Foundation models (FoMos), referring to large-scale AI models, possess human-like capabilities and are able to perform competitively in the domain of human intelligence. The breakthrough in FoMos has inspired researchers to deploy such models in the sixth-generation (6G) mobile networks for automating a broad range of tasks in next-generation mobile applications. While the sizes of FoMos are reaching their peaks, their next phase is expected to focus on fine-tuning the models to specific downstream tasks. This inspires us to propose the vision of FoMo fine-tuning as a 6G service. Its key feature is the exploitation of existing parameter-efficient fine-tuning (PEFT) techniques to tweak only a small fraction of model weights for a FoMo to become customized for a specific task. To materialize the said vision, we survey the state-of-the-art PEFT and then present a novel device-edge fine-tuning (DEFT) framework for providing efficient and privacy-preserving fine-tuning services at the 6G network edge. The framework consists of the following comprehensive set of techniques: 1) Control of fine-tuning parameter sizes in different transformer blocks of a FoMo; 2) Over-the-air computation for realizing neural connections in DEFT; 3) Federated DEFT in a multi-device system by downloading a FoMo emulator or gradients; 4) On-the-fly prompt-ensemble tuning; 5) Device-to-device prompt transfer among devices. Experiments are conducted using pre-trained FoMos with up to 11 billion parameters to demonstrate the effectiveness of DEFT techniques. The article is concluded by presenting future research opportunities.

翻译：基础模型（FoMos）指大规模AI模型，具备类人智能能力，能在人类智能领域展现出竞争性表现。FoMos的突破性进展激励研究人员将该类模型部署至第六代（6G）移动网络，以实现下一代移动应用中广泛任务的自动化。当FoMos的规模达到巅峰后，其下一阶段预计将聚焦于针对特定下游任务的模型微调。这启发我们提出将FoMo微调作为6G服务的愿景。其核心特征在于利用现有参数高效微调（PEFT）技术，仅调整FoMo中极小部分模型权重，使其定制化适配特定任务。为实现上述愿景，我们调研了前沿PEFT技术，并提出一种新型设备-边缘微调（DEFT）框架，可在6G网络边缘提供高效且保护隐私的微调服务。该框架包含以下综合技术集合：1）控制FoMo不同Transformer模块中的微调参数量；2）利用空中计算实现DEFT中的神经连接；3）通过下载FoMo模拟器或梯度实现多设备系统中的联邦DEFT；4）即时提示集成调优；5）设备间的提示传输。实验采用预训练参数规模达110亿的FoMo验证了DEFT技术的有效性。本文最后展望了未来研究方向。