{"data":{"slug":"lora-paper","etype":"paper","name":"LoRA: Low-Rank Adaptation (2021)","cat":"Systems","subtitle":"Fine-tuning for the GPU-poor — tiny adapters instead of full weights.","summary":"Microsoft researchers showed models adapt through low-rank update matrices a thousandth the size of the weights — making fine-tuning accessible to anyone with one GPU and creating the adapter ecosystems around Llama and Stable Diffusion.","attrs":null,"kf":null,"aliases":["LoRA"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://arxiv.org/abs/2106.09685","label":"arXiv 2106.09685"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T06:34:57+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}