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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Drivers and constraints of full fine-tuning.
• Core objectives and advantages of PEFT.
• Industry applications and real-world use cases.

LoRA (Low-Rank Adaptation)

• Core concepts and intuition behind LoRA.
• Implementation of LoRA using Hugging Face and PyTorch.
• Practical exercise: Fine-tuning a model with LoRA.

Adapter Tuning

• Operational mechanics of adapter modules.
• Integration strategies for transformer-based architectures.
• Practical exercise: Applying Adapter Tuning to a transformer model.

Prefix Tuning

• Leveraging soft prompts for model adaptation.
• Advantages and limitations relative to 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.
• Balance between training speed, memory consumption, and accuracy.
• Conducting benchmark tests and interpreting results.

Deploying Fine-Tuned Models

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

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.