Instructions to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM", dtype="auto")

llama-cpp-python

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM",
	filename="Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16
# Run inference directly in the terminal:
./llama-cli -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

Use Docker

docker model run hf.co/Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

LM Studio
Jan

vLLM

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

SGLang

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with Ollama:
```
ollama run hf.co/Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16
```

Unsloth Studio

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM to start chatting

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

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 Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with Docker Model Runner:
```
docker model run hf.co/Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16
```

Lemonade

How to use Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM:BF16

Run and chat with the model

lemonade run user.Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-BF16

List all available models

lemonade list

ELBAZ NVIDIA-NEMOTRON-3-NANO-30B PRISM (UNCENSORED)

Model Release Date: December 18, 2025

Model Description

This model is an abliterated (uncensored) version of nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 that has had its refusal mechanisms removed using PRISM (Projected Refusal Isolation via Subspace Modification). The model will respond to prompts that the original model would refuse.

Key Specs: The model employs a hybrid Mixture-of-Experts (MoE) architecture, consisting of 23 Mamba-2 and MoE layers, along with 6 Attention layers. Each MoE layer includes 128 experts plus 1 shared expert, with 6 experts activated per token. The model has 3.5B active parameters and 30B parameters in total.

31.58B parameter hybrid architecture
52-layer design (Mamba-2 + MoE + Attention)
1M token context length (1,048,576)
BF16 precision
Text generation with reasoning capabilities

The supported languages include: English, German, Spanish, French, Italian, and Japanese. Improved using Qwen.

This model is ready for commercial use.

Motivation

This project exists as research and development experimentation into understanding how large language models encode and enforce refusal behaviors, contributing to broader AI safety research by providing empirical data on refusal mechanism localization and tradeoffs between safety and capability.

Author

Eric Elbaz (Ex0bit)

Model Tree

nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (Base Model)
└── Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM (This Model)
    ├── Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-BF16.gguf
    ├── Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-Q8_0.gguf
    ├── Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-Q6_K.gguf
    └── Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-IQ4_XS.gguf

Available Quantizations

Quantization	Size	Description
BF16	63 GB	Full precision, best quality
Q8_0	34 GB	8-bit, near-lossless quality
Q6_K	34 GB	6-bit k-quant, excellent quality
IQ4_XS	18 GB	Importance-weighted 4-bit, great quality/size ratio

The IQ4_XS quantization uses importance-weighted quantization which provides better quality than standard Q4 quantizations at similar sizes.

Prompt Format

This model uses the Nemotron chat format with thinking/reasoning support:

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_prompt}<|im_end|>
<|im_start|>assistant

Template Structure

Component	Token/Format
System Start	`<\|im_start\|>system`
User Start	`<\|im_start\|>user`
Assistant Start	`<\|im_start\|>assistant`
End of Turn	`<\|im_end\|>`
Thinking Start	`<think>`
Thinking End	`</think>`

Quick Start

Using with llama.cpp

# Download the model
huggingface-cli download Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM \
    Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-IQ4_XS.gguf \
    --local-dir .

# Run inference
./llama-cli -m Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-IQ4_XS.gguf \
    -p "<|im_start|>user
Your prompt here<|im_end|>
<|im_start|>assistant
" \
    -n 2048 \
    --temp 0.7 \
    -ngl 999

llama.cpp with llama-server

# Start the server
./llama-server -m Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM-IQ4_XS.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 999 \
    -c 32768

# Example API call
curl http://localhost:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "messages": [
            {"role": "user", "content": "Your prompt here"}
        ],
        "temperature": 0.7
    }'

Using with Ollama

# Pull and run directly from Hugging Face
ollama pull hf.co/Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM
ollama run hf.co/Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM

Note: The hf.co/ prefix is required to pull from Hugging Face. Requires Ollama 0.3.0+.

PRISM Methodology

Method: Projected Refusal Isolation via Subspace Modification

The model was abliterated using PRISM - a state-of-the-art abliteration methodology combining multiple principled techniques for effective refusal removal while preserving model capabilities.

Hardware Requirements

Quantization	Min VRAM	Recommended	Hardware Examples
IQ4_XS	12 GB	16+ GB	RTX 4090, A100, Apple M2/M3/M4 Pro/Max
Q6_K	24 GB	32+ GB	RTX 4090, A100 40GB, Apple M3/M4 Max
Q8_0	24 GB	32+ GB	RTX 4090, A100 40GB, Apple M3/M4 Max
BF16	64 GB	80+ GB	A100 80GB, H100, Multi-GPU setups

Note: The IQ4_XS quantization runs well on consumer hardware with 16GB+ VRAM.

Ethical Considerations

This model has been modified to reduce safety guardrails. Users are responsible for:

Complying with all applicable laws and regulations
Not using the model for illegal activities
Understanding the potential risks of unrestricted AI responses
Implementing appropriate safeguards in production environments

License

NVIDIA Open Model License (same as base model nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16)

Citation

@misc{elbaz2025nemotronprism,
  author = {Elbaz, Eric},
  title = {Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM: An Abliterated Nemotron Hybrid Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Ex0bit/Elbaz-NVIDIA-Nemotron-3-Nano-30B-A3B-PRISM}}
}