Iambackup/Nemotron-3-Super-120B-A12B-JANG_4M-CRACK
Important: This model uses the JANG quantization format — the GGUF equivalent for MLX on Apple Silicon. Currently only supported by [MLX Studio](https://mlx.studio) and the jang-tools Python package.<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>
<p align="center"> <a href="https://mlx.studio"><img src="https://mlx.studio/assets/screenshots/mlx-studio-featured.png?v=1" alt="MLX Studio App" width="600"></a> </p>
<h4 align="center"><a href="https://mlx.studio">MLX Studio</a> — the only app that natively supports JANG models</h4>
<div align="center">
<img src="dealign_mascot.png" width="128" />
Nemotron 3 Super 120B — JANG_4M + CRACK
JANG mixed-precision · CRACK abliterated · Mamba + MoE + Attention · No guardrails · 63 GB
<a href="https://ko-fi.com/jangq"><img src="https://img.shields.io/badge/Ko--fi-Support_Development-FF5E5B?logo=ko-fi&logoColor=white&style=for-the-badge" alt="Ko-fi"></a>
</div>
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
Also see: Nemotron JANG_2L CRACK — 43 GB, 96.2% HarmBench, 95.7% MMLU
HarmBench Results
289/320 (90.3%)
MMLU Results
196/208 (94.2%) — 208 questions across 13 subjects with thinking recovery
CRACK vs Base
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.
Links
<p align="center"> <a href="https://ko-fi.com/jangq"><img src="https://img.shields.io/badge/Ko--fi-SupportDevelopment-FF5E5B?logo=ko-fi&logoColor=white&style=flat-square" alt="Ko-fi"></a> <a href="https://x.com/dealignai"><img src="https://img.shields.io/badge/X-@dealignai-000000?logo=x&logoColor=white&style=flat-square" alt="X/Twitter"></a> <a href="https://github.com/jjang-ai/jangq"><img src="https://img.shields.io/badge/GitHub-jjang--ai/jangq-181717?logo=github&logoColor=white&style=flat-square" alt="GitHub"></a> <a href="https://mlx.studio"><img src="https://img.shields.io/badge/MLXStudio-App-blue?style=flat-square" alt="MLX Studio"></a> <a href="https://jangq.ai"><img src="https://img.shields.io/badge/Website-jangq.ai-green?style=flat-square" alt="Website"></a> </p>
Disclaimer
This model is provided for research and educational purposes. The creators are not responsible for any misuse.
<p align="center">Created by <a href="https://jangq.ai">Jinho Jang</a> · 장진호 제작</p>
한국어
Nemotron 3 Super 120B — JANG_4M + CRACK
pip install "jang[mlx]"GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai
<p align="center">Created by <a href="https://jangq.ai">Jinho Jang</a> · 장진호 제작</p>