peft-fine-tuning

You can fine-tune large language models with limited GPU memory using PEFT adapters like LoRA and QLoRA. This approach trains only a tiny fraction of parameters, dramatically reducing compute and memory requirements for models up to 70B.

LoRA applies parameter-efficient adapters to train a fraction of model weights, while QLoRA combines this with quantization via bitsandbytes to further reduce memory usage. Both enable rapid experimentation on large-scale models within constrained hardware environments.

Yes, multi-adapter serving allows you to deploy and switch between task-specific adapters without retraining. This lets you load one base model and dynamically swap PEFT adapters for different downstream tasks.

PEFT fine-tuning requires CUDA-enabled hardware and the PEFT, transformers, and PyTorch libraries. Optional bitsandbytes integration provides efficient memory usage through quantization for training 7B-70B parameter models.

To start training, install the required packages, load your base model using transformers, configure a LoRA adapter, and initiate training on your dataset. This enables rapid experimentation with minimal hardware requirements.