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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

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

Setting up the Training Loop

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References

Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This book provides a comprehensive theoretical foundation for deep learning, covering concepts such as neural networks, backpropagation, optimization algorithms, and loss functions, which are fundamental to understanding the training loop.
Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc.) - Introduces the Transformer architecture, which forms the basis of Large Language Models. Understanding this architecture helps grasp how LLMs process input and generate logits within the forward pass.
Decoupled Weight Decay Regularization, Ilya Loshchilov and Frank Hutter, 2019 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1711.05101 - Presents AdamW, an optimization algorithm widely used for training transformer models, as specifically mentioned in the section for the optimizer step.
PyTorch Documentation: Tutorials, Autograd, and Optim, PyTorch Core Developers, Ongoing (PyTorch Foundation) - The official resource for practical implementation of deep learning training loops using PyTorch, including details on automatic differentiation, optimizers, and data handling.
Hugging Face Transformers: Trainer Class Documentation, Hugging Face, Ongoing (Hugging Face) - Describes the Trainer class, a high-level API for fine-tuning Transformer models, which abstracts many details of the training loop for efficient LLM adaptation.

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