Fine-Tuning LLMs on Consumer Hardware: LoRA Guide 2024

LoRA and QLoRA enable parameter-efficient fine-tuning of 7B-70B LLMs on consumer GPUs, eliminating the need for A100 hardware. LoRA freezes the base model, training only small adapter matrices to reduce trainable parameters by 100-1000x. QLoRA further optimizes this via 4-bit quantization and paged optim, allowing 7B models to run on 6GB-8GB VRAM. These methods provide fast training, small checkpoint files, and the ability to merge adapters back into the base model for specific domain adaptation.