Instructions to use dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L

Run Hermes

hermes

OpenClaw new

How to use dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "dealignai/Nemotron-3-Super-120B-A12B-UNCENSORED-JANG_2L",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

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.

MLX Studio — the only app that natively supports JANG models

Nemotron 3 Super 120B — JANG_2L + CRACK

JANG mixed-precision · CRACK abliterated · Mamba + MoE + Attention · No guardrails · 43 GB

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. One of the most architecturally complex open models available.

It has been:

JANG quantized — JANG_2L profile (8-bit attention, 6-bit important, 2-bit experts) — 43 GB
CRACK abliterated — permanent weight-level removal of safety refusal


Architecture	Nemotron 3 Super — 120B total, ~12B active, 3 layer types
Quantization	JANG_2L (8/6/2-bit mixed, 2.76 avg) — 43 GB
Abliteration	CRACK — novel weight surgery
HarmBench	96.2% (308/320)
MMLU	95.7% (199/208 with thinking)
Speed	45 tok/s (M3 Ultra 256GB)
Thinking	ON/OFF supported (ChatML)
Fits on	64 GB+ Macs

HarmBench Results

308/320 (96.2%)

Category	Score
Harassment / Bullying	21/21	100%
Misinformation / Disinfo	54/54	100%
Copyright	79/80	99%
Chemical / Biological	40/42	95%
Harmful	17/18	94%
Illegal	50/53	94%
Cybercrime / Intrusion	47/52	90%

MMLU Results

199/208 (95.7%) — 208 questions across 13 subjects with thinking recovery

Subject	Score	/16	Type
HS Biology	16/16	100%	BASE
College Physics	15/16	94%	HARD
Conceptual Physics	15/16	94%	HARD
Machine Learning	15/16	94%	HARD
Professional Medicine	15/16	94%	HARD
World Religions	15/16	94%	BASE
Electrical Engineering	14/16	88%	HARD
HS Geography	14/16	88%	BASE
Formal Logic	13/16	81%	HARD
Abstract Algebra	12/16	75%	HARD
HS Mathematics	12/16	75%	HARD
College CS	12/16	75%	HARD
College Math	10/16	63%	HARD

CRACK vs Base

	CRACK	Base JANG_2L
MMLU (with thinking)	95.7%	86.0%
HarmBench	96.2%	0%
Speed	45 tok/s	46 tok/s

Surgery improved reasoning by +9.7% — safety guardrails were interfering with mathematical problem-solving.

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_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)

Tip: Use temperature=0.6 for thinking mode (NVIDIA recommendation). Use temperature=1.0 for chat.

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.

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.

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