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本文作者提出了LORA低资源训练方法，让普通玩家有了微调大模型的可能。 更重要的是作者用大量实验和理论分析为我们讲解LORA微调背后的原理，让我们能够更加容易掌握和理解LORA微调过程。 StableDiffusion超详细训练原理讲解+实操教学，LORA参数详解与训练集处理技巧，【倾囊相授】c站排行前十五炼丹师教你养赛博女儿，作者亲自讲解：LoRA 是什么？ LoRA（低秩适应）是一种高效的大模型微调方法，通过冻结原始参数、训练低秩增量矩阵来减少计算开销。本文详解LoRA原理、超参数设置（rank、alpha、dropout）及工程实现，包括Transformer层应用和HuggingFace PEFT实战。适合LLM微调开发者优化训练效率。
文章浏览阅读1.2k次，点赞30次，收藏27次。LoRA（Low-Rank Adaptation）是一种参数高效微调技术，通过冻结预训练模型参数，仅对低秩矩阵进行增量训练，显著降低训练和存储成本。文章详细解析了LoRA的原理、训练步骤、与传统微调的对比及在Transformer中的应用。LoRA特别适合大规模模型微调、多任务切换. QLoRA是LoRA的进阶版，核心优化是： 先对预训练模型进行量化（如4bit），再在量化模型上添加LoRA模块。 量化能大幅降低原模型的显存占用，LoRA保持参数量精简，两者结合实现“超低显存微调”。 LoRA 是一种技术，它允许高效地微调模型，而只需更新模型权重的一小部分。 当您有一个在大型数据集上预训练的大型模型，但希望在较小的数据集上或针对特定任务进行微调时，这非常有用。
LoRA的核心思想是，在冻结预训练模型权重后，将可训练的低秩分解矩阵注入到的Transformer架构的每一层中，从而大大减少了在下游任务上的可训练参数量。
LoRA 是一种神经网络优化技术，它通过添加低秩矩阵来提高模型在处理特定任务时的性能，增强其自适应性，而无需对神经网络进行大量的重新训练。
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