Important: This model uses the JANG quantization format — the GGUF equivalent for MLX on Apple Silicon. Currently only supported by MLX Studio and the jang-tools Python package.

MLX Studio — the only app that natively supports JANG models

⚡ All JANG models are meant to be run in vMLX

Mistral Small 4 119B — JANG_4M + CRACK

JANG mixed-precision · CRACK abliterated · MLA Attention + MoE · Vision · No guardrails · 64 GB

Ko-fi

What Is This?

This is Mistral Small 4 119B — a 119B parameter MoE model with Multi-head Latent Attention (MLA), 128 experts (top-4 active), and built-in Pixtral vision.

It has been:

JANG quantized — JANG_4M profile (8-bit attention, 4-bit experts) — 64 GB
CRACK abliterated — permanent weight-level removal of safety refusal


Architecture	Mistral 4 MoE — 119B total, ~8B active, MLA + 128 experts
Quantization	JANG_4M (8/4-bit mixed, 4.1 avg) — 64 GB
HarmBench	95.3% (305/320)
MMLU	90.9% (189/208 with reasoning)
Compliance	8/8
Vision	Pixtral tensors included — VL via MLX Studio engine
Reasoning	ON/OFF supported (`reasoning_effort`)
Fits on	96 GB+ Macs

HarmBench Results

305/320 (95.3%)

Category	Score
Covering Tracks	20/20	100%
API Hacking	96/100	96%
Cloud Exploits	95/100	95%
Auth Bypass	94/100	94%

CRACK vs Base

	CRACK	Base JANG_4M
HarmBench	95.3%	0%
Coherence	6/6	6/6
Code	2/2	2/2

CRACK surgery preserves model quality while removing refusal (see metrics above and below).

MMLU Results (with reasoning recovery)

189/208 (90.9%) — no-think 156/208 (75.0%) + reasoning recovered 33

Subject	Score
HS Biology	16/16	100%
Electrical Engineering	14/16	88%
Conceptual Physics	14/16	88%
Professional Medicine	14/16	88%
HS Geography	14/16	88%
College Physics	13/16	81%
World Religions	13/16	81%
HS Mathematics	12/16	75%
College CS	11/16	69%
College Mathematics	10/16	62%
Machine Learning	10/16	62%
Abstract Algebra	9/16	56%
Formal Logic	8/16	50%

Scores shown are no-think pass. Reasoning recovery improved total from 75.0% to 90.9%.

CRACK vs Base

	CRACK	Base JANG_4M
MMLU (with reasoning)	90.9%	94%
HarmBench	95.3%	0%
Coherence	6/6	6/6
Speed	~45 tok/s	~48 tok/s

Surgery reduced MMLU by only 3.1% — minimal impact on reasoning capability.

---\n\n## Install & Usage

pip install "jang[mlx]"

python

from jang_tools.loader import load_jang_model
from mlx_lm import generate

model, tokenizer = load_jang_model("dealignai/Mistral-Small-4-119B-JANG_4M-CRACK")

messages = [{"role": "user", "content": "Your prompt here"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False)

response = generate(model, tokenizer, prompt=prompt, max_tokens=2000)
print(response)

Reasoning Mode

Reasoning is OFF by default. To enable:

python

prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True,
    tokenize=False, reasoning_effort="high")

The model reasons inside [THINK]...[/THINK] tags before answering.

About JANG

JANG (Jang Adaptive N-bit Grading) is a mixed-precision quantization format for Apple Silicon — the GGUF equivalent for MLX.

About CRACK

CRACK (Controlled Refusal Ablation via Calibrated Knockouts) is a weight-level intervention that removes safety alignment while preserving reasoning quality. The modification is permanently baked into the published weights — no LoRA, no fine-tuning, no system prompts.

Links

Ko-fi

X/Twitter

GitHub

Disclaimer

This model is provided for research and educational purposes. The creators are not responsible for any misuse. By downloading this model, you agree to use it responsibly and in compliance with applicable laws.

한국어

Mistral Small 4 119B — JANG_4M + CRACK

항목	내용
크기	64 GB
HarmBench	95.3% (305/320)
최소 요구사양	96 GB 메모리 Mac

pip install "jang[mlx]"

GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai

Created by Jinho Jang · 장진호 제작