{
  "id": "lora-forward",
  "title": "LoRA linear layer",
  "kind": "implement",
  "summary": "Forward pass of a linear layer with a low rank adapter, merge of the adapter into the weight, and the trainable parameter count.",
  "difficulty": "easy",
  "tags": ["lora", "fine-tuning", "linear-algebra"],
  "lesson": "phases/11-llm-engineering/08-fine-tuning-lora",
  "cheatsheet": "lora-fine-tuning",
  "minutes": 15,
  "entry": ["lora_forward", "merge_lora", "count_trainable"],
  "collections": ["llm-inference"],
  "time_limit_s": 10,
  "mutants": {
    "alpha_not_divided": "Scales the adapter path by alpha and forgets to divide by the rank.",
    "count_product": "Counts r times in_features times out_features, the size of a full matrix product, not of A and B.",
    "merge_in_place": "Adds the adapter into W in place, so the caller's frozen weight changes.",
    "rank_from_w": "Reads the rank from the number of rows of W, not from the number of rows of A.",
    "rank_over_alpha": "Uses r / alpha as the merge scale, the inverse of the correct scale."
  }
}