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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The motivations for and limitations of full fine-tuning
• An overview of PEFT: its objectives and advantages
• Real-world applications and industry use cases

LoRA (Low-Rank Adaptation)

• The core concepts and intuitive understanding behind LoRA
• Implementing LoRA using Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model with LoRA

Adapter Tuning

• The operational mechanics of adapter modules
• Integration strategies with transformer-based models
• Practical exercise: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for the fine-tuning process
• Analytical comparison of strengths and limitations relative to LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Evaluating and Comparing PEFT Methods

• Key metrics for assessing performance and efficiency
• Navigating trade-offs in training speed, memory consumption, and accuracy
• Conducting benchmarking 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 existing applications and pipelines

Best Practices and Extensions

• Combining PEFT with quantization and distillation techniques
• Application in low-resource and multilingual environments
• Future trajectories and current areas of active research