标题： 基于CPU的LoRA微调：一种面向大语言模型的高效元生成框架 显示英文标题

标题： LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

摘要： 低秩适配器（LoRAs）通过实现参数高效的更新，改变了大型语言模型（LLMs）的微调。然而，它们的广泛应用仍受到对基于GPU训练依赖的限制。在本工作中，我们提出了一种理论上有依据的LoRA微调方法，专门针对计算资源有限的用户，特别是那些仅限于标准笔记本电脑CPU的用户。我们的方法学习了一个元操作符，通过利用Mistral-7B-Instruct-v0.2模型的大量预训练适配器，将任何输入数据集（表示为概率分布）映射到一组LoRA权重。而不是进行新的基于梯度的更新，我们的流程直接在CPU上通过现有LoRAs的轻量级组合构建适配器。虽然生成的适配器不如基于GPU训练的适配器性能好，但它们在下游任务中始终优于基础Mistral模型，为传统基于GPU的微调提供了一种实用且易于访问的替代方案。 显示英文摘要

摘要： Low-Rank Adapters (LoRAs) have transformed the fine-tuning of Large Language Models (LLMs) by enabling parameter-efficient updates. However, their widespread adoption remains limited by the reliance on GPU-based training. In this work, we propose a theoretically grounded approach to LoRA fine-tuning designed specifically for users with limited computational resources, particularly those restricted to standard laptop CPUs. Our method learns a meta-operator that maps any input dataset, represented as a probability distribution, to a set of LoRA weights by leveraging a large bank of pre-trained adapters for the Mistral-7B-Instruct-v0.2 model. Instead of performing new gradient-based updates, our pipeline constructs adapters via lightweight combinations of existing LoRAs directly on CPU. While the resulting adapters do not match the performance of GPU-trained counterparts, they consistently outperform the base Mistral model on downstream tasks, offering a practical and accessible alternative to traditional GPU-based fine-tuning.