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
Managing Computational Resources
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- Mixed-Precision Training, Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu, 2018 International Conference on Learning Representations (ICLR) 2018 DOI: 10.48550/arXiv.1710.03740 - Foundational paper introducing mixed-precision training techniques, including loss scaling, to significantly reduce memory usage and speed up deep learning model training.
- Training Deep Nets with Sublinear Memory Cost, Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin, 2016 arXiv preprint arXiv:1604.06174 DOI: 10.48550/arXiv.1604.06174 - Original research paper proposing gradient checkpointing (also known as activation checkpointing) to trade computation for memory, enabling training deeper neural networks.
- QLoRA: Efficient Finetuning of Quantized LLMs via LoRA, Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer, 2023 arXiv preprint arXiv:2305.14314 DOI: 10.48550/arXiv.2305.14314 - Introduces QLoRA, a method for fine-tuning quantized large language models, heavily relying on 8-bit optimizers and paged optimizers for memory savings.
- DeepSpeed: Large Scale Distributed Training of DL Models with System Optimizations, Saurabh Agarwal, Shuai Che, Michael Gschwind, Hanwen Chang, Minjia Zhang, Reza Yazdani, Jeff Rasley, Elton Zheng, Minmin Gong, Xinggang Wang, Hao Liu, Bo Li, Yuxiong He, 2021 Proceedings of the VLDB Endowment, Vol. 15 (VLDB Endowment) DOI: 10.14778/3554821.3554867 - Presents DeepSpeed, a deep learning optimization library that enables efficient large-scale distributed training through various system optimizations, including memory management.
- Hugging Face Accelerate Documentation, Hugging Face, 2024 (Hugging Face) - Official documentation for Hugging Face Accelerate, a library that simplifies mixed-precision training, gradient accumulation, and distributed training setups.