{
  "id": 9068351,
  "title": "QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation",
  "url": "https://urgent.news/2026/09/21/qlora-fine-tuning-of-ministral-llm-for-sequence-to-function-protein",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-21T13:09:59.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.24538v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far faster than the capacity for manual curation. Most computational approaches consider annotation as multi-label classification over a fixed ontology, which constrains predictions to a predefined label set. In this work we study the the protein…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}