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
Sourcing and Selecting High-Quality Datasets
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- Datasets: A Hugging Face library for accessing and sharing datasets, Hugging Face, 2024 (Hugging Face) - Official documentation for the Hugging Face
datasetslibrary, which provides tools for efficiently loading, processing, and sharing datasets for machine learning, including those used for fine-tuning large language models. - A Survey on Data-Centric AI: From Data to Model, Hongzhi Wang, Peiran Ma, Yanli Li, Donghui Lin, Mengyuan Zhang, Jiancheng Li, 2023 IEEE Transactions on Knowledge and Data Engineering, Vol. 35 (IEEE) DOI: 10.1109/TKDE.2023.3320623 - This survey provides a comprehensive review of data-centric AI, including discussions on data quality, data processing, and their impact on model performance, highly relevant to selecting and preparing datasets.
- About The Licenses, Creative Commons, 2024 (Creative Commons) - Official guide explaining the different Creative Commons licenses and their implications, essential for understanding usage rights of publicly available datasets in commercial and research contexts.
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