QLoRA Fine-Tuning — Large Model Adaptation on Limited GPU Memory

I will run a QLoRA fine-tuning job for your large language model — configuring 4-bit NF4 quantisation via bitsandbytes, LoRA adapter configuration for the quantised model, gradient checkpointing for memory efficiency, and training execution on your GPU hardware. QLoRA enables fine-tuning of models that would otherwise be inaccessible on your hardware — a 13B model that requires 26GB VRAM for standard LoRA fine-tuning requires only 10GB with QLoRA's 4-bit quantisation, bringing large model fine-tuning within reach of a single consumer GPU.

The fine-tuning covers bitsandbytes 4-bit NF4 quantisation configuration, LoRA adapter setup for the quantised model, gradient checkpointing, training monitoring, evaluation, and delivery of the trained LoRA adapter weights.