{
  "@context": "https://schema.org",
  "@type": "TechArticle",
  "@id": "https://anchorfact.org/kb/parameter-efficient-fine-tuning",
  "headline": "Parameter-Efficient Fine-Tuning: LoRA, QLoRA, and Adapters",
  "description": "Parameter-efficient fine-tuning (PEFT) adapts large pre-trained models to new tasks by training only a small fraction of parameters. LoRA and QLoRA have democratized LLM customization — GPT-equivalent quality fine-tuning now runs on consumer hardware.",
  "dateCreated": "2026-05-24T02:49:13.649Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "LoRA: Low-Rank Adaptation of Large Language Models",
      "sameAs": "https://arxiv.org/abs/2106.09685"
    },
    {
      "@type": "CreativeWork",
      "name": "QLoRA: Efficient Finetuning of Quantized Language Models",
      "sameAs": "https://arxiv.org/abs/2305.14314"
    }
  ]
}