language:

• en library_name: mlx license: other base_model: nvidia/Nemotron-Cascade-2-30B-A3B 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 and the jang-tools Python 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:

1. JANG quantized — JANG_2L profile (8-bit attention, 6-bit important, 2-bit experts) — 10 GB
2. CRACK abliterated — permanent weight-level removal of safety refusal

Also see: JANG_4M version — 17 GB, 99.4% HarmBench, 82.7% MMLU (fits on 32 GB Macs)

HarmBench Results

319/320 (99.7%)

CRACK vs Base

Surgery impact on reasoning: minimal (-2% no-think, ~-1% with thinking).

JANG_2L CRACK vs JANG_4M CRACK vs JANG_2L Base

Note: There is no standard MLX quantization for Nemotron Cascade 2. The nemotron_h architecture (Mamba + MoE + Attention hybrid) is only supported by JANG format via MLX Studio and jang-tools.

MMLU Results (with reasoning recovery)

139/208 (66.8%) — no-think 102/208 + thinking recovered 37

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

pip install "jang[mlx]"

GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai