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
Hands-on Practical: Building a Fine-Tuning Dataset
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- Hello Dolly: Democratizing Generative AI, The Databricks Team, 2023 Databricks Blog (Databricks) - Introduces the
databricks-dolly-15kdataset, its creation methodology, and its role in open-source instruction-following LLMs. - The 🤗 Datasets Library Documentation, Hugging Face, 2024 Hugging Face Documentation - Official documentation for the Hugging Face
datasetslibrary, covering data loading, processing, and management, which is central to building fine-tuning datasets. - Tokenization in 🤗 Transformers, Hugging Face, 2024 Hugging Face Documentation (Hugging Face) - A guide to tokenization within the Hugging Face
transformerslibrary, explaining its practical usage for preparing text data for LLMs. - Finetuned Language Models Are Zero-Shot Learners, Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le, 2021 arXiv (arXiv) DOI: 10.48550/arXiv.2109.01652 - A foundational paper on instruction tuning, demonstrating how structuring tasks for fine-tuning enables better generalization in language models.