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 Duration 14 hours

Course Outline

Foundational Concepts of Parameter-Efficient Fine-Tuning (PEFT)

  • Challenges and constraints associated with full-model fine-tuning
  • Strategic objectives and operational advantages of PEFT
  • Industry-specific applications and deployment scenarios

LoRA (Low-Rank Adaptation) Framework

  • Technical principles and operational logic of LoRA
  • Implementation strategies using Hugging Face and PyTorch
  • Practical application: Fine-tuning models via LoRA

Adapter Tuning Methodologies

  • Mechanisms and functionality of adapter modules
  • Integration protocols with transformer-based architectures
  • Practical application: Deploying Adapter Tuning on transformer models

Prefix Tuning Techniques

  • Application of soft prompts for model adaptation
  • Comparative analysis of strengths and limitations versus LoRA and adapters
  • Practical application: Prefix Tuning for LLM tasks

Assessment and Comparative Analysis of PEFT Methods

  • Evaluation metrics for performance and resource efficiency
  • Balance of trade-offs in training velocity, memory consumption, and model accuracy
  • Interpretation of benchmark experiments and outcome analysis

Deployment of Fine-Tuned Models

  • Procedures for saving and retrieving fine-tuned model states
  • Operational considerations for PEFT-based model deployment
  • Integration into existing application pipelines and systems

Best Practices and Advanced Extensions

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

Concluding Summary and Recommended Pathways

Requirements

  • Foundational understanding of machine learning principles
  • Practical experience with large language models (LLMs)
  • Proficiency in Python and PyTorch

Intended Audience

  • Data scientists
  • AI engineers

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