m-courtyard

Name: m-courtyard
Author: Mcourtyard

Verified

M-Courtyard: Local AI Model Fine-tuning Assistant for Apple Silicon. Zero-code, zero-cloud, privacy-first desktop app powered by Tauri + React + mlx-lm.

153stars

14forks

TypeScript

Installation

# Add to your Claude Code skills
git clone https://github.com/Mcourtyard/m-courtyard

Getting Started

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Security ReportVerified

Last scanned: 5/30/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-30T16:19:27.032Z",
  "npmAuditRan": true,
  "pipAuditRan": true
}

README.md

Frequently Asked Questions

What is m-courtyard?

m-courtyard is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Mcourtyard. M-Courtyard: Local AI Model Fine-tuning Assistant for Apple Silicon. Zero-code, zero-cloud, privacy-first desktop app powered by Tauri + React + mlx-lm. It has 153 GitHub stars.

Is m-courtyard safe to use?

Yes. m-courtyard passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install m-courtyard?

Clone the repository with "git clone https://github.com/Mcourtyard/m-courtyard" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is m-courtyard written in?

m-courtyard is primarily written in TypeScript. It is open-source under Mcourtyard on GitHub, so you can review or fork the full source.

Are there alternatives to m-courtyard?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh m-courtyard against similar tools.

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M-Courtyard

Zero-code local LLM fine-tuning & data prep on Apple Silicon. Privacy-first, powered by MLX.

English | 简体中文

Why M-Courtyard?

M-Courtyard is a desktop assistant designed to demystify LLM fine-tuning. Forget about writing Python scripts, managing CUDA dependencies, or renting expensive cloud GPUs. If you have an Apple Silicon Mac, you can build your own custom AI locally.

Zero-Code Pipeline: From raw PDF/DOCX files to local datasets, MLX fine-tuning, and exportable local runtimes in 4 easy steps.
100% Local & Private: No data leaves your machine. Perfect for fine-tuning on sensitive enterprise data or personal journals.
Optimized for Apple MLX: Powered by mlx-lm, maximizing the potential of unified memory on M1/M2/M3/M4 chips.
AI-Powered Data Prep: Automatically turn unstructured documents into high-quality instruction datasets using local models, or fall back to built-in rules when you do not want AI generation.

Latest Update (v0.5.6)

macOS Tahoe + MLX Training Stability: M-Courtyard now automatically sets AGX_RELAX_CDM_CTXSTORE_TIMEOUT=1 for training subprocesses to mitigate the upstream MLX / macOS Tahoe Metal watchdog regression that can crash LoRA runs with kIOGPUCommandBufferCallbackErrorImpactingInteractivity.
Clearer Recovery Guidance: Smart Alerts now recognize this Metal watchdog signature and explain the fallback path if it still appears on Tahoe.

Features

Automated Data Preparation

Multi-format Import: Drag & drop .txt, .pdf, .docx.
Smart Segmentation: Automatically clean and chunk documents.
AI Dataset Generation: Use local Ollama models to generate Knowledge Q&A, Style Imitation, or Instruction Training datasets.
Built-in Rules Mode: Generate datasets without any external runtime when you prefer a fully self-contained workflow.

Effortless Fine-tuning (LoRA)

Unified Model Hub: Auto-detect local HuggingFace / ModelScope / Ollama assets, or pull the latest models online (Qwen, DeepSeek, GLM, Gemma, Llama, GPT-OSS, etc.).
Live Visuals: Real-time training loss charts, ETA, and resource monitoring.
Presets: 1-click configurations (Quick / Standard / Thorough) for different needs.

Test & Export

Built-in Chat: Test your fine-tuned adapter instantly.
One-Click Ollama Export: Merge, quantize (Q4/Q8/F16), and export straight to Ollama. Play with your model immediately.
MLX Export for Local Runtimes: Export fused MLX models that can be used with mlx-lm.server and loaded in LM Studio on Apple Silicon.

Local Runtime Support

mlx-lm is the core engine: training and built-in inference are powered by Apple MLX rather than Ollama.
Ollama is currently optional but recommended: it is used for Ollama-based AI dataset generation and one-click Ollama export.
LM Studio is supported as a parallel local runtime: use its local OpenAI-compatible server for AI dataset generation, or load exported MLX models there on Apple Silicon.
Built-in rules remain available with no extra runtime: if you do not want to install Ollama or LM Studio, you can still generate datasets with the built-in rules path.

Interface Tour

1. Data Preparation

Import documents, auto-clean, and generate training datasets using local LLMs.

2. Model Training

Real-time loss curves, ETA, and progress tracking powered by Apple MLX.

3. Testing & Export

Instantly chat with your fine-tuned model and export it either to Ollama or as MLX assets for LM Studio / local MLX workflows.

Requirements

OS: macOS 14+ (Sonoma or later)
Chip: Apple Silicon (M1 / M2 / M3 / M4 series)
RAM: 16 GB+ recommended (for 7B/8B models); 8 GB works for small models (1.5B/3B)
Core Runtime: M-Courtyard guides the local uv / Python / mlx-lm setup inside the app
Optional Local Runtime: Ollama installed and running if you want Ollama-based AI dataset generation or Ollama export
Optional Local Runtime: LM Studio if you want LM Studio-based AI dataset generation or to load exported MLX models there
No extra runtime required: the built-in rules path can generate datasets without Ollama or LM Studio

Quick Start

Download the Pre-built App (Recommended)

Go to Releases and download the latest .dmg.
Open the .dmg and drag M-Courtyard.app to your Applications folder.
Open Terminal and run this command to allow the app to run (since it's not code-signed yet):
```
sudo xattr -rd com.apple.quarantine /Applications/M-Courtyard.app
```
Launch M-Courtyard from Applications!

Prerequisites:

Node.js 18+ & pnpm
Rust toolchain
Xcode Command Line Tools (xcode-select --install)

# 1. Clone the repo
git clone https://github.com/Mcourtyard/m-courtyard.git
cd m-courtyard/app

# 2. Install dependencies
pnpm install

# 3. Development mode
pnpm tauri dev

# OR: Production build
pnpm tauri build

Tech Stack

Frontend: React 19 + TypeScript + TailwindCSS v4 + Vite + Zustand
Desktop Framework: Tauri 2.x (Rust)
AI Core: mlx-lm (Apple MLX), local Python venv managed automatically
Storage: SQLite + local filesystem

Community & Support

Join our community to share your fine-tuned models, get help, or suggest features!

Discord — Live chat & support
GitHub Discussions — Feature ideas and Q&A
GitHub Issues — Bug reports

If M-Courtyard helps you build your local AI, please consider giving it a star on GitHub!

Support

If M-Courtyard saves you time, consider buying me a coffee — it helps keep the project alive! ☕

Chinese supporters can also use 爱发电 (WeChat Pay / Alipay supported).

License

M-Courtyard is open-source software licensed under the AGPL-3.0 License. For brand name and logo usage, see Brand and Logo Usage Notice. For commercial use or different licensing terms, please contact: tuwenbo0112@gmail.com