---
title: "Fine-Tuning Biological Foundation Models with LoRA Using NVIDIA BioNeMo Recipes"
url: https://daily.dev/posts/fine-tuning-biological-foundation-models-with-lora-using-nvidia-bionemo-recipes-veoulundb
source_url: https://developer.nvidia.com/blog/fine-tuning-biological-foundation-models-with-lora-using-nvidia-bionemo-recipes
type: article
source: "NVIDIA Developer"
published: 2026-06-15T18:11:04.908Z
updated: 2026-06-17T16:20:16.702Z
tags: ["ai", "lora"]
reading_time: 12
upvotes: 1
comments: 0
language: en
---

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# Fine-Tuning Biological Foundation Models with LoRA Using NVIDIA BioNeMo Recipes

**[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 12 min read · 1 upvotes · 0 comments

## Summary

NVIDIA BioNeMo Recipes provides step-by-step workflows for fine-tuning large biological foundation models using LoRA (Low-Rank Adaptation), enabling parameter-efficient adaptation on a single workstation GPU. Two case studies are presented: ESM2-3B (protein language model) fine-tuned for protein secondary structure prediction achieving accuracy competitive with state-of-the-art baselines (Q3: 84.80%, Q8: 74.30%), and Evo2-1B (DNA language model) fine-tuned for splice-site classification, jumping from 52.3% (head-only) to 96.6% accuracy while training only ~1.4% of parameters. The recipes integrate NVIDIA Transformer Engine, Megatron-Bridge, and sequence packing (THD format) for ~5.5x throughput improvement. Both workflows complete in under one hour on an NVIDIA RTX 6000 Blackwell GPU.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developer.nvidia.com/blog/fine-tuning-biological-foundation-models-with-lora-using-nvidia-bionemo-recipes>

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---

Tags: [#ai](https://daily.dev/tags/ai), [#lora](https://daily.dev/tags/lora)

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