Hands-On Build Guide: QLoRA Fine‑Tuner From Scratch in PyTorch with NF4 Quantization and LoRA Merging
A step‑by‑step guide to training a quantized LLaMA‑style model with QLoRA, merging LoRA adapters, and packaging the result as a portable CV asset.
Qlora
LoRA vs QLoRA: A Practical Guide to Efficient LLM Fine‑Tuning
Introduction As large language models (LLMs) have grown into the tens and hundreds of billions of parameters, full fine‑tuning has become prohibitively expensive for most practitioners. Two techniques—LoRA and QLoRA—have emerged as leading approaches for parameter-efficient fine‑tuning (PEFT), enabling high‑quality adaptation on modest hardware. They are related but distinct: LoRA (Low-Rank Adaptation) introduces small trainable matrices on top of a frozen full‑precision model. QLoRA combines 4‑bit quantization of the base model with LoRA adapters, making it possible to fine‑tune huge models (e.g., 65B) on a single 24–48 GB GPU. This article walks through: ...