Download

Running in a minute.

Python 3.10 or later is the only requirement. MAFDET installs llama.cpp and the models by itself, with a progress bar for every download. Current version: v0.4.1, release notes on GitHub.

With a double-click

  1. Download MAFDET: on GitHub, Code → Download ZIP, and unzip it (or git clone).
  2. Double-click start_mafdet.bat. The first time it creates a Python environment in .venv and installs MAFDET, about a minute. Needs Python 3.10 or later.
  3. After that, a double-click opens MAFDET in under a second. After an update it installs the new libraries by itself.

From PyPI

With Python from python.org, pip works as it is.

$ pip install mafdet
$ mafdet

Runs on NVIDIA through CUDA (reference: RTX 2060, 6 GB), on AMD Radeon through Vulkan (in place, not measured yet), or on the CPU.

Either way: MAFDET asks whether to open the window or the terminal app, looks at your machine, installs the right build of llama.cpp (CUDA, Vulkan, Metal or CPU) and downloads the model that runs best. No PyTorch, no manual downloads.

Requirements

Built for the hardware most people own.

Windows

An NVIDIA GPU with up-to-date drivers (tested target: RTX 2060, 6 GB), an AMD Radeon through Vulkan (in place, not measured yet), or just a CPU.

Tested

macOS

Apple Silicon (M1 or later): the models run on the GPU through Metal, in the unified memory. Tested on an M2 Pro with 16 GB, macOS 26.2: Qwen3-4B reads 593 tokens/s and writes 57. Intel Macs run on the CPU.

Tested

Linux

The llama.cpp builds and code paths are in place (CUDA, Vulkan, CPU), but nobody has run it end to end yet. Tell us how it went.

Not tested yet

Python

3.10 or later (tested with 3.12). No PyTorch: llama.cpp runs the models, transformers only provides the tokenizers.

Disk

About 0.7 GB for llama.cpp with CUDA (100 MB with Vulkan, 60 MB for the CPU), plus the models: from 0.6 GB (Qwen3-0.6B) to 2.3 GB for the 4B recommended on 6-8 GB cards.

Memory

16 GB of RAM recommended for CPU-only runs and for models larger than the VRAM. Models larger than the VRAM run partly on the CPU: it works, but slower, and less reasoning fits before Enter.

The first start

What happens when you open it.

  1. 01

    Window or terminal? The launcher asks, and can remember your choice (mafdet --ask shows it again).

  2. 02

    Your machine. MAFDET looks at the GPU, the memory and the CPU, and installs the right build of llama.cpp: CUDA for an NVIDIA card, Vulkan for a Radeon, Metal on an Apple Silicon Mac, CPU otherwise.

  3. 03

    The model. The catalog says what every model does on your hardware before you download it, and marks the one that runs best. Qwen3-4B is the pick on a 6-8 GB card.

  4. 04

    Chat. Start typing: the status line shows what the model reads and thinks while you write. Every answer ends with its numbers.