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      "statement": "# Dettmers and colleagues: QLoRA quantized adapter fine-tuning\n\nFine-tuned low-rank adapters through frozen four-bit model weights, combining NormalFloat storage, double quantization and paged optimizers to reduce memory use.\n\n## Scientific scope\n\nQuantized storage is dequantized for higher-precision computation. Quality retention and memory figures are empirical for the studied tasks; QLoRA builds on the already catalogued LoRA method.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nTim Dettmers; Artidoro Pagnoni; Ari Holtzman; Luke Zettlemoyer\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314v1)\n- [NeurIPS 2023: QLoRA proceedings record](https://proceedings.neurips.cc/paper_files/paper/2023/hash/1feb87871436031bdc0f2beaa62a049b-Abstract-Conference.html)\n",
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