The field of Artificial Intelligence has witnessed remarkable progress in
recent years, especially with the emergence of powerful large language models
(LLMs) based on the transformer architecture. Cloud-based LLMs, such as
OpenAI's ChatGPT, offer impressive capabilities but come with concerns
regarding latency and privacy due to network dependencies. This article
presents an innovative approach to LLM inference, envisioning a future where
LLMs with billions of parameters can be executed directly on mobile devices
without network connectivity. The article showcases a fine-tuned GPT LLM with 3
billion parameters that can operate smoothly on devices with as low as 4GB of
memory. Through the integration of native code and model quantization
techniques, the application not only serves as a general-purpose assistant but
also facilitates seamless mobile interactions with text-to-actions features.
The article provides insights into the training pipeline, implementation
details, test results, and future directions of on-device LLM inference. This
breakthrough technology opens up possibilities for empowering users with
sophisticated AI capabilities while preserving their privacy and eliminating
latency concerns.

AI 领域近年来取得了显著的进展，尤其是基于变压器架构的强大大型语言模型（LLMs）的出现。本文介绍了一种创新的 LLM 推理方法，展望了在无需网络连接的情况下，拥有数十亿参数的 LLMs 可以直接在移动设备上执行的未来。该应用程序不仅作为一个通用助手，而且通过原生代码和模型量化技术的结合，还可以实现与文本到动作功能的无缝移动交互。文章还提供了关于本地 LLM 推理的训练流程、实现细节、测试结果和未来方向的见解。这一突破性技术为用户提供了强大的人工智能能力，同时保护了用户的隐私并消除了延迟问题。