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
Standard Metrics for PEFT Evaluation
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- LoRA: Low-Rank Adaptation of Large Language Models, Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2021 International Conference on Learning Representations (ICLR) - Introduces LoRA, a parameter-efficient fine-tuning method frequently evaluated against in the context of this section.
- SuperGLUE: A Stronger General Language Understanding Evaluation Benchmark, Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel Bowman, 2019 Advances in Neural Information Processing Systems 32, Vol. 32 (NeurIPS Proceedings) DOI: 10.48550/arXiv.1905.00537 - Presents SuperGLUE, a widely used benchmark for evaluating natural language understanding models, encompassing various NLU tasks and metrics.
- BLEU: a Method for Automatic Evaluation of Machine Translation, Kishore Papineni, Salim Roukos, Todd Ward, Wei-Jing Zhu, 2002 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL) (Association for Computational Linguistics (ACL)) DOI: 10.3115/1073083.1073135 - Foundational paper introducing BLEU, a standard automatic metric for evaluating the quality of machine-translated text.
- ROUGE: A Package for Automatic Evaluation of Summaries, Chin-Yew Lin, 2004 Text Summarization Branches Out: Proceedings of the ACL-04 Workshop (Association for Computational Linguistics) DOI: 10.3115/1614008.1614022 - Introduces ROUGE, a widely adopted set of metrics for automatically evaluating summarization and other text generation tasks.