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
Implementing LoRA with the PEFT Library
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- LoRA: Low-Rank Adaptation of Large Language Models, Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2021 arXiv preprint arXiv:2106.09685 DOI: 10.48550/arXiv.2106.09685 - The original paper introducing Low-Rank Adaptation (LoRA), detailing its theoretical background and mechanism for parameter-efficient fine-tuning.
- Parameter-Efficient Fine-Tuning (PEFT) Library Documentation, Hugging Face, 2024 (Hugging Face) - The official documentation for the Hugging Face PEFT library, providing comprehensive guides and API references for implementing LoRA and other PEFT methods.
- QLoRA: Efficient Finetuning of Quantized LLMs, Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer, 2023 arXiv preprint arXiv:2305.14314 DOI: 10.48550/arXiv.2305.14314 - Introduces QLoRA, an efficient fine-tuning method using 4-bit quantization, relevant for understanding the memory-saving techniques discussed in the section.
- Hugging Face Transformers Library Documentation, Hugging Face, 2024 (Hugging Face) - The official documentation for the Hugging Face Transformers library, offering essential information on loading and using pre-trained models and tokenizers, which are prerequisites for applying PEFT.