Text Generation
MLX
Safetensors
English
nemotron_h
nemotron
mamba
mamba2
mixture-of-experts
6bit
quantized
apple-silicon
conversational
reasoning
lm-studio
custom_code
6-bit
Instructions to use FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit 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("FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit") 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
- Pi
How to use FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit 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 "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit"
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 FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit"
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 "FaisalFehad/Nemotron-3-Super-120B-A12B-MLX-6bit" \ --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"
| Field | Response |
|---|---|
| Model Application Field(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning, Customer Service |
| Describe the life critical impact (if present). | Not Applicable |
| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from training. |
| Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | We used a Gemma-3 4B-based guard model trained on Nemotron Content Safety Dataset v2 for content safety to exclude potentially illegal or harmful content from the training. |
| Use Case Restrictions: | Abide by the NVIDIA Nemotron Open Model License Agreement. |
| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use Nemotron Content Safety Dataset V2 and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |