Fine-tuning and Adapting Large Language Models
Chapter 1: Foundations of LLM Adaptation
Recap: Pre-trained Language Models and Transformers
The Need for Fine-tuning and Adaptation
Transfer Learning Paradigms in NLP
Architectural Considerations for Fine-tuning
Overview of Fine-tuning Approaches
Chapter 2: Data Preparation for Fine-tuning
Instruction Tuning Principles
Sourcing and Constructing Instruction Datasets
Formatting Data for Supervised Fine-tuning (SFT)
Domain Adaptation Data Requirements
Handling Data Scarcity and Imbalance
Data Augmentation Techniques for Text
Practice: Preparing an Instruction Tuning Dataset
Chapter 3: Full Parameter Fine-tuning
Mechanism of Full Fine-tuning
Setting up the Training Loop
Hyperparameter Tuning Strategies
Regularization Techniques to Prevent Overfitting
Managing Computational Resources
Checkpointing and Resuming Training
Hands-on Practical: Full Fine-tuning a Smaller LLM
Chapter 4: Parameter-Efficient Fine-tuning (PEFT) Methods
Rationale for Parameter Efficiency
Low-Rank Adaptation (LoRA)
Quantized Low-Rank Adaptation (QLoRA)
Adapter Modules
Prompt Tuning
Prefix Tuning
Comparison of PEFT Techniques
Implementation with Hugging Face PEFT Library
Hands-on Practical: Fine-tuning with LoRA
Hands-on Practical: Fine-tuning with QLoRA
Chapter 5: Advanced Fine-tuning Strategies
Multi-Task Fine-tuning
Sequential Adaptation and Continual Learning
Mitigating Catastrophic Forgetting
Introduction to Reinforcement Learning from Human Feedback (RLHF)
Reward Model Training
Policy Optimization with PPO
Challenges in Advanced Adaptation
Chapter 6: Evaluation and Analysis of Fine-tuned Models
Limitations of Standard NLP Metrics
Evaluating Instruction Following Capabilities
Assessing Factual Accuracy and Hallucinations
Bias and Fairness Assessment Techniques
Robustness Evaluation (Adversarial Attacks, OOD)
Model Calibration Assessment
Qualitative Analysis and Error Categorization
Human Evaluation Protocols
Practice: Analyzing Model Outputs for Errors
Chapter 7: Optimization and Deployment Considerations
Memory Optimization during Training
Accelerating Training with Distributed Strategies
Post-tuning Optimization: Quantization
Post-tuning Optimization: Pruning
Merging PEFT Adapters
Model Serialization and Packaging
Inference Serving Frameworks
Monitoring Fine-tuned Models in Production
Model Serialization and Packaging
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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- Hugging Face Transformers: Share and Use Models, Hugging Face, 2024 (Hugging Face) - A comprehensive guide to saving and loading models, configurations, and tokenizers using the
save_pretrainedandfrom_pretrainedmethods within the Hugging Face Transformers library. - safetensors GitHub Repository, Hugging Face, 2022 - The official repository providing technical specifications, implementations, and background on the
safetensorsformat, emphasizing its security and performance advantages over traditional serialization methods. - Hugging Face PEFT Documentation: Package Structure, Hugging Face, 2024 (Hugging Face) - Documentation describing the specific procedures for saving, loading, and integrating Parameter-Efficient Fine-Tuning (PEFT) adapters, such as LoRA, with base language models.
- ONNX (Open Neural Network Exchange) Documentation, ONNX Community, 2024 (Linux Foundation AI & Data (LFAI)) - The official resource for understanding the ONNX format, its ecosystem, and how it enables model interoperability and optimized inference across various hardware and runtimes.
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