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Course Outline

Overview of Parameter-Efficient Fine-Tuning (PEFT)

  • Rationale for PEFT and constraints associated with full fine-tuning
  • PEFT framework: objectives and operational advantages
  • Industrial applications and strategic use cases

Low-Rank Adaptation (LoRA)

  • Core principles and conceptual foundation of LoRA
  • Implementation utilizing Hugging Face and PyTorch libraries
  • Practical application: Executing model fine-tuning via LoRA

Adapter-Based Tuning

  • Mechanisms governing adapter module functionality
  • Incorporation within transformer-based architectures
  • Practical application: Deploying Adapter Tuning on a transformer model

Prefix Tuning Techniques

  • Utilization of soft prompts for parameter optimization
  • Comparative analysis of efficacy relative to LoRA and adapter methods
  • Practical application: Conducting Prefix Tuning on Large Language Model (LLM) tasks

Assessment and Comparative Analysis of PEFT Methodologies

  • Performance metrics and efficiency indicators for evaluation
  • Operational trade-offs involving training latency, memory consumption, and accuracy
  • Benchmarking procedures and interpretation of experimental outcomes

Deployment of Fine-Tuned Models

  • Procedures for saving and loading fine-tuned model weights
  • Strategic considerations for deploying PEFT-enabled models in production environments
  • Integration protocols for enterprise applications and data pipelines

Operational Guidelines and Advancements

  • Synergies between PEFT, quantization, and knowledge distillation techniques
  • Implementation within low-resource computational and multilingual contexts
  • Emerging research trends and future developmental pathways

Executive Summary and Strategic Next Steps

Requirements

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

Intended Audience

  • Data scientists
  • AI engineers
 14 Hours

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