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SpatialPEFT: A Parameter-Efficient Fine-Tuning Framework for Spatial Transcriptomics Foundation Models
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DOI：10.1093/bioinformatics/btag503.png)
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Abstract
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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.