SpatialPEFT: a parameter-efficient fine-tuning framework for spatial transcriptomics foundation models

SpatialPEFT is a unified parameter-efficient fine-tuning framework that enables the robust adaptation of large spatial transcriptomics foundation models (up to 1.4 billion parameters) on a single 16 GB consumer-grade GPU. By integrating Low-Rank Adaptation (LoRA), gradient checkpointing, and a spatial-aware adapter, it reduces peak VRAM by over 87% while substantially improving downstream spatial annotation accuracy.

SpatialPEFT is implemented in Python and released under the MIT license. The source code, documentation, and tutorials are freely available at https://github.com/applerplay/SpatialPEFT, with an archival snapshot deposited at Zenodo (DOI: 10.5281/zenodo.20725321).