QLoRA With Unsloth — Faster Fine-Tuning With Memory Efficiency

I will implement QLoRA fine-tuning using Unsloth — achieving 2x faster training and 70% less memory usage compared to standard Hugging Face PEFT QLoRA implementation, through Unsloth's optimised triton kernels and memory-efficient attention implementation. Unsloth's optimisations are particularly valuable for QLoRA on consumer hardware — the faster training speed reduces the clock time for experiments that would otherwise take days on a single GPU, and the additional memory savings enable slightly larger models or batch sizes than standard QLoRA on the same hardware.

The implementation covers Unsloth installation and configuration for your hardware, model loading via Unsloth's fast loading, QLoRA adapter configuration, training execution with Unsloth's optimised trainer, speed and memory comparison against standard Hugging Face implementation, and adapter delivery.