Part 1 — What Is Fine-Tuning? Teach an Existing Model a New Job
Understand weights, training, and useful applications with a one-weight numerical experiment.
Guided reading · 10 parts
Start with what fine-tuning means, then learn full tuning, adapters, LoRA, QLoRA, supervised instruction tuning, and preference tuning through ten clear articles, numerical examples, diagrams, and Python notebooks.
Start with Part 1Understand weights, training, and useful applications with a one-weight numerical experiment.
Create a labeled workshop dataset, keep paraphrases together, and learn what a baseline reveals.
Adapt a pretrained miniature router and inspect exactly which parameters changed.
Learn the PEFT family by training a bottleneck adapter and inspecting bias-only updates.
Calculate a LoRA update by hand, train it, and prove that merging preserves predictions.
Quantize a small matrix, measure reconstruction error, and train an update around a frozen quantized base.
Pretrain a tiny language model, mask prompt tokens, and teach a simple instruction-to-label format.
Calculate the DPO objective and train a tiny response-choice policy against a frozen reference.
Compare five adaptation methods, inspect a confusion matrix, and verify a saved model reloads correctly.
Move from teaching models to an optional LoRA training script, then connect each notebook to its website article.