Instructions to use nvidia/Nemotron-Content-Safety-Reasoning-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Nemotron-Content-Safety-Reasoning-4B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Nemotron-Content-Safety-Reasoning-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/Nemotron-Content-Safety-Reasoning-4B", dtype="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/Nemotron-Content-Safety-Reasoning-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-Content-Safety-Reasoning-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Nemotron-Content-Safety-Reasoning-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Nemotron-Content-Safety-Reasoning-4B

SGLang

How to use nvidia/Nemotron-Content-Safety-Reasoning-4B 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 "nvidia/Nemotron-Content-Safety-Reasoning-4B" \
    --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": "nvidia/Nemotron-Content-Safety-Reasoning-4B",
		"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 "nvidia/Nemotron-Content-Safety-Reasoning-4B" \
        --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": "nvidia/Nemotron-Content-Safety-Reasoning-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Nemotron-Content-Safety-Reasoning-4B with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-Content-Safety-Reasoning-4B
```

trebedea commited on Dec 3, 2025

Commit

0bddf7d

verified ·

1 Parent(s): 6edfd77

Added model metadata

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Files changed (1) hide show

README.md +25 -1

README.md CHANGED Viewed

@@ -1,3 +1,27 @@
 # Nemotron-Content-Safety-Reasoning-4B Overview
 ## Description
@@ -656,4 +680,4 @@ NVIDIA believes Trustworthy AI is a shared responsibility and we have establishe
 For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.
-Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

+---
+license: other
+datasets:
+- nvidia/Nemotron-Content-Safety-Reasoning-Dataset
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+language:
+- en
+metrics:
+- f1
+base_model:
+- google/gemma-3-4b-it
+pipeline_tag: text-generation
+tags:
+- content safety
+- guardrail
+- LLM safety
+- topical moderation
+- dialogue moderation
+- custom policy
+- dialogue agents
+library_name: transformers
+---
 # Nemotron-Content-Safety-Reasoning-4B Overview
 ## Description
 For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).