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
Model Calibration Assessment
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- On Calibration of Modern Neural Networks, Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger, 2017 Proceedings of the 34th International Conference on Machine Learning (ICML), Vol. 70 (PMLR (Proceedings of Machine Learning Research)) DOI: 10.5555/3305890.3306010 - Introduces Expected Calibration Error (ECE) and temperature scaling for calibrating modern neural networks, a fundamental work in deep learning calibration.
- Predicting Good Probabilities with Confidence Calibration, Alexandru Niculescu-Mizil, Rich Caruana, 2005 Proceedings of the 22nd International Conference on Machine Learning DOI: 10.1145/1102351.1102430 - Compares various methods for confidence calibration, including isotonic regression and binning, providing a broad overview of calibration techniques.
- Measuring and Improving the Reliability of Large Language Models, Samuel Kadavath, Tom Phambu, Liang Chen, Yuntao T. Lee, Keegan Scherer, Tom Zhao, et al., 2022 arXiv preprint arXiv:2207.05221 (arXiv) DOI: 10.48550/arXiv.2207.05221 - Examines calibration in large language models (LLMs) and discusses methods for improving their reliability, directly addressing LLM-specific challenges.