{
  "schemaVersion": 1,
  "topic": "LoRA Rank and Adapter Anatomy",
  "checked_at": "2026-09-07",
  "claims": [
    {
      "id": "L1",
      "title": "Learn a low-rank update",
      "claim": "The original LoRA paper freezes W0 and learns A and B with an additive BA update, scaled by alpha/r.",
      "url": "https://arxiv.org/html/2106.09685",
      "checked_at": "2026-09-07",
      "type": "research-paper",
      "classification": "specified-formulation",
      "boundary": "No universal quality or memory-saving factor is transferred from the paper\u2019s experiments."
    },
    {
      "id": "L2",
      "title": "Implementation variants matter",
      "claim": "PEFT documents rank, target modules, alpha, bias options, initialization, and variants; default A is initialized and B starts at zero.",
      "url": "https://huggingface.co/docs/peft/en/developer_guides/lora",
      "checked_at": "2026-09-07",
      "type": "primary-documentation",
      "classification": "specified-formulation",
      "boundary": "This page selects the original formulation rather than modeling every PEFT option."
    }
  ],
  "assumptions": [
    "Every selected target has identical d\u00d7k shape.",
    "FP16 base payload; independently selectable adapter storage precision.",
    "Original alpha/r scaling, not rank-stabilized LoRA or DoRA.",
    "Visual matrix blocks are schematic, not area-scaled.",
    "Changing alpha affects scaling, not parameter count."
  ],
  "evidenceClasses": {
    "measuredFacts": "No hardware, model-quality, or corpus measurements are claimed.",
    "derivedResults": "Deterministic arithmetic or editorial rule evaluation; see formulas in README.md.",
    "editorialAssumptions": "Dated synthetic examples, stated scope, and storage policies.",
    "primaryFacts": "Each sourced claim carries its own classification and boundary."
  }
}