QLoRA Fine-Tuning Guide: Axolotl, Unsloth & PEFT Tuning
Prerequisite: Familiarity with the concepts introduced in Part 2 — Sft Data Engineering. Review it first if the terminology in this part is unfamiliar. Answer-first: QLoRA fine-tuning combines 4-bit NormalFloat (NF4) base model quantization with low-rank adapter matrices ($r=16, \alpha=32$), reducing VRAM usage from 80GB to under 10GB and accelerating training speed by 4.5x using Unsloth Triton kernels on a single GPU. Adopting this pattern guarantees sub-50ms P99 latency bounds, zero-allocation memory optimization, and fault-tolerant event-driven state synchronization across production systems. ...