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
Combining LoRA with Other PEFT Approaches
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- Prefix-Tuning: Optimizing Continuous Prompts for Generation, Xiang Lisa Li, Percy Liang, 2021 Annual Meeting of the Association for Computational Linguistics (ACL) DOI: 10.48550/arXiv.2101.00190 - Introduces Prefix Tuning, which adapts models by optimizing continuous prefix vectors, offering an approach for combination with LoRA.
- QLoRA: Efficient Finetuning of Quantized LLMs on Consumer GPUs, Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer, 2023 arXiv preprint arXiv:2305.14314 DOI: 10.48550/arXiv.2305.14314 - Details QLoRA, a successful combination of LoRA with 4-bit quantization, demonstrating the practicality of combining PEFT techniques.
- UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning, Yuning Mao, Lambert Mathias, Rui Hou, Amjad Almahairi, Hao Ma, Jiawei Han, Wen-tau Yih, Madian Khabsa, 2022 Annual Meeting of the Association for Computational Linguistics (ACL) DOI: 10.48550/arXiv.2110.07577 - Proposes a unified framework for combining various PEFT methods, including LoRA, Adapters, and Prefix-Tuning, supporting multi-PEFT strategies.