📄 中文摘要
集成 Cursor 与本地 LLM（LM Studio）和 GitHub Copilot 的过程包括几个步骤。首先，确保安装了 Cursor 和 LM Studio，并可选地安装 ngrok。接下来，下载一个或多个本地模型，如 Gemma2、Llama3 或 DeepSeekCoder。第一部分重点在于设置 LM Studio 引擎并通过 ngrok 将其暴露为 API，以便 Cursor 可以使用。用户需根据操作系统下载相应的 LM Studio 安装包，安装后启动应用程序。通过这些步骤，可以提升编码效率，实现更好的开发体验。
📄 English Summary
Use Cursor with LM Studio
Integrating Cursor with local LLMs (LM Studio) and GitHub Copilot involves several steps. First, ensure that Cursor and LM Studio are installed, along with the optional ngrok. Additionally, download one or more local models such as Gemma2, Llama3, or DeepSeekCoder. The first part focuses on setting up the LM Studio engine and exposing it as an API via ngrok for Cursor to utilize. Users need to download the appropriate LM Studio installation package for their operating system, install it, and launch the application. These steps aim to enhance coding efficiency and provide a better development experience.
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数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等