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
Configuring Training Arguments and Hyperparameters
Build LLM apps faster with Kerb
Cleaner syntax. Built-in debugging. Production-ready from day one.
Built for the AI systems behind ApX Machine Learning
Was this section helpful?
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook covering deep learning optimization principles, including gradient descent, learning rates, batch size effects, and regularization techniques such as weight decay.
- Adam: A Method for Stochastic Optimization, Diederik P. Kingma and Jimmy Ba, 2014 International Conference for Learning Representations (ICLR) DOI: 10.48550/arXiv.1412.6980 - Introduces the Adam optimizer, a widely used adaptive learning rate optimization algorithm often paired with learning rate schedules and weight decay in deep learning training.
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, 2018 arXiv preprint arXiv:1810.04805 DOI: 10.48550/arXiv.1810.04805 - Presents the BERT model and its training methodology, which includes the application of a linear learning rate warmup followed by linear decay, a common strategy for pre-training and fine-tuning large language models.
- transformers.TrainingArguments, Hugging Face, 2024 (Hugging Face) - Official documentation for the
TrainingArgumentsclass in the Hugging Facetransformerslibrary, detailing the configuration options for fine-tuning models.
© 2025 ApX Machine LearningEngineered with