Hugging Face PEFT
The adapter library underneath most of the others. Maximum control.
90
sovereigntyPEFT — 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.
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST
LicenseApache-2.0
PricingFree, Apache-2.0.
Open sourceYes
Self-hostableYes
Local-first dataYes
What it does well
- +The reference implementation of the adapter methods
- +Composes naturally with Transformers, Datasets and Accelerate
- +Adapter files are small and trivially portable
- +Apache-2.0 with a large maintained ecosystem
Where it falls short
- −A library, not a workflow — you build the training loop
- −No UI, no config-file convenience layer
- −More decisions land on you than with Axolotl
Hugging Face PEFT as an alternative to
Where Hugging Face PEFT shows up in our comparisons, and how it ranked.
Hugging Face PEFT head-to-head
Straight comparisons against the tools people weigh it against.