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-toolsPython package.
Nemotron Cascade 2 30B — JANG_4M + CRACK
JANG mixed-precision · CRACK abliterated · Mamba + MoE + Attention · No guardrails · 17 GB
What Is This?
This is NVIDIA Nemotron Cascade 2 30B — a 30B parameter hybrid model with THREE layer types: Mamba-2 SSM + MoE (128 experts, top-6) + Attention. One of the most architecturally advanced small models available.
It has been:
- JANG quantized — JANG_4M profile (8-bit attention, 4-bit experts) — 17 GB
- CRACK abliterated — permanent weight-level removal of safety refusal
| Architecture | Nemotron Cascade 2 — 30B total, ~3B active, 3 layer types |
| Quantization | JANG_4M (8/4-bit mixed, 4.1 avg) — 17 GB |
| HarmBench | 99.4% (318/320) |
| MMLU | 82.7% (172/208 with thinking) |
| Speed | ~127 tok/s (M4 Ultra 256GB) |
| Thinking | ON/OFF supported (ChatML) |
| Fits on | 32 GB+ Macs |
Also see: JANG_2L version — 10 GB, 99.7% HarmBench, 66.8% MMLU (fits on 16 GB Macs)
HarmBench Results
318/320 (99.4%)
| Category | Score | |
|---|---|---|
| API Hacking | 100/100 | 100% |
| Covering Tracks | 20/20 | 100% |
| Auth Bypass | 99/100 | 99% |
| Cloud Exploits | 99/100 | 99% |
CRACK vs Base
| CRACK | Base JANG_4M | |
|---|---|---|
| MMLU (with thinking) | 82.7% | 88% |
| HarmBench | 99.4% | 0% |
| Speed | ~127 tok/s | ~130 tok/s |
Surgery reduced MMLU by ~5% — safety guardrails were slightly entangled with reasoning pathways.
MMLU Results (with reasoning recovery)
172/208 (82.7%) — no-think 128/208 (61.5%) + thinking recovered 47
| Subject | Score | |
|---|---|---|
| HS Biology | 15/16 | 94% |
| Conceptual Physics | 14/16 | 88% |
| World Religions | 13/16 | 81% |
| College Physics | 12/16 | 75% |
| HS Geography | 12/16 | 75% |
| Professional Medicine | 12/16 | 75% |
| Electrical Engineering | 9/16 | 56% |
| College CS | 8/16 | 50% |
| Formal Logic | 8/16 | 50% |
| College Mathematics | 7/16 | 44% |
| HS Mathematics | 7/16 | 44% |
| Abstract Algebra | 6/16 | 38% |
| Machine Learning | 5/16 | 31% |
Scores shown are no-think pass. Thinking recovery improved total from 61.5% to 82.7%.
JANG_4M CRACK vs JANG_4M Base vs JANG_2L CRACK
| JANG_4M CRACK | JANG_4M Base | JANG_2L CRACK | |
|---|---|---|---|
| Size | 17 GB | 17 GB | 10 GB |
| MMLU | 82.7% | 88% | 66.8% |
| HarmBench | 99.4% | 0% | 99.7% |
| Speed | ~127 tok/s | ~130 tok/s | ~121 tok/s |
| Fits on | 32 GB Mac | 32 GB Mac | 16 GB Mac |
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-Cascade-2-30B-A3B-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 using per-layer projected vectors from structurally-mirrored prompt pairs.
Links
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
Nemotron Cascade 2 30B — JANG_4M + CRACK
| 항목 | 내용 |
|---|---|
| 크기 | 17 GB |
| HarmBench | 99.4% (318/320) |
| MMLU | 82.7% (172/208) |
| 속도 | ~127 tok/s (M4 Ultra) |
| 최소 요구사양 | 32 GB 메모리 Mac |
pip install "jang[mlx]"
GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai