{
  "schema_version": "1.8.0",
  "rubric_version": "1.2",
  "license": "CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)",
  "record": {
    "id": "K-0027",
    "slug": "lora-adapter",
    "title": "LoRA adapter",
    "aliases": [
      "LoRA",
      "low-rank adaptation",
      "low rank adaptation",
      "LoRA adapters"
    ],
    "status": "published",
    "last_reviewed": "2026-10-27",
    "review_interval_days": 90,
    "steward": null,
    "provenance": {
      "drafted_by": "ai",
      "reviewed_by": [
        "codex-review"
      ]
    },
    "risk_flags": [],
    "flags": [],
    "one_liner": "A set of compact, low-rank weight updates learned while a model's original weight matrices stay fixed.",
    "sources": [
      {
        "source": "S-0077",
        "supports": "frozen base weight matrices; trainable low-rank updates; merging or separately applying updates; switching tasks",
        "locator": "§4.1–4.2"
      }
    ],
    "related": [
      "K-0025",
      "K-0019"
    ],
    "type": "concept",
    "url": "https://trustbutveri.fyi/concepts/lora-adapter/",
    "source_file": "content/concepts/lora-adapter.md",
    "flags_all": [],
    "body_markdown": "A low-rank adaptation (LoRA) adapter contains compact updates to a pretrained model's weight matrices. LoRA represents each update using two smaller matrices. Training learns those matrices while keeping the original weight matrix fixed [[S-0077]].\n\nAt inference time, the update can be applied alongside the original weights or merged into them. Different adapters let the same base model serve different tasks without storing a separate copy of all its weights for each task [[S-0077]].",
    "body_text": "A low-rank adaptation (LoRA) adapter contains compact updates to a pretrained model's weight matrices. LoRA represents each update using two smaller matrices. Training learns those matrices while keeping the original weight matrix fixed [S-0077]. At inference time, the update can be applied alongside the original weights or merged into them. Different adapters let the same base model serve different tasks without storing a separate copy of all its weights for each task [S-0077].",
    "referenced_by": []
  }
}