Verified65% confidenceFactTime unknown
使用LoRA微调时，实际需要训练的参数量只有原来的0.1%-1%
3
Sources
65%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Unsloth教程：本地微调大模型省显存加速训练指南
githubunslothai
Related Entities
Related Claims
Verified对于一个 70 亿参数的模型，LoRA 通常只需训练不到 1% 的参数量，显存需求可降低 60% 以上77% similarUnverifiedLoRA微调只需训练不到1%的参数即可达到接近全参微调的效果，显存需求大幅降低至单张消费级显卡可承受范围76% similarUnverified针对有限意图集合训练的轻量级专用模型参数量通常在10M以下，其推理延迟可比通用LLM调用低2-3个数量级70% similarUnverified在CIFAR-10上训练40个epoch的复现实验中，plain-56的训练准确率为84.0%，plain-20的训练准确率为95.1%，尽管plain-56参数量（853,018）约为plain-20（269,722）的三倍69% similar
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