The Ultimate Guide to Fine-Tuning Large Language Models with PyTorch and HuggingFace
The Ultimate Guide to Fine-Tuning Large Language Models with PyTorch and HuggingFace
About the Book
This book is a practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks. It covers essential topics, from loading quantized models and setting up LoRA adapters to properly formatting datasets and selecting the right training arguments. The book focuses on the key details and "knobs" you need to adjust to effectively handle LLM fine-tuning. Additionally, it provides a comprehensive overview of the tools you'll need along the way, including HuggingFace's Transformers, Datasets, and PEFT packages, as well as BitsAndBytes, Llama.cpp, and Ollama. The final chapter includes troubleshooting tips for common error messages and exceptions that may arise.
About the Author
Table of Contents
- Frequently Asked Questions (FAQ)
- Why Fine-Tune LLMs?
- Why This Book?
- Who Should Read This Book?
- What Do I Need to Know?
- How to Read This Book
- Chapter 0: TLDR;
- Loading a Quantized Model
- Setting Up Low-Rank Adapters
- Formatting Your Dataset
- Training with HuggingFace
- Querying the Model
- Chapter 1: Pay Attention to LLMs
- Language Models, Small and Large
- Attention is All You Need
- No Such Thing As Too Much RAM
- Flash Attention
- Types of Fine-Tuning
- Self-Supervised
- Supervised
- Instruction
- Chapter 2: Quantizing Models
- Overview of Data Types
- BitsAndBytes
- 8-bit Quantization
- 4-bit Quantization
- Loading a Quantized Model
- Chapter 3: Low-Rank Adaptation (LoRA)
- Fine-Tuning as Adaptation
- Going Low-Rank
- PeFT
- Setting Up Low-Rank Adapters
- Chapter 4: Formatting Your Dataset
- Tokenization
- Special Tokens Galore
- Padding or Packing
- Chatterbox Models and the EOS Token
- Chat Templates
- ChatML
- Formatting Functions
- Text Fields
- Chapter 5: Training with HuggingFace
- SFTTrainer
- Trainer
- TrainingArguments
- Optimizers
- Gradient Checkpointing
- Training Models
- Self-Supervised (#1)
- Instruction (#2)
- Supervised (#3)
- Chapter 6: Deploying It Locally
- Querying Your Model
- Llama.cpp
- The GGUF Format
- Conversion
- Quantization
- Ollama
- Custom Model
- Chapter -1: Troubleshooting
- Appendix: Setting Up Your GPU Pod
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