Introduction#
Fine-tuning large language models used to require multi-GPU clusters and massive memory. QLoRA (Quantized Low-Rank Adaptation) changed that by combining 4-bit quantization with Low-Rank Adaptation (LoRA), enabling you to fine-tune a 7-billion-parameter model on a single 24 GB GPU. The technique freezes the base model weights, loads them in 4-bit precision, and trains only small adapter layers — reducing memory usage by over 60%.
This tutorial walks you through fine-tuning Mistral-7B on a custom instruction dataset using the Hugging Face ecosystem.