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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Why full fine-tuning has limitations and the motivation for PEFT
• The goals and advantages of the PEFT paradigm
• Real-world industry applications and use cases

LoRA (Low-Rank Adaptation)

• The core concept and intuition behind LoRA
• Implementation using Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model via LoRA

Adapter Tuning

• Mechanisms behind adapter modules
• Integrating adapters into transformer-based architectures
• Practical exercise: Applying Adapter Tuning to a transformer model

Prefix Tuning

• The role of soft prompts in the fine-tuning process
• Comparing strengths and limitations against LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Evaluating and Comparing PEFT Methods

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

Deploying Fine-Tuned Models

• Processes for saving and loading fine-tuned models
• Strategic considerations for deploying PEFT-based models
• Integration into production applications and pipelines

Best Practices and Advanced Extensions

• Enhancing PEFT with quantization and distillation techniques
• Application in low-resource and multilingual environments
• Exploring future trends and active research domains