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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The rationale and constraints of full fine-tuning
• PEFT overview: objectives and advantages
• Industrial applications and real-world use cases

LoRA (Low-Rank Adaptation)

• Core concepts and intuition behind LoRA
• Practical implementation of LoRA with Hugging Face and PyTorch
• Hands-on session: Fine-tuning a model using LoRA

Adapter Tuning

• Functionality of adapter modules
• Integration strategies for transformer-based models
• Hands-on session: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Utilization of soft prompts for fine-tuning
• Comparison of strengths and limitations with LoRA and adapters
• Hands-on session: Executing Prefix Tuning on an LLM task

Evaluation and Comparison of PEFT Methods

• Key metrics for assessing performance and efficiency
• Balancing training speed, memory consumption, and accuracy
• Benchmarking experiments and interpreting results

Deploying Fine-Tuned Models

• Procedures for saving and loading fine-tuned models
• Key considerations when deploying PEFT-based models
• Integration into production applications and pipelines

Best Practices and Advanced Extensions

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