Fine-tuning and Adapting Large Language Models
Chapter 1: Foundations of LLM Adaptation
Recap: Pre-trained Language Models and Transformers
The Need for Fine-tuning and Adaptation
Transfer Learning Paradigms in NLP
Architectural Considerations for Fine-tuning
Overview of Fine-tuning Approaches
Chapter 2: Data Preparation for Fine-tuning
Instruction Tuning Principles
Sourcing and Constructing Instruction Datasets
Formatting Data for Supervised Fine-tuning (SFT)
Domain Adaptation Data Requirements
Handling Data Scarcity and Imbalance
Data Augmentation Techniques for Text
Practice: Preparing an Instruction Tuning Dataset
Chapter 3: Full Parameter Fine-tuning
Mechanism of Full Fine-tuning
Setting up the Training Loop
Hyperparameter Tuning Strategies
Regularization Techniques to Prevent Overfitting
Managing Computational Resources
Checkpointing and Resuming Training
Hands-on Practical: Full Fine-tuning a Smaller LLM
Chapter 4: Parameter-Efficient Fine-tuning (PEFT) Methods
Rationale for Parameter Efficiency
Low-Rank Adaptation (LoRA)
Quantized Low-Rank Adaptation (QLoRA)
Adapter Modules
Prompt Tuning
Prefix Tuning
Comparison of PEFT Techniques
Implementation with Hugging Face PEFT Library
Hands-on Practical: Fine-tuning with LoRA
Hands-on Practical: Fine-tuning with QLoRA
Chapter 5: Advanced Fine-tuning Strategies
Multi-Task Fine-tuning
Sequential Adaptation and Continual Learning
Mitigating Catastrophic Forgetting
Introduction to Reinforcement Learning from Human Feedback (RLHF)
Reward Model Training
Policy Optimization with PPO
Challenges in Advanced Adaptation
Chapter 6: Evaluation and Analysis of Fine-tuned Models
Limitations of Standard NLP Metrics
Evaluating Instruction Following Capabilities
Assessing Factual Accuracy and Hallucinations
Bias and Fairness Assessment Techniques
Robustness Evaluation (Adversarial Attacks, OOD)
Model Calibration Assessment
Qualitative Analysis and Error Categorization
Human Evaluation Protocols
Practice: Analyzing Model Outputs for Errors
Chapter 7: Optimization and Deployment Considerations
Memory Optimization during Training
Accelerating Training with Distributed Strategies
Post-tuning Optimization: Quantization
Post-tuning Optimization: Pruning
Merging PEFT Adapters
Model Serialization and Packaging
Inference Serving Frameworks
Monitoring Fine-tuned Models in Production
Mitigating Catastrophic Forgetting
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- A Comprehensive Survey of Continual Learning: Theory, Methodology and Applications, German I. Parisi, Ronald Kemker, Jose L. Part, Christopher Kanan, Stefan Wermter, 2019 Neural Networks, Vol. 113 (Elsevier) DOI: 10.1016/j.neunet.2019.01.012 - Provides a broad overview of continual learning, including discussions on catastrophic forgetting, rehearsal strategies, and regularization techniques.
- Overcoming catastrophic forgetting in neural networks, James Kirkpatrick, Razvan Pascanu, Gabriel Jimenez Rezende, Adria Puigdomenech Badia, Oriol Vinyals, Fabio Hubert, Zachary Li, Peter Battaglia, Laurent Sifre, Evan Zoph, Martin Reichstein, Dean Hassabis, Iordanis Antonoglou, Charles Blundell, 2017 Proceedings of the National Academy of Sciences, Vol. 114 (National Academy of Sciences) DOI: 10.1073/pnas.1611835114 - Introduces Elastic Weight Consolidation (EWC), a regularization method to mitigate catastrophic forgetting by identifying and protecting important parameters for previous tasks.
- Learning without Forgetting, Zhizhong Li, Derek Hoiem, 2018 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40 (IEEE) DOI: 10.1109/TPAMI.2017.2756813 - Proposes Learning without Forgetting (LwF), which utilizes knowledge distillation from the old model to preserve knowledge when learning new tasks.
- Parameter-Efficient Transfer Learning for NLP, Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly, 2019 Proceedings of the 36th International Conference on Machine Learning (ICML), Vol. 97 (PMLR) DOI: 10.48550/arXiv.1902.00751 - Introduces adapter modules, a parameter-efficient fine-tuning method that can be applied for parameter isolation to mitigate catastrophic forgetting.