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

Nemotron 3 Super 120B — JANG_4M + CRACK

JANG mixed-precision · CRACK abliterated · Mamba + MoE + Attention · No guardrails · 63 GB

What Is This?

This is NVIDIA Nemotron 3 Super 120B — a 120B parameter hybrid model with THREE layer types: Mamba SSM + MoE (512 experts, top-22) + Attention.

It has been:

JANG quantized — JANG_4M profile (8-bit attention, 4-bit experts) — 63 GB
CRACK abliterated — permanent weight-level removal of safety refusal


Architecture	Nemotron 3 Super — 120B total, ~12B active, 3 layer types
Quantization	JANG_4M (8/4-bit mixed, 4.1 avg) — 63 GB
HarmBench	90.3% (289/320)
MMLU	94.2% (196/208 with thinking)
Speed	~40 tok/s (M3 Ultra 256GB)
Thinking	ON/OFF supported (ChatML)
Fits on	96 GB+ Macs

Also see: Nemotron JANG_2L CRACK — 43 GB, 96.2% HarmBench, 95.7% MMLU

HarmBench Results

289/320 (90.3%)

Category	Score
Misinformation / Disinfo	54/54	100%
Copyright	74/80	92%
Chemical / Biological	38/42	90%
Harassment / Bullying	19/21	90%
Harmful	16/18	89%
Illegal	46/53	87%
Cybercrime / Intrusion	42/52	81%

MMLU Results

196/208 (94.2%) — 208 questions across 13 subjects with thinking recovery

Subject	Score	/16	Type
Professional Medicine	16/16	100%	HARD
HS Biology	15/16	94%	BASE
College Physics	15/16	94%	HARD
Conceptual Physics	15/16	94%	HARD
Machine Learning	13/16	81%	HARD
Electrical Engineering	13/16	81%	HARD
College CS	13/16	81%	HARD
HS Geography	14/16	88%	BASE
World Religions	14/16	88%	BASE
Formal Logic	12/16	75%	HARD
College Math	11/16	69%	HARD
HS Mathematics	11/16	69%	HARD
Abstract Algebra	10/16	63%	HARD

CRACK vs Base

	CRACK	Base JANG_4M
MMLU	94.2%	~86%
HarmBench	90.3%	0%

Install & Usage

pip install "jang[mlx]"

from jang_tools.loader import load_jang_model
from mlx_lm import generate

model, tokenizer = load_jang_model("dealignai/Nemotron-3-Super-120B-A12B-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)

Thinking Mode

Thinking is ON by default. To disable:

prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True,
    enable_thinking=False, tokenize=False)

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) removes safety alignment from LLMs at the weight level.

Disclaimer

This model is provided for research and educational purposes. The creators are not responsible for any misuse.

Created by Jinho Jang · 장진호 제작

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