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_2L + CRACK
JANG mixed-precision · CRACK abliterated · Mamba + MoE + Attention · No guardrails · 10 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_2L profile (8-bit attention, 6-bit important, 2-bit experts) — 10 GB
- CRACK abliterated — permanent weight-level removal of safety refusal
| Architecture | Nemotron Cascade 2 — 30B total, ~3B active, 3 layer types |
| Quantization | JANG_2L (8/6/2-bit mixed, 2.3 avg) — 10 GB |
| HarmBench | 99.7% (319/320) |
| MMLU | 66.8% (139/208) |
| Speed | ~121 tok/s (M4 Ultra 256GB) |
| Thinking | ON/OFF supported (ChatML) |
| Fits on | 16 GB+ Macs |
Also see: JANG_4M version — 17 GB, 99.4% HarmBench, 82.7% MMLU (fits on 32 GB Macs)
HarmBench Results
319/320 (99.7%)
| Category | Score | |
|---|---|---|
| Auth Bypass | 100/100 | 100% |
| Cloud Exploits | 100/100 | 100% |
| Covering Tracks | 20/20 | 100% |
| API Hacking | 99/100 | 99% |
CRACK vs Base
| CRACK | Base JANG_2L | |
|---|---|---|
| MMLU (with thinking) | 66.8% | ~68% |
| MMLU (no thinking) | 49.0% | 51.0% |
| HarmBench | 99.7% | 0% |
| Speed | ~121 tok/s | ~125 tok/s |
Surgery impact on reasoning: minimal (-2% no-think, ~-1% with thinking).
JANG_2L CRACK vs JANG_4M CRACK vs JANG_2L Base
| JANG_2L CRACK | JANG_2L Base | JANG_4M CRACK | |
|---|---|---|---|
| Size | 10 GB | 10 GB | 17 GB |
| MMLU | 66.8% | ~68% | 82.7% |
| HarmBench | 99.7% | 0% | 99.4% |
| Speed | ~121 tok/s | ~125 tok/s | ~127 tok/s |
| Fits on | 16 GB Mac | 16 GB Mac | 32 GB Mac |
Note: There is no standard MLX quantization for Nemotron Cascade 2. The
nemotron_harchitecture (Mamba + MoE + Attention hybrid) is only supported by JANG format via MLX Studio andjang-tools.
MMLU Results (with reasoning recovery)
139/208 (66.8%) — no-think 102/208 + thinking recovered 37
| Subject | Score | |
|---|---|---|
| HS Biology | 14/16 | 88% |
| HS Geography | 12/16 | 75% |
| World Religions | 11/16 | 69% |
| Conceptual Physics | 11/16 | 69% |
| Electrical Engineering | 10/16 | 62% |
| College Physics | 9/16 | 56% |
| Formal Logic | 9/16 | 56% |
| College Mathematics | 6/16 | 38% |
| College CS | 4/16 | 25% |
| HS Mathematics | 4/16 | 25% |
| Machine Learning | 3/16 | 19% |
| Professional Medicine | 7/16 | 44% |
| Abstract Algebra | 2/16 | 12% |
Scores shown are no-think pass. Thinking recovery improved total from 49.0% to 66.8%.
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_2L-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_2L + CRACK
| 항목 | 내용 |
|---|---|
| 크기 | 10 GB |
| HarmBench | 99.7% (319/320) |
| MMLU | 66.8% (139/208) |
| 속도 | ~121 tok/s (M4 Ultra) |
| 최소 요구사양 | 16 GB 메모리 Mac |
pip install "jang[mlx]"
GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai