Deep learning· 2023
QLoRA: Efficient Finetuning of Quantized LLMs
DOI 10.52202/075280-0441arXiv 2305.14314OpenAlex W7133233472
On the reading lists
- #136 in the Top 500 (2026 edition)
- #20 in Deep learning (2026 edition)
Why it matters
Combined 4-bit quantization with LoRA, democratizing fine-tuning of very large models.
Condensed from the expert lists' notes.
The record
- First released
- 2023 (from arXiv 2305.14314)
- Citations
- 6,016 citations, as counted by Papers With Code (CC BY-SA 4.0)
- Named by
- both expert lists
- Also relevant to
- Language, speech & LLMs
- Score
- 0.615 · computed rank 136 · consensus 0.79, must-read 0.5, citations percentile 0.938 (2020s · deep-learning), awards 0, existing lists 0. Method
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.