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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Motivation and the limitations associated with full fine-tuning
• Overview of PEFT: objectives and key benefits
• Industrial applications and use cases

LoRA (Low-Rank Adaptation)

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

Adapter Tuning

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

Prefix Tuning

• Leveraging soft prompts for fine-tuning purposes
• 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
• Trade-offs involving training speed, memory consumption, and accuracy
• Benchmarking experiments and interpreting results

Deploying Fine-Tuned Models

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

Best Practices and Extensions

• Combining PEFT with quantization and distillation techniques
• Applications in low-resource and multilingual contexts
• Future trends and active research areas