Free Lesson
⚙️ Tailor a Model: Fine-Tuning, LoRA, and Quantization
Part of Life of a Model: Build, Teach, Tune, Ship
60 min
Nov 9, 2026 2:00 PM
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What you'll learn
Choose between fine-tuning, prompting, and RAG
Use a simple decision framework to know when fine-tuning pays off and when it doesn't.
Fine-tune efficiently with LoRA and QLoRA
Adapt a large model by training a small set of added weights instead of retraining the whole thing.
Shrink models with distillation and quantization
Trade precision (FP16, INT8, INT4) for speed and memory, and weigh quality against size and cost.
Why this topic matters
Most teams never train a model from scratch, but almost every team needs to adapt one and run it affordably. Fine-tuning fits the model to your task, while distillation and quantization fit it to your budget. These choices decide whether an AI feature stays a demo or becomes a product you can serve at a cost that makes business sense.
