Alpaca-LoRA

Alpaca-LoRA is a research project for instruction-tuning Meta’s original LLaMA models efficiently on consumer hardware. It reproduces the Stanford Alpaca approach using Low-Rank Adaptation instead of updating every model parameter. Hugging Face PEFT and bitsandbytes reduce the memory and compute needed for fine-tuning. The repository includes training scripts, inference tools, prompt templates, datasets, and checkpoint export utilities. Provided workflows were designed to fine-tune models such as LLaMA 7B on a single high-end consumer GPU. Generated LoRA adapters can be loaded alongside the original base model for instruction-following inference. The project became an influential early reference for parameter-efficient LLM fine-tuning and remains useful for studying LoRA workflows.