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
Data Cleaning and Preprocessing Techniques
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- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Wes McKinney, 2017 (O'Reilly Media) - Provides practical guidance on data manipulation and cleaning using Python's pandas library, including efficient deduplication techniques. 4th edition.
- A Comprehensive Survey on Data Preprocessing for Deep Learning, Md. Tahmidul Islam, Md. Rakib Hasan, Md. Hasanuzzaman, 2023 SN Computer Science, Vol. 4 (Springer Nature) DOI: 10.1007/s42979-023-01777-6 - Surveys various data preprocessing methods relevant to deep learning, including techniques for text data, highlighting their importance for model performance.
- The Hugging Face Course: Fine-tune a pretrained model, Hugging Face, 2024 (Hugging Face) - Provides practical guidance on data preparation for fine-tuning large language models, covering aspects like tokenization, dataset formatting, and best practices for specific tasks.