peft-fine-tuning

Fine-tune large language models with limited GPU memory using parameter-efficient methods like LoRA and QLoRA. These adapters update small layer-wise parameters, dramatically reducing compute and memory requirements for 7B–70B models.

LoRA and QLoRA are parameter-efficient fine-tuning methods for memory optimization. QLoRA adds quantization via bitsandbytes to further reduce memory overhead, while LoRA applies small trainable adapters to base model layers.

Multi-adapter serving allows rapid task swapping across domains without full-model retraining. The HuggingFace PEFT library supports loading multiple adapters to a single base model for flexible deployment.

PEFT fine-tuning relies on the HuggingFace PEFT library, Transformers ecosystem, and PyTorch. You must install peft, transformers, and torch, with optional bitsandbytes for quantization support.

Apply a LoRA configuration to a 7B base model by installing the peft package and wrapping the model with a LoRA adapter setup. This integrates directly with the HuggingFace Transformers ecosystem to streamline deployment.

Parameter-efficient fine-tuning is not ideal when you need full model weight updates rather than small adapter parameters. If GPU memory is not a constraint, standard fine-tuning may preserve performance better for complex domain shifts.