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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。 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。 本文深入剖析了LoRA模型，它是用于微调大型语言模型的低秩适应技术，在Stable Diffusion模型应用中很受欢迎。 介绍了其工作原理、查找和使用方法，还给出使用注意事项，并列举了一些LoRA模型示例，具有高效、灵活等优点。
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。 It listens for lora signals from all the end devices within its range. 例如，企业模型迭代时，全参数微调每个版本可能占用800GB，而LoRA只需加载几兆大小的任务特定文件即可。 快速任务切换：在不同垂直领域或场景中，可随时加载不同的LoRA文件进行切换，无需重新训练。
LORA (Low-Rank Adaptation，低秩适应)是一种模型的轻量 微调 方法，通过向模型的部分层添加可训练的低秩矩阵模块，实现模型在特定任务上的能力调整，同时保持原模型参数不变。
以下是一个LoRA模型详解分析，从下图可以看到，该模型只有144MB，相比SD模型至少2GB起步，LORA确实算得上是小模型，非常适合硬件资源受限的用户。 最近在用LoRA微调一些开源大模型，在学习LoRA时，发现大部分文章只是对起理论做了解释，但是具体到代码的实现却少得可怜。 作为一名实践主义者，遂想把其底层实现细节，用文章的方式记录下来，即是方便以后复习，也供网上读者参考。 LoRa（Long Range）通信是一种能跑得远、耗能低、成本还不高的无线通信技术。 它不是用来传输高清视频的，而是用来解决“远、慢、省”的物联网需求，比如环境监测、农业传感器、智能抄表等。 Each device has a lora chip that allows it to transmit small packets of data over long distances using the lora radio protocol
A gateway is a receiving station that is connected to the internet
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