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

Fundamentals of Parameter-Efficient Fine-Tuning (PEFT)

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

LoRA (Low-Rank Adaptation)

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

Adapter Tuning

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

Prefix Tuning

• Leveraging soft prompts for fine-tuning purposes
• Comparative strengths and limitations relative to LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Evaluation and Comparison of PEFT Techniques

• Key metrics for assessing performance and efficiency
• Balancing training speed, memory consumption, and model accuracy
• Interpreting benchmark results and experimental outcomes

Deployment of Fine-Tuned Models

• Procedures for saving and loading fine-tuned weights
• Considerations for deploying PEFT-enhanced models
• Integration into production applications and pipelines

Best Practices and Advanced Extensions

• Combining PEFT with quantization and model distillation
• Application in low-resource and multilingual contexts
• Future trajectories and current research fronts