huggingface/peft is a parameter-efficient fine-tuning (Parameter-Efficient Fine-Tuning, PEFT) library that additionally trains only some of the parameters of large pretrained models to adapt them to new tasks. The latest release visible on GitHub is v0.21.2. However, the provided release information contains no list of changes for this version, so what changed cannot be determined from this information alone.
The intent of this library is to reduce both computational cost and storage cost by updating only some weights, as with LoRA, instead of retraining all weights. The repository's README states that the latest PEFT techniques can deliver performance on a par with full fine-tuning. It integrates with Transformers, Diffusers, and Accelerate to support model training and inference, adapter management, and distributed training and inference. It is a Python project licensed under Apache-2.0, has 21817 GitHub stars, and the repository was started on 2022-11-25.
However, there are points that remain unverified. What changed in v0.21.2 can only be known by reading the release notes directly, and the index computed for this repository consists of a single momentum index (momentum) of 3.1, with no freshness, technology, or quality scores given. Performance comparisons or the spread of adoption cases cannot be used as grounds, nor should the version number alone be read as meaning that new features were shipped. Until the changes are confirmed, the practical significance of this release cannot be judged.