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
Post-tuning Optimization: Pruning
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- Learning both Weights and Connections for Efficient Neural Networks, Song Han, Jeff Pool, John Tran, William J. Dally, 2015 Advances in Neural Information Processing Systems, Vol. 28 DOI: 10.48550/arXiv.1506.02626 - Presents the foundational method of magnitude-based weight pruning and its effectiveness for reducing model size.
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Subnetworks, Jonathan Frankle, Michael Carbin, 2019 International Conference on Learning Representations (ICLR), Vol. 11576 (Springer, Cham) DOI: 10.1007/978-3-030-22137-7_32 - Introduces the hypothesis that sparse subnetworks exist within randomly initialized networks that can be trained to match the performance of the full network.
- Are Sixteen Heads Really Better than One? Multi-Head Attention Is a Bag-of-Experts, Paul Michel, Omer Levy, Graham Neubig, 2019 International Conference on Learning Representations (ICLR) (International Conference on Learning Representations (ICLR)) DOI: 10.48550/arXiv.1905.09424 - Examines the redundancy within multi-head attention in transformer models, suggesting methods for structured pruning of attention heads.
- torch.nn.utils.prune, PyTorch Documentation, 2023 - Official documentation for PyTorch's
torch.nn.utils.prunemodule, providing functions for applying various pruning methods.
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