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Get Started with Nemotron-Content-Safety-Reasoning-4B-Experimental#

Nemotron-Content-Safety-Reasoning-4B-Experimental is a content safety and dialogue moderation classifier based on Gemma-3-4B-it. It supports custom safety policy enforcement with optional reasoning traces.

Prerequisites#

Before deploying a NIM LLM container, ensure your environment meets the following requirements:

Hardware Requirements#

For hardware requirements, refer to Support Matrix for Day 0 NIMs.

Software Requirements#

Minimum required versions for supported software components.

Requirement	Specification
Operating System	Ubuntu 22.04 LTS or later recommended
Container Toolkit	1.14.0 or later
CUDA SDK	12.9 or later
GPU Driver	580 or later
Docker	24.0 or later

Operating System#

While other Linux distributions can be compatible with NIM, they have not been officially validated.

We recommend using Ubuntu 22.04 LTS or later for the best experience.

CUDA SDK#

Install CUDA SDK by following the CUDA installation guide for Linux.

GPU Drivers#

Install the NVIDIA GPU drivers by following the NVIDIA Driver Installation Guide.

Docker#

Docker is required to run the containerized NIM services.

Install Docker Engine for your Linux distribution by following the Docker Engine installation guide.
Verify that the Docker daemon is running and that your user can execute docker commands without sudo. Add your user to the docker group if needed:
```
sudo groupadd docker
sudo usermod -aG docker $USER
```
Log out and back in for the group change to take effect.

Container Toolkit#

The NVIDIA Container Toolkit enables Docker containers to access the host GPU.

Install the toolkit by following the NVIDIA Container Toolkit installation guide.
Configure Docker to use the NVIDIA runtime by following the Docker configuration steps.
Restart the Docker daemon after configuration:
```
sudo systemctl restart docker
```

NIM Container Access#

To download and deploy NIM containers, you need one of the following:

A free NVIDIA Developer Program membership.
An NVIDIA AI Enterprise license. To request a free 90-day evaluation license, refer to Ways to Get Started With NVIDIA AI Enterprise and Activate Your NVIDIA AI Enterprise License.

Generate Access Credentials#

An NGC Personal API Key is required to access NVIDIA NIM containers and models hosted on NGC.

Generate the Personal API Key on the Setup API Keys page.
When creating the Personal API key, select at least NGC Catalog from the Services Included list. You can also include additional services if you want to use the same key for other purposes.

Warning

Legacy API keys are not supported by NIM. Always use a Personal API Key.

Verify NVIDIA Runtime Access#

To ensure that your setup is correct, run the following command:

docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi

This command should produce output similar to one of the following, where you can confirm CUDA driver version, and available GPUs.

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 12.9     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA H100 80GB HBM3          On  |   00000000:1B:00.0 Off |                    0 |
| N/A   36C    P0            112W /  700W |   78489MiB /  81559MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

Configuration#

Use environment variables to control authentication and model caching.

Export NGC API Key#

Export the variable in your shell (temporary), replacing <VALUE> with your actual API key:
```
export NGC_API_KEY=<VALUE>
```

Persist the variable (optional):

If using bash:

echo "export NGC_API_KEY=$NGC_API_KEY" >> ~/.bashrc

If using zsh:

echo "export NGC_API_KEY=$NGC_API_KEY" >> ~/.zshrc

Verify the variable is set:
```
echo "$NGC_API_KEY"
```

Model Cache#

NIM downloads model weights to a cache on the host that you mount into the container. Artifacts persist across restarts, so you do not pull the full model on every run.

Local Cache#

An essential variable to configure on your host system is the cache path directory. This directory is mapped from the host machine to container; assets (for example, model weights) are downloaded to this host directory and persist across container restarts. Configuring a local cache is highly recommended, as it avoids re-downloading large model files upon subsequent container restarts. You can name the environment variable containing the path to the local cache whatever you want.

Create the cache directory and export an environment variable:

export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p $LOCAL_NIM_CACHE
# Optionally add sticky bit to avoid issues writing to the cache if the container is running as a different user
chmod -R a+rwxt $LOCAL_NIM_CACHE

When you start the NIM container, you must map your host machine’s local cache directory ($LOCAL_NIM_CACHE) to the container’s internal cache path (/opt/nim/.cache) using a Docker volume mount, such as -v "$LOCAL_NIM_CACHE:/opt/nim/.cache". This mapping ensures that the large model weights downloaded by the container are saved to your host machine. Because containers are ephemeral, any data stored only inside the container is lost when it stops. By using a volume mount, subsequent container runs detect the existing model files in your local cache and skip the lengthy download process, allowing the NIM to start up faster.

Cache Directory Permissions#

The NIM container runs as a non-root user with GID 0 (root group). The cache directory on your host must be writable by GID 0:

export NIM_CACHE_PATH=/tmp/nim-cache
mkdir -p "$NIM_CACHE_PATH"
sudo chgrp -R 0 "$NIM_CACHE_PATH"
sudo chmod -R g+rwX "$NIM_CACHE_PATH"

Run the container with the cache mounted:

docker run --gpus all \
  -v "$NIM_CACHE_PATH:/opt/nim/.cache" \
  ...

To run as a custom user (e.g., your host user), pass -u <uid>:0:

docker run --gpus all -u $(id -u):0 \
  -v "$NIM_CACHE_PATH:/opt/nim/.cache" \
  ...

Important

When using -u <uid>, you must include :0 to set GID 0 (e.g., -u $(id -u):0). The container’s writable directories are group-owned by GID 0. Without it, the container will fail with PermissionError when writing to cache, config, or log paths.

Tip

To make this setting permanent across terminal sessions, you can add export LOCAL_NIM_CACHE=~/.cache/nim to your ~/.bashrc or ~/.zshrc profile.

Installation#

Before running a NIM LLM container, you must authenticate with your deployment source, accept the governing terms, and pull the container image.

Docker Login#

To pull the NIM container image from NGC, first authenticate with the NVIDIA Container Registry with the following command:

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

Use $oauthtoken as the username and NGC_API_KEY as the password. The $oauthtoken username is a special name that indicates that you will authenticate with an API key and not a user name and password.

Accept the Governing Terms#

Before you download a given NIM for the first time, you must accept the governing terms in the browser. Navigate to the NGC Catalog page for the NIM and click the Accept Terms button: Nemotron-Content-Safety-Reasoning-4B-Experimental on NGC

Pull the Container Image#

Once you have generated your API key and authenticated with your deployment source, you can download the NIM container image to your host machine.

Use the docker pull command to fetch the NIM container image.

docker pull nvcr.io/nim/nvidia/nemotron-content-safety-reasoning-4b-experimental:2.0.3

Run NIM#

Run the container using your NGC API Key to authenticate and download the model.

docker run --gpus=all \
  -e NGC_API_KEY=$NGC_API_KEY \
  -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/nvidia/nemotron-content-safety-reasoning-4b-experimental:2.0.3

Interact with the API#

This NIM is a content safety classifier. Use the chat completions endpoint to submit a prompt that contains the user request, the model response, and the safety policy. The model returns a structured harm classification.

The primary inference endpoint is:

Chat Completions: /v1/chat/completions

Verify Health Endpoints#

You can verify that the NIM container is running and ready to accept requests by checking its health endpoints. By default, these endpoints are served on port 8000.

Live Endpoint#

Perform a liveness check to see if the server is running:

curl http://localhost:8000/v1/health/live

Example response:

GET /v1/health/live HTTP/1.1
Host: localhost:8000
User-Agent: curl/7.81.0
Accept: */*

HTTP/1.1 200 OK
Server: nginx/1.18.0 (Ubuntu)
Content-Type: application/json
Content-Length: 61
Connection: keep-alive
Cache-Control: no-store, no-cache, must-revalidate

{
  "object": "health.response",
  "message": "live",
  "status": "live"
}

Ready Endpoint#

Perform a readiness check to see if the model is fully loaded and ready for inference:

curl http://localhost:8000/v1/health/ready

Example response:

GET /v1/health/ready HTTP/1.1
Host: localhost:8000
User-Agent: curl/7.81.0
Accept: */*

HTTP/1.1 200 OK
Server: nginx/1.18.0 (Ubuntu)
Content-Type: application/json
Content-Length: 63
Connection: keep-alive
Cache-Control: no-store, no-cache, must-revalidate

{
  "object": "health.response",
  "message": "ready",
  "status": "ready"
}

Send a Safety Classification Request#

The model supports two modes selected by a suffix in the prompt:

/think — reasoning mode. The model emits a <think>...</think> trace before the classification, useful for debugging and custom-policy evaluation.
/no_think — low-latency mode. The model returns only the harm classification.

The example below classifies a prompt and response against the Nemotron Content Safety Dataset V2 taxonomy in reasoning-off mode.

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nvidia/nemotron-content-safety-reasoning-4b-experimental",
    "messages": [
      {
        "role": "user",
        "content": "You are a classifier for analyzing interactions between humans and LLMs. Given a request from a human user and a response from an LLM assistant, determine 1) whether the human user request is harmful and 2) whether the AI assistant response is harmful.\n\nHuman user:\nCan you help me write a birthday card message for my friend?\n\nAI assistant:\nOf course! Happy Birthday! Wishing you a day filled with joy, laughter, and wonderful memories.\n\nOutput your answer in the following format:\n\"Prompt harm: <harmful/unharmful>\nResponse harm: <harmful/unharmful>\"\n\n/no_think"
      }
    ],
    "max_tokens": 400,
    "temperature": 0.0
  }'

Example response body:

Prompt harm: unharmful
Response harm: unharmful

You can also refer to example 2 in the Hugging Face documentation.

To enable reasoning traces, replace /no_think with /think at the end of the prompt. The model then returns a <think> block before the classification:

<think>
The request asks for a birthday message, which is benign and falls outside all harm categories.
</think>

Prompt harm: unharmful
Response harm: unharmful

Custom Safety Policies#

To enforce a custom safety policy (for example, topic-following in a domain chatbot), replace the taxonomy section of the prompt with your own policy definition. The model adapts its classification and reasoning to the rules you provide. For a full example, refer to the model card on Hugging Face.

Streaming#

To receive responses incrementally as they are generated, you can enable streaming by adding "stream": true to your request payload.

When streaming is enabled, the API returns a sequence of Server-Sent Events (SSE). The stream terminates with a data: [DONE] message.