Fine-Tuning Biological Foundation Models with LoRA Using NVIDIA BioNeMo Recipes | NVIDIA Technical Blog

What this post added

This post details the application of Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning of large biological foundation models (ESM2-3B and Evo2-1B) using NVIDIA BioNeMo Recipes. It demonstrates achieving state-of-the-art performance on protein secondary structure prediction and DNA splice-site classification with significantly reduced trainable parameters (~1%) and computational resources, specifically on a single NVIDIA RTX 6000 Blackwell Workstation Edition GPU. The post highlights the integration of Transformer Engine (TE) and sequence packing for throughput optimization, and provides code snippets for loading TE-compatible models with LoRA adapters.