torchtune vs Hugging Face PEFT

torchtune is PyTorch's own fine-tuning library, built as readable, hackable training recipes rather than a framework with a configuration language on top. Nothing is hidden behind abstraction layers, which makes it the right choice when you need to change how training works rather than what it trains on — custom loss functions, unusual data pipelines, research variations. It is maintained inside the PyTorch project, so its dependency story is unusually clean.

PEFT — Parameter-Efficient Fine-Tuning — is the Hugging Face library that implements LoRA, QLoRA, prefix tuning and related adapter methods directly against Transformers models. Several of the tools above use it internally. Reaching for it directly makes sense when you are integrating fine-tuning into an existing training pipeline rather than running a standalone job, because it is a library you call rather than a harness you run.