GitHub - ammaarreshi/gemma-chat: Local AI chat + coding agent for Apple Silicon, powered by Gemma 4 via MLX / Supports Ollama

Vibe code without the internet.
A local coding agent powered by Google's Gemma 4 — runs entirely on your Mac via Apple's MLX framework.
No API keys. No cloud. No Wi-Fi required.

The Idea

What if you could vibe code from an airplane? Or a cabin with no cell signal? Or just... without sending your code to someone else's server?

Gemma Chat is an open-source Electron app that runs Gemma 4 natively on Apple Silicon. You describe what you want to build, and it writes the code — HTML, CSS, JavaScript, multi-file projects — with a live preview that updates as the model types. No internet connection needed after the initial model download.

It's a proof-of-concept for fully offline, local-first vibe coding using a small open model. The model is ~3 GB. The whole thing runs on your laptop.

How It Works

Describe what you want to build — "A retro calculator app" or "A landing page for a coffee shop"
Watch it code — Gemma writes files character-by-character with a live preview
Iterate — Ask for changes, it edits the files and the preview updates in real-time

Everything happens locally. The model runs via MLX-LM, Apple's framework for running LLMs on Apple Silicon. Your code, your prompts, your conversations — all on your machine.

Features

🛠 Build Mode — Coding agent with a live preview canvas. Writes multi-file projects into a sandboxed workspace.
💬 Chat Mode — Conversational AI with tool use (web search, URL fetch, calculator, bash).
🔄 Model Switching — Hot-swap between 4 Gemma variants on the fly.
🎤 Voice Input — Local speech-to-text via in-browser Whisper.
✈️ Works Offline — After the one-time model download, everything runs without internet.
💾 Zero Config — Python venv + MLX runtime auto-provisions on first launch.

Available Models

Model	Size	Best For
Gemma 4 E2B	~1.5 GB	Fast Q&A, simple tasks
Gemma 4 E4B	~3 GB	Recommended. Speed + capability balance
Gemma 4 27B MoE	~8 GB	Stronger reasoning (needs 16 GB+ RAM)
Gemma 4 31B	~18 GB	Maximum quality (needs 32 GB+ RAM)

Getting Started

Requirements: macOS on Apple Silicon, Python 3.10–3.13, Node 20+.

git clone https://github.com/ammaarreshi/gemma-chat-public.git
cd gemma-chat-public
npm install
npm run dev

First launch will auto-detect Python → create a venv → install MLX-LM → download the model (~3 GB) → ready to vibe code.

Tip: Install Python via Homebrew if you don't have it: brew install python@3.13

Building a Distributable

Produces a signed .dmg in dist/. Share it directly — recipients just drag to Applications.

Tech Stack

Layer	Tech
App Shell	Electron + Vite + React 19 + TypeScript + Tailwind
Model Runtime	MLX-LM (auto-installed into a local venv)
Speech-to-Text	transformers.js (Whisper, runs in-browser via WASM)
Workspace	Per-conversation sandboxed filesystem + local HTTP server

Architecture

src/
├── main/              Electron main process
│   ├── index.ts       Window + IPC + agent loop
│   ├── mlx.ts         MLX-LM venv install / server lifecycle / chat streaming
│   ├── workspace.ts   Per-conversation workspace + static file server
│   └── tools.ts       Tool definitions + system prompts + XML action parser
├── preload/           contextBridge API surface
├── renderer/src/
│   ├── components/
│   │   ├── Setup.tsx      First-run onboarding + download progress
│   │   ├── Chat.tsx       Main layout + model switcher
│   │   ├── Canvas.tsx     Preview / Code / Files tabs (Build mode)
│   │   ├── Message.tsx    Chat bubbles + tool cards + activity bar
│   │   ├── Composer.tsx   Input + mic button
│   │   └── Sidebar.tsx    Conversation list
│   └── lib/whisper.ts     Browser Whisper pipeline
└── shared/types.ts    IPC types + model registry

Under the Hood

Agent Loop — In Build mode, each assistant turn streams tokens from the local MLX server. XML <action> blocks are parsed from the stream, executed (file writes, bash commands, etc.), and results are fed back for the next turn. Up to 40 rounds per user message.

Live Streaming — As the model generates file content, partial writes are flushed to disk every ~450ms. The preview iframe reloads in real-time so you watch the page build itself.

Tool Protocol — Small models handle XML more reliably than JSON function calling, so tools are invoked via an XML-based format:

<action name="write_file">
<path>index.html</path>
<content>
<!doctype html>
...
</content>
</action>

Credits

Gemma by Google DeepMind
MLX by Apple Machine Learning Research
transformers.js by Hugging Face

Created by @ammaar and AI :)

License

MIT