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
Pre-training vs. Fine-Tuning
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- Universal Language Model Fine-tuning for Text Classification, Jeremy Howard and Sebastian Ruder, 2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Association for Computational Linguistics) DOI: 10.18653/v1/P18-1031 - This paper introduced an effective method for fine-tuning pre-trained language models for downstream NLP tasks, demonstrating the efficiency and performance gains of transfer learning.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, 2019 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (Association for Computational Linguistics) DOI: 10.18653/v1/N19-1423 - A landmark paper that presented a powerful pre-training technique for language models and a successful fine-tuning strategy, becoming a standard for many subsequent LLMs.
- Transfer Learning in Natural Language Processing, Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, Thomas Wolf, 2019 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials (Association for Computational Linguistics) DOI: 10.18653/v1/N19-5004 - This tutorial provides a comprehensive overview of transfer learning techniques applied to natural language processing, serving as an excellent resource for understanding its principles and applications in LLMs.
- Exploring the Limits of Language Modeling, Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu, 2016 arXiv preprint arXiv:1602.02410 DOI: 10.48550/arXiv.1602.02410 - This paper demonstrates the effectiveness of large-scale language model pre-training on vast datasets, showcasing the potential for models to learn general language understanding before specialized tasks.