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
Recap: Pre-trained Language Models and Transformers
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- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Ćukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems (NeurIPS), Vol. 30 DOI: 10.48550/arXiv.1706.03762 - Introduces the Transformer architecture, including the self-attention mechanism, which enabled large-scale pre-training of language models.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, 2018 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) DOI: 10.48550/arXiv.1810.04805 - Presents the Bidirectional Encoder Representations from Transformers (BERT) model and introduces the Masked Language Modeling objective for pre-training.
- Improving Language Understanding by Generative Pre-Training, Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, 2018 arXiv preprint DOI: 10.48550/arXiv.1803.05380 - Describes the GPT-1 model and the generative pre-training approach using next-token prediction, a foundation for autoregressive language models.
- CS224N: Natural Language Processing with Deep Learning, Diyi Yang, Tatsunori Hashimoto, 2025 (Stanford University) - An academic course offering extensive lecture materials and assignments on deep learning methods for NLP, including detailed coverage of Transformers and large language models.