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Advanced LoRA and PEFT Techniques for LLM Fine-Tuning

Chapter 1: Revisiting Fine-Tuning and the Need for Efficiency

Computational Costs of Full Fine-Tuning

The Parameter Efficiency Imperative

Mathematical Preliminaries: Singular Value Decomposition

Taxonomy of Parameter-Efficient Fine-Tuning Methods

Chapter 2: Low-Rank Adaptation (LoRA) In Depth

The LoRA Hypothesis: Low Intrinsic Rank of Adaptation

Mathematical Formulation of LoRA

Decomposing Weight Update Matrices

Rank Selection Strategies

Scaling Parameter Alpha

Implementing LoRA Layers

Integrating LoRA into Transformer Architectures

Hands-on Practical: Applying Basic LoRA

Chapter 3: Survey of PEFT Methodologies

Adapter Tuning: Architecture and Mechanisms

Adapter Tuning Implementation Details

Prefix Tuning: Conditioning via Continuous Prefixes

Prompt Tuning and P-Tuning Variations

Comparative Analysis: Parameters vs Performance Trade-offs

Memory and Computational Footprints

Hands-on Practical: Implementing Adapter Tuning

Chapter 4: Advanced LoRA Implementations and Variants

LoRA Initialization Strategies

Merging LoRA Weights Post-Training

Quantized LoRA (QLoRA): Principles

QLoRA Implementation Details

Paged Optimizers for Memory Efficiency

Combining LoRA with Other PEFT Approaches

Hands-on Practical: Implementing QLoRA

Chapter 5: Optimization, Deployment, and Practical Considerations

Infrastructure Requirements for PEFT Training

Optimizers and Learning Rate Schedulers for PEFT

Techniques for Multi-Adapter / Multi-Task Training

Debugging PEFT Implementations

Performance Profiling PEFT Training and Inference

Distributed Training Strategies with PEFT

Serving Models with PEFT Adapters

Hands-on Practical: Fine-tuning with Multiple LoRA Adapters

Chapter 6: Evaluating PEFT Performance and Limitations

Standard Metrics for PEFT Evaluation

Benchmarking PEFT against Full Fine-Tuning

Analyzing Robustness and Generalization

Investigating Catastrophic Forgetting

Computational Cost Analysis Revisited

Current Limitations and Open Research Questions

Advanced LoRA and PEFT Techniques for LLM Fine-Tuning

Prerequisites: Python, Deep Learning, LLMs

Level:

Advanced

What You'll Learn

PEFT Fundamentals
Analyze the limitations of full fine-tuning and the mathematical principles behind parameter-efficient approaches.
LoRA Implementation
Implement and configure LoRA layers within standard Transformer architectures.
Advanced LoRA Variants
Implement and evaluate advanced techniques like QLoRA and LoRA merging strategies.
Comparative PEFT Analysis
Compare and contrast various PEFT methods (Adapters, Prefix Tuning, Prompt Tuning) based on performance and computational cost.
PEFT Optimization
Optimize PEFT training workflows, including infrastructure considerations, optimizers, and debugging strategies.
Performance Evaluation
Evaluate the performance, robustness, and limitations of different PEFT methods on downstream tasks.

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