{
  "id": "fine-tuning",
  "code": "PTL-0105",
  "term": "Fine-Tuning",
  "aliases": [
    "model fine-tuning",
    "transfer learning"
  ],
  "category": "foundations",
  "definition": "Fine-tuning is the further training of a pretrained model on a smaller, task- or domain-specific dataset so that its weights adapt to that task or domain.",
  "description": "Fine-tuning complements prompting. Prompting changes only the input, while fine-tuning changes the model, which can teach a consistent format, style, or specialized skill that is hard to obtain from instructions alone, at the cost of training data, compute, and a separate model to maintain. Parameter-efficient methods such as LoRA update only a small number of added weights, and instruction tuning is the fine-tuning step that turns a base model into an assistant.",
  "example": null,
  "broader": [],
  "narrower": [
    "knowledge-distillation"
  ],
  "related": [
    "base-model",
    "instruction-tuning",
    "low-rank-adaptation",
    "prompt-tuning"
  ],
  "introduced": 2018,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "Universal Language Model Fine-tuning for Text Classification",
      "authors": "Howard and Ruder",
      "year": 2018,
      "url": "https://arxiv.org/abs/1801.06146"
    },
    {
      "title": "LoRA: Low-Rank Adaptation of Large Language Models",
      "authors": "Hu et al.",
      "year": 2021,
      "url": "https://arxiv.org/abs/2106.09685"
    }
  ],
  "url": "https://protologue.com/t/fine-tuning/",
  "citation": "Protologue. (2026). Fine-Tuning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0105). https://protologue.com/t/fine-tuning/"
}