JANGQ-AI/Mistral-Small-4-119B-A6B-JANG_4M

<p align="center"> <a href="https://mlx.studio"><img src="https://raw.githubusercontent.com/jjang-ai/jangq/main/assets/mlx-studio-light.png" alt="MLX Studio" width="500"></a> </p>

<h4 align="center"><a href="https://mlx.studio">MLX Studio</a> — the only app that natively supports JANG models with reasoning</h4>

<p align="center"> <img src="https://raw.githubusercontent.com/jjang-ai/jangq/main/assets/jangq-logo-dark.png" alt="JANG" width="300"> </p>

<p align="center"> <a href="https://vmlx.net"><img src="vmlx-app.png" alt="vMLX — run JANG models on Apple Silicon" width="820"></a> </p>

<h3 align="center">⚡ All JANG models are meant to be run in <a href="https://vmlx.net">vMLX</a></h3>

<h3 align="center">Mistral Small 4 (119B-A6B) — JANG_4M (4.08-bit) — Reasoning + VLM</h3> <p align="center"><b>JANG</b> — Jang Adaptive N-bit Grading | The GGUF Equivalent for MLX</p>

<p align="center"> <a href="https://github.com/jjang-ai/jangq"><img src="https://img.shields.io/badge/GitHub-Source_Code-blue?logo=github" alt="GitHub"></a>&nbsp; <a href="https://pypi.org/project/jang/"><img src="https://img.shields.io/pypi/v/jang?label=PyPI&color=green" alt="PyPI"></a>&nbsp; <a href="https://jangq.ai"><img src="https://img.shields.io/badge/Web-jangq.ai-orange" alt="Website"></a>&nbsp; <a href="https://x.com/dealignai"><img src="https://img.shields.io/badge/X-@dealignai-black?logo=x" alt="X/Twitter"></a> </p>

JANG is fully open-source. Quantization engine and full commit history: github.com/jjang-ai/jangq. Created by Jinho Jang.

Supported apps: MLX Studio (full native support) and oMLX (PR #364). LM Studio, Ollama, and Inferencer do not yet support JANG.

Why JANG models?

Tools like mlx-lm, oMLX (oQ), and others can quantize models — but shipping a tool is the easy part. JANG models come from hundreds of hours of per-architecture testing: finding which layers break at which bit depths, which MoE routing survives quantization, which models need bfloat16 to avoid NaN. We don't just quantize — we convert, verify, benchmark, and publish every model with tested scores. No other project in the MLX ecosystem publishes pre-tested quantized models at this scale.

Speed Comparison

5x faster prefill than MLX Community (216 vs 43 tok/s). JANG_2L runs on 48 GB Macs at half the size.

Benchmarked on M3 Ultra 256 GB with bfloat16 compute.

Key Features

• —80 tok/s generation on M3 Ultra
• —57 GB on disk, 68 GB peak RAM
• —Vision (VLM): Pixtral encoder, 1540px max
• —Reasoning mode: [THINK]...[/THINK] step-by-step reasoning
• —Code generation: Complete functions with optimized logic
• —Math: Step-by-step calculations
• —119B total / 6B active — MLA attention + 128 MoE experts
• —First Mistral Small 4 on Apple Silicon with full MLA + MoE support

Architecture

119B total parameters, 6B active per token
- 36 layers, all MoE (128 experts, top-4 routing)
- MLA attention: kv_lora_rank=256, q_lora_rank=1024
- Pixtral vision: 24 layers, 1540px max
- Reasoning: [THINK]...[/THINK] with reasoning_effort control
- bfloat16 compute (auto-detected)

Benchmarks

MMLU benchmarks in progress — will be updated with per-subject scores and MLX 4-bit comparison.

Install

pip install jang[mlx]

Created by [Jinho Jang](mailto:eric@jangq.ai) — [jangq.ai](https://jangq.ai) — [@dealignai](https://x.com/dealignai)