Machine learningDeep Learning, Language Models, Parameter Efficient Fine-Tuning
QLoRA
QLoRA 是一种由 Dettmers 等人于 2023 年提出的高效微调方法，它通过量化和低秩适配（low-rank adaptation）实现了对大型语言模型的微调。通过结合 4 位量化与 LoRA，QLoRA 将内存需求降低了 75%，使得在单个 GPU 上微调拥有 650 亿参数的模型成为可能。
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来源
- Dettmers, T., Pagnoni, A., Holtzman, A., & Contrastive, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. arXiv preprint arXiv:2305.14314. link ↗
如何引用本页
ScholarGate. (2026, June 3). Efficient Finetuning of Quantized LLMs. ScholarGate. https://scholargate.app/zh/deep-learning/qlora
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