Foundation models are reshaping computational biology. Pretrained on massive corpora of protein or genomic sequences, models such as ESM2 (a protein language model) and Evo 2 (a DNA language model) capture statistical regularities of biological sequences. These transfer well to a wide range of downstream tasks, including structure prediction, variant effect, and functional annotation.

Yet adapting these models to a specific task is nontrivial: at billions of parameters, full fine-tuning quickly becomes impractical, both in compute and storage of optimizer state and checkpoints.

Low-Rank Adaptation (LoRA) directly addresses this challenge.…