JANGQ-AI/Mistral-Small-4-119B-A6B-JANG_2L

MLX Studio — the only app that natively supports JANG models with reasoning

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Mistral Small 4 (119B-A6B) — JANG_2L (2.14-bit) — Reasoning + VLM

JANG — Jang Adaptive N-bit Grading | The GGUF Equivalent for MLX

     

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

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First Mistral Small 4 (119B) on Apple Silicon. MLA attention + 128 MoE experts + Pixtral VLM. 5x faster prefill than MLX Community 4-bit.

Reasoning mode: Set reasoning_effort to "high" for step-by-step reasoning with [THINK]...[/THINK] tags.

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Speed Comparison — JANG vs MLX Community

Model	Size	Gen tok/s	Prefill tok/s	RAM	Fits On
JANG_2L (this model)	30 GB	82	216	40 GB	48 GB Macs
JANG_4M	57 GB	80	202	68 GB	96+ GB Macs
JANG_6M	84 GB	74	160	95 GB	128+ GB Macs
MLX Community 4-bit	63 GB	84	43	68 GB	96+ GB Macs

• 5x faster prefill (216 vs 43 tok/s)
• Half the size (30 GB vs 63 GB) at comparable generation speed
• Benchmarked on M3 Ultra 256 GB with bfloat16 compute

Key Features

• 82 tok/s generation on M3 Ultra — matches MLX 4-bit at half the size
• 30 GB on disk, 40 GB peak RAM — fits 48 GB Macs (M4 Pro, M2/M3 Max)
• Vision (VLM): Pixtral encoder, 1540px max, processes images
• Reasoning mode: [THINK]...[/THINK] step-by-step reasoning
• Code generation: Complete functions with docstrings and optimized logic
• Math: Step-by-step calculations with distributive property
• 119B total / 6B active per token — MLA attention + 128 MoE experts

Architecture

JANG_2L Bit Allocation

Tensor Type	Bits	Purpose
Attention (q/k/v/o projections)	8	Critical — preserves MLA precision
Embeddings, lm_head	8	Critical — token representation
MoE gate (router)	16	Float16 passthrough — routing precision
Shared experts	6	Important — always active
Routed experts (128)	2	Compressed — many experts = redundancy
Norms, biases	full	Float — tiny tensors, keep exact

Results: 94.0% MMLU at 2-bit (200 Questions, Reasoning Mode)

Subject	Score
Abstract Algebra	13/20 (65%)
Anatomy	20/20 (100%)
Astronomy	20/20 (100%)
College CS	20/20 (100%)
College Physics	19/20 (95%)
HS Biology	19/20 (95%)
HS Chemistry	20/20 (100%)
HS Mathematics	18/20 (90%)
Logical Fallacies	19/20 (95%)
World Religions	20/20 (100%)
Total	188/200 (94.0%)

Five perfect 100% subjects. 119B intelligence in 30 GB at 82 tok/s.

Requirements

• MLX Studio for native JANG support with reasoning
• Or: - Apple Silicon Mac with 48+ GB unified memory

Install

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Created by Jinho Jang — jangq.ai — @dealignai