Introduction to LLM Fine-Tuning
Chapter 1: Foundations of Model Customization
What is Fine-Tuning?
Pre-training vs. Fine-Tuning
When to Fine-Tune: An Analytical Framework
Overview of Fine-Tuning Strategies
The Role of Transfer Learning in LLMs
Setting Up Your Development Environment
Chapter 2: Data Preparation for Fine-Tuning
Sourcing and Selecting High-Quality Datasets
Instruction-Based vs. Conversational Data Formats
Data Cleaning and Preprocessing Techniques
Creating and Structuring Custom Datasets
Tokenization for Fine-Tuning
Hands-on Practical: Building a Fine-Tuning Dataset
Chapter 3: Full Parameter Fine-Tuning
The Mechanics of Full Fine-Tuning
Architectural Considerations for Full Fine-Tuning
Managing Computational Resources
Configuring Training Arguments and Hyperparameters
Monitoring Training: Loss and Metrics
Saving and Loading Fine-Tuned Models
Practice: Full Fine-Tuning on a Small-Scale Model
Chapter 4: Parameter-Efficient Fine-Tuning (PEFT)
Introduction to Parameter-Efficient Fine-Tuning
Low-Rank Adaptation (LoRA): Theory and Operation
Implementing LoRA with the PEFT Library
Quantization and its effect on Fine-Tuning (QLoRA)
Other PEFT Methods: A Brief Survey
Comparing PEFT and Full Fine-Tuning Trade-offs
Hands-on Practical: Fine-Tuning with LoRA
Chapter 5: Evaluation and Deployment
Defining Performance Metrics for Generative Tasks
Quantitative Evaluation: ROUGE, BLEU, and Perplexity
Qualitative Evaluation: Human-in-the-Loop Assessment
Building an Evaluation Pipeline
Strategies for Merging Adapters with the Base Model
Preparing Models for Inference
Practice: Evaluating a Fine-Tuned Model
The Mechanics of Full Fine-Tuning
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook covering fundamental concepts like neural networks, backpropagation, loss functions, and optimizers, essential for understanding the mechanics of fine-tuning.
- Adam: A Method for Stochastic Optimization, Diederik P. Kingma and Jimmy Ba, 2015 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1412.6980 - Introduces the Adaptive Moment Estimation (Adam) optimizer, a foundational algorithm for the AdamW optimizer discussed in the section.
- 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 enhanced version of Adam that decouples weight decay from the adaptive learning rate, helpful for effectively training large models.
- CS224n: Natural Language Processing with Deep Learning - Course Materials, Stanford University, 2023 (Stanford University) - Provides excellent course materials covering the fundamental principles of deep learning for NLP, including backpropagation, optimizers, and the training loop relevant to fine-tuning LLMs.