for gauging performance and efficiency
• Navigating trade-offs in training speed, memory consumption, and accuracy
• Conducting benchmark experiments and interpreting results

Deploying Fine-Tuned Models

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

Best Practices and Extensions

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