I built a local AI assistant fine‑tuned on my own conversations

说明与用途

JERN 是一个本地 AI 助手，基于 1.5B Qwen 模型，在个人对话上微调。它在用户机器上运行，访问本地文件和 shell，并使用工具循环实现安全执行。

问题

Most assistants run on remote servers, ignore personal files, and sound generic. I needed a private, local assistant that understands my voice, can access my files and shell, and is small enough to run on a single GPU.

结果

JERN provides a fully local AI assistant that can understand the user’s conversational style, access local files, execute shell commands, and use a tool loop for safe interactions. It runs on a modest GPU and can also leverage a free Colab GPU via SSH tunnel.

工作流

• 1. Clone the JERN repo from GitHub.
• 2. Install dependencies with pip.
• 3. Run python main.py to start the FastAPI server and web UI.
• 4. The model downloads the base Qwen model and fine‑tunes it on a text file of personal conversations (takes minutes on GPU).
• 5. The assistant answers user queries, emits tool calls (files, shell, web search, etc.), the server executes them locally, and returns results to the model.
• 6. Optionally, run python main.py colab --gpu=T4 --serve to use a free Colab GPU while keeping tools local via SSH tunnel.

证据

I wanted an AI assistant that is actually mine: it runs on my machine, knows how I talk, and can use my files and shell.

来源

Everything runs on my machine with one command: python main.py opens the web UI at http://127.0.0.1:8000.

来源

The training data is one text file, data/chat/conversations.txt, with [N] conversations I wrote: how JERN introduces itself, how it should talk, and examples of when to call a tool.

来源

Built-in tools cover files, shell, web search, downloads and system info.

来源

Because the tools are local, JERN can read my real files and run real commands. That is useful and also a risk, so review what it wants to run before you approve it.

来源