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

Foundations of Parameter-Efficient Fine-Tuning (PEFT)

• The drivers and constraints associated with full fine-tuning
• An overview of PEFT objectives and advantages
• Industry applications and practical use cases

LoRA (Low-Rank Adaptation)

• The core concept and intuitive understanding of LoRA
• Implementation of LoRA utilizing Hugging Face and PyTorch
• Practical session: Fine-tuning a model via LoRA

Adapter Tuning

• The mechanism behind adapter modules
• Integration strategies for transformer-based architectures
• Practical session: Applying Adapter Tuning to a transformer model

Prefix Tuning

• The use of soft prompts for model adaptation
• Advantages and limitations when contrasted with LoRA and adapters
• Practical session: Executing Prefix Tuning on an LLM task

Assessment and Comparison of PEFT Methods

• Key metrics for assessing performance and efficiency
• Trade-offs involving training speed, memory consumption, and accuracy
• Conducting benchmark experiments and interpreting results

Deployment of Fine-Tuned Models

• Procedures for saving and loading fine-tuned models
• Key considerations for deploying PEFT-based models
• Integration into existing applications and workflows

Best Practices and Advanced Extensions

• Combining PEFT with quantization and distillation techniques
• Application in low-resource and multilingual contexts
• Emerging trends and active areas of research