# Mobile Device

Lora is a local language model optimized for mobile devices, which can be quickly integrated into mobile applications via its SDK. Supporting iOS and Android platforms, its performance is comparable to GPT-4o-mini, featuring a 1.5GB size and 2.4 billion parameters, and is specifically optimized for real-time mobile inference. Lora’s key advantages include low energy consumption, lightweight design, and rapid response times. Compared to other models, it demonstrates significant advantages in energy consumption, size, and speed. Provided by PeekabooLabs, Lora primarily targets developers and enterprise clients, helping them quickly integrate advanced language model capabilities into mobile applications to enhance user experience and application competitiveness.

Mobile-Agent is an autonomous multi-modal mobile device agent that leverages Multi-Modal Large Language Model (MLLM) technology. Firstly, it utilizes visual perception tools to accurately recognize and locate visual and textual elements on the front-end interface of applications. Based on the perceived visual environment, it autonomously plans and decomposes complex operational tasks and navigates mobile applications through step-by-step operations. Unlike previous solutions that relied on application-specific XML files or mobile system metadata, Mobile-Agent's vision-centric approach offers greater adaptability in various mobile operational environments, eliminating the need for customization to specific systems. To evaluate the performance of Mobile-Agent, we introduced Mobile-Eval, a benchmark for evaluating mobile device operations. Based on Mobile-Eval, we conducted a comprehensive evaluation of Mobile-Agent. Experimental results show that Mobile-Agent achieved significant accuracy and completion rates. Even with challenging instructions, such as multi-app operations, Mobile-Agent was still able to fulfill the requirements.