---
language:
- en
library_name: mlx
license: other
base_model: nvidia/Nemotron-3-Super-120B-A12B
tags:
- jang
- quantized
- mixed-precision
- apple-silicon
- mlx
- moe
- mamba
- abliterated
- uncensored
- crack
pipeline_tag: text-generation
thumbnail: dealign_mascot.png
---
> **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.
---
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](https://huggingface.co/nvidia/Nemotron-3-Super-120B-A12B) — a 120B parameter hybrid model with THREE layer types: Mamba SSM + MoE (512 experts, top-22) + Attention.
It has been:
1. **JANG quantized** — JANG_4M profile (8-bit attention, 4-bit experts) — **63 GB**
2. **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](https://huggingface.co/dealignai/Nemotron-3-Super-120B-A12B-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
```bash
pip install "jang[mlx]"
```
```python
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:
```python
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
---
## Disclaimer
This model is provided for research and educational purposes. The creators are not responsible for any misuse.
---
Created by Jinho Jang · 장진호 제작
---
## 한국어
### Nemotron 3 Super 120B — JANG_4M + CRACK
| 항목 | 내용 |
|------|------|
| 크기 | 63 GB |
| HarmBench | 90.3% (289/320) |
| MMLU | 94.2% (196/208) |
| 속도 | ~40 tok/s (M3 Ultra) |
| 최소 요구사양 | 96 GB 메모리 Mac |
```bash
pip install "jang[mlx]"
```
[GitHub](https://github.com/jjang-ai/jangq) · [HuggingFace](https://huggingface.co/JANGQ-AI) · [MLX Studio](https://mlx.studio) · [Ko-fi](https://ko-fi.com/jangq) · [X @dealignai](https://x.com/dealignai)
---
Created by Jinho Jang · 장진호 제작