NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4

Instructions to use TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4

SGLang

How to use TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 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 "TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4" \
    --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": "TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
		"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 "TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4" \
        --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": "TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/TitanML/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4
```

NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 / privacy.md

jamesdborin

Duplicate from nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4

6c06e7e 2 months ago

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Field	Response
Generatable or reverse engineerable personal data?	No
Personal data used to create this model?	No
Was consent obtained for any personal data used?	Not Applicable
A description of any methods implemented in data acquisition or processing, if any, to address the prevalence of personal data in the training data, where relevant and applicable.	We used only prompts that do not contain any personal data for synthetic data generation.
How often is the dataset reviewed?	Before Release
Is there provenance for all datasets used in training?	Yes
Does data labeling (annotation, metadata) comply with privacy laws?	Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?	No, not possible with externally-sourced data.
Applicable Privacy Policy	NVIDIA Privacy Policy
During AI model development, strict adherence to copyright policy ensured compliance through risk mitigation and legal reviews. Post-data collection, reserved rights content is identified and removed, with verified opt-out processes for rightsholders. Detailed records document due diligence and transparency.	True
We employ automated tools and data processing techniques during data preparation to identify and filter certain categories of personal information. Scans of training datasets detected no PII.	True. We employ automated tools and data processing techniques to scan for Personally Identifiable Information (PII) during data preparation to identify and filter certain categories of personal information, including phone numbers, email addresses, credit card numbers, and public-facing contact details. Scans of Common Crawl, CC-News, and Wikimedia datasets did not detect PII in the majority of samples; however, Microsoft Presidio indicated potential findings including business contact information embedded in natural language, such as email addresses and phone numbers. These were removed using verified instances of PII through a combination of automated filtering and human-in-the-loop validation. In contrast, scans of financial reasoning datasets, including NVIDIA-created and web-scraped datasets, via Presidio Analyzer, indicated false positives such as numerical sequences, and did not indicate any verified instances of PII. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy.
Privacy Testing:	Constrained to English-language inputs. Multi-lingual parity is not currently claimed or guaranteed.