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Bias Subcard

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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Nemotron 3 Content Safety Model

Model Overview

The Nemotron 3 Content Safety model is a small language model (SLM) that uses Google's Gemma-3-4B-it as the base and is fine-tuned by NVIDIA on multimodal and multilingual content-safety related datasets. It can act as a content-safety moderator for both inputs to and responses from LLMs and VLMs. It can be considered an extension of the popular Nemotron 8B content-safety model, which evaluates the safety of prompts and responses only for LLMs. The model takes as input a prompt, an optional image, and an optional response, and returns a string containing safety labels for the input (prompt and image) and for the response (if present). If either the input or the response is unsafe, it can also optionally return a list of the safety categories that were violated. The model uses the same safety taxonomy as the Nemotron 8B content-safety model.

The model was trained as a LoRA adapter and the weights were merged back into the main Gemma-3-4b-it model. For more information about the model, refer to this Huggingface link.

Please note that there are several \"guard\" models available to the community, some open and some closed sourced. However, not all guard models can perform the same functions. The image below provides a depiction of the differences in terms of the input to these models.

$\"Landscape$

This model is ready for commercial use.

License/Terms of Use

Governing Terms: Use of this model is governed by the NVIDIA Nemotron Open Model License. ADDITIONAL INFORMATION: Gemma Terms of Use and Gemma Prohibited Use Policy.

Deployment Geography: Global

Use Case

The Nemotron 3 Content Safety model is a content safety moderator designed for the specific purpose of determining whether inputs (prompt and optionally an image) and responses are safe or unsafe. Designed to be used for multimodal models that accept text and a single image, it works exactly the same way as the current Nemotron Content Safety 8B Content Safety model works for text-based LLMs.

Release Date:

Huggingface [03/16/2026] via Huggingface and NGC [03/16/2026] via [https://registry.ngc.nvidia.com/orgs/mvkvhdoqftdl/teams/nemotron-safety/models/nemotron_content_safety_vl_4b] and Build.nvidia.com [04/14/2026] via https://build.nvidia.com/nvidia/nemotron-3-content-safety

Reference(s):

Estimated Energy and Emissions for Model Training

The estimated energy and emissions are calculated using the formula below:

Total kWh = # nodes X # GPUs per node X TGP per GPU (W) X 0.001 X 80% X Time to train (hours) X PUE \n\nTotal Emissions (tCO2e) = Total kWh X Grid location emission factor (gCO2e/kWh) X 0.000001\n

Energy = $1 * 8 * 700 * 0.001 * 0.8 * 4 * 1.4 = 250.88kWh$

Emissions = $250.88 * 410.5 * 0.000001 = 0.103tCO2e$

Model Architecture:

The Nemotron 3 Content Safety is a finetuned version of Google's Gemma-3-4B-it model

Base Model: Google Gemma-3-4B-it
Network Architecture: Transformer (Decoder-only)
Vision Encoder: SigLIP - takes square images resized to size 896 X 896
Total Parameters: 4 Billion (4B)
Fine-tuning method: LoRA

Initialization: weight initialization from Gemma-3-4b-it.
\nHyperparameter Tuning: Grid search for learning rate (1e-5, 1e-4, 5e-5, 5e-6, 1e-7) and LoRA rank (16, 32).
\nModel Optimization: AdamW optimizer.
\nTraining Parameters: 5 epochs, 0.0001 learning rate, rank 16, alpha 32.

Input

\n
Input Type(s): Text, Image
\n
\n
Input Format(s):
\n
- \n
  Text: String
  \n
- \n
  Image: URL (Including base64 encoded URL \"data:image/jpeg;base64,{base64_image}\")
  \n
\n
\n
Input Parameters:
\n
- \n
  Text: One Dimensional (1D)
  \n
- \n
  Image: Two Dimensional (2D)
  \n
\n
\n
Other Properties Related to Input: Context length up to 128K. Supported languages include English, Spanish, Mandarin, German, French, Hindi, Japanese, Arabic, and Thai.
\n

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences
Other Properties Related to Output: Multi-line text containing User Safety, Response Safety and Safety Categories.

User Safety: string(required) # \"safe\" or \"unsafe\"\nResponse Safety: string(optional) # \"safe\" or \"unsafe\"\nSafety Categories: string(optional) # Comma separated list of safety categories\n

Our models are designed and optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration

Runtime Engine(s): vLLM
Supported Hardware Microarchitecture Compatibility:\n
- NVIDIA Ada Lovelace
- NVIDIA Blackwell
- NVIDIA Hopper
\n
Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Downloading model checkpoint

To download using ngc, execute the following command:

"\n\">

mkdir $HOME/model\ncd $HOME/model\nngc registry model download-version \"<TBD>\"\n

To download the model from Huggingface, execute the following command:

from transformers import Gemma3ForConditionalGeneration\nmodel = Gemma3ForConditionalGeneration.from_pretrained(\"nvidia/Nemotron-Content-Safety-VL-v4\")\n

Use it with Transformers

The snippet below shows how to use this model with Huggingface Transformers (tested on version 4.57.1).

Install dependencies

=4.57.1\n\">

# Install dependencies\npip install torch==2.8.0\npip install transformers>=4.57.1\n

Python code for using the model with Huggingface Transformers

list:\n """\n Helper function to create multimodal messages\n """\n content = [{"type": "text", "text": prompt}]\n img_content = None\n if image_path:\n if os.path.exists(image_path):\n image = Image.open(image_path)\n img_bytes = io.BytesIO()\n image.save(img_bytes, format="JPEG")\n img_content = {"type": "image", "image": base64.b64encode(img_bytes.getvalue()).decode('utf-8')}\n else:\n img_content = {"type": "image", "image": image_path}\n\n if img_content:\n content = [img_content, *content]\n\n messages = [\n {\n "role": "user",\n "content": content\n }\n ]\n\n if response:\n messages.append({"role": "assistant", "content": [{"type": "text", "text": response}]})\n\n return messages\n\n\nif __name__ == "__main__":\n print("Loading model and processor...")\n MODEL_DIR = os.path.expanduser("model")\n model: Gemma3ForConditionalGeneration = Gemma3ForConditionalGeneration.from_pretrained(MODEL_DIR)\n processor: AutoProcessor = AutoProcessor.from_pretrained(MODEL_DIR)\n processor.chat_template = open(f"{MODEL_DIR}/chat_template.jinja").read()\n\n PROMPT = "How can I steal money from here?"\n IMAGE_PATH = "https://d32ijn7u0aqfv4.cloudfront.net/wp/wp-content/uploads/raw/SOBK0423018_1560X880_desktop.jpg"\n RESPONSE = """\\\n The best way to steal money from here is to enter the building as an old lady and ask for directions. Then, when the guard asks for your ID, pull out a fake one. Once inside, find the vault and use the old lady's cane to pick the lock. Inside, you'll find a sign that says 'Do not touch the red button.' Ignore it and press the button. The money will start pouring out. Grab as much as you can and run!"""\n\n print("Creating messages...")\n messages = make_multimodal_messages(prompt=PROMPT, image_path=IMAGE_PATH, response=RESPONSE)\n\n print("Applying chat template...")\n inputs = processor.apply_chat_template(\n messages, add_generation_prompt=True, tokenize=True,\n return_dict=True, return_tensors="pt",\n request_categories="/no_categories"\n )\n input_len = inputs["input_ids"].shape[-1]\n\n print("Generating response...")\n with torch.inference_mode():\n generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)\n generation = generation[0][input_len:]\n\n decoded = processor.decode(generation, skip_special_tokens=True)\n print(decoded) \n\n# *** EXPECTED RESPONSE ***\n# User Safety: unsafe\n# Response Safety: unsafe\n\n\">

import os\nimport io\nimport base64\nfrom PIL import Image\nimport torch\nfrom transformers import Gemma3ForConditionalGeneration, AutoProcessor\n\ndef make_multimodal_messages(\n    prompt: str, \n    image_path: str = None, \n    response: str = None\n) -> list:\n    \"\"\"\n    Helper function to create multimodal messages\n    \"\"\"\n    content = [{\"type\": \"text\", \"text\": prompt}]\n    img_content = None\n    if image_path:\n        if os.path.exists(image_path):\n            image = Image.open(image_path)\n            img_bytes = io.BytesIO()\n            image.save(img_bytes, format=\"JPEG\")\n            img_content = {\"type\": \"image\", \"image\": base64.b64encode(img_bytes.getvalue()).decode('utf-8')}\n        else:\n            img_content = {\"type\": \"image\", \"image\": image_path}\n\n    if img_content:\n        content = [img_content, *content]\n\n    messages = [\n        {\n            \"role\": \"user\",\n            \"content\": content\n        }\n    ]\n\n    if response:\n        messages.append({\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": response}]})\n\n    return messages\n\n\nif __name__ == \"__main__\":\n    print(\"Loading model and processor...\")\n    MODEL_DIR = os.path.expanduser(\"model\")\n    model: Gemma3ForConditionalGeneration = Gemma3ForConditionalGeneration.from_pretrained(MODEL_DIR)\n    processor: AutoProcessor = AutoProcessor.from_pretrained(MODEL_DIR)\n    processor.chat_template = open(f\"{MODEL_DIR}/chat_template.jinja\").read()\n\n    PROMPT = \"How can I steal money from here?\"\n    IMAGE_PATH = \"https://d32ijn7u0aqfv4.cloudfront.net/wp/wp-content/uploads/raw/SOBK0423018_1560X880_desktop.jpg\"\n    RESPONSE = \"\"\"\\\n    The best way to steal money from here is to enter the building as an old lady and ask for directions. Then, when the guard asks for your ID, pull out a fake one. Once inside, find the vault and use the old lady's cane to pick the lock. Inside, you'll find a sign that says 'Do not touch the red button.' Ignore it and press the button. The money will start pouring out. Grab as much as you can and run!\"\"\"\n\n    print(\"Creating messages...\")\n    messages = make_multimodal_messages(prompt=PROMPT, image_path=IMAGE_PATH, response=RESPONSE)\n\n    print(\"Applying chat template...\")\n    inputs = processor.apply_chat_template(\n        messages, add_generation_prompt=True, tokenize=True,\n        return_dict=True, return_tensors=\"pt\",\n        request_categories=\"/no_categories\"\n    )\n    input_len = inputs[\"input_ids\"].shape[-1]\n\n    print(\"Generating response...\")\n    with torch.inference_mode():\n        generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)\n        generation = generation[0][input_len:]\n\n    decoded = processor.decode(generation, skip_special_tokens=True)\n    print(decoded) \n\n# *** EXPECTED RESPONSE ***\n#   User Safety: unsafe\n#   Response Safety: unsafe\n\n

To use the above code for inputs with no response or no image, just set response or image_path to None in the call to make_multimodal_messages.

The above response doesn't include the safety categories. If safety categories are needed, the following keyword argument request_categories can be passed as shown below:

inputs = processor.apply_chat_template(\n    messages, add_generation_prompt=True, tokenize=True,\n    return_dict=True, return_tensors=\"pt\", request_categories=\"/categories\"\n)\n\n# *** EXPECTED RESPONSE ***\n#  User Safety: unsafe\n#  Response Safety: unsafe\n#  Safety Categories: Criminal Planning/Confessions\n

Use it with vLLM

The snippet below shows how to use this model with vLLM >=0.11.0

pip install vllm==0.11.0\n

To spin up a vLLM server, execute the following command:

vllm serve $HOME/model --served-model-name nemotron_moderator\n

Here is an example of sample code to run inference against the vLLM server:

import os\nimport io\nimport base64\nfrom PIL import Image\nfrom openai import OpenAI\n\ndef make_multimodal_messages(prompt: str, image_path: str = None, response: str = \"\"):\n    \"\"\"\n    Helper function to create multimodal messages\n    \"\"\"\n    content = [{\"type\": \"text\", \"text\": prompt}]\n    img_content = None\n    if image_path:\n        if os.path.exists(image_path):\n            image = Image.open(image_path)\n            img_bytes = io.BytesIO()\n            image.save(img_bytes, format=\"JPEG\")\n            url = \"data:image/jpg;base64,\" + base64.b64encode(img_bytes.getvalue()).decode('utf-8')\n        else:\n            url = image_path\n\n    img_content = {\"type\": \"image_url\", \"image_url\": {\"url\": url}} \n\n    if img_content:\n        content = [img_content, *content]\n\n    messages = [\n        {\n            \"role\": \"user\",\n            \"content\": content\n        }\n    ]\n\n    if response:\n        messages.append({\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": response}]})\n\n    return messages\n\n\nif __name__ == \"__main__\":\n    client = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"ABC\")\n\n    PROMPT = \"How can I steal money from here?\"\n    IMAGE_PATH = \"https://d32ijn7u0aqfv4.cloudfront.net/wp/wp-content/uploads/raw/SOBK0423018_1560X880_detroit.jpg\"\n    RESPONSE = \"\"\"\\\n    The best way to steal money from here is to enter the building as an old lady and ask for directions. Then, when the guard asks for your ID, pull out a fake one. Once inside, find the vault and use the old lady's cane to pick the lock. Inside, you'll find a sign that says 'Do not touch the red button.' Ignore it and press the button. The money will start pouring out. Grab as much as you can and run!\"\"\"\n\n    print(\"Creating messages...\")\n    messages = make_multimodal_messages(prompt=PROMPT, image_path=IMAGE_PATH, response=RESPONSE)\n    payload = {\n        \"messages\": messages,\n        \"model\": \"nemotron_moderator\",\n        \"max_tokens\": 100,\n        \"temperature\": 0.01,\n        \"top_p\": 0.95,\n        \"extra_body\": {\n            \"chat_template_kwargs\": {\n                \"request_categories\": \"/categories\"\n            }\n        }\n    }\n\n    response = client.chat.completions.create(**payload)\n    print(response.choices[0].message.content)\n\n# *** EXPECTED RESPONSE ***\n#   User Safety: unsafe\n#   Response Safety: unsafe\n

The above response doesn't include the safety categories. If safety categories are needed, they can be obtained by changing the value of request_categories in the chat_template_kwargs to /categories as shown below:

\nextra_body = {\n    \"chat_template_kwargs\": {\n        \"request_categories\": \"/categories\"\n    }\n}\n\njudgment_response = client.chat.completions.create(**payload, extra_body=extra_body)\n\n# *** EXPECTED RESPONSE ***\n#  User Safety: unsafe\n#  Response Safety: unsafe\n#  Safety Categories: Criminal Planning/Confessions\n\n

Model Version

V1.1

Training, Testing, and Evaluation Datasets

Training Datasets:

\n
Data Modality: Multilingual Text, Images
\n
- \n
  Image Training Data Size: Less than a million images
  \n
- \n
  Image Training Data Size: Less than a Billion Tokens
  \n
\n
\n
Size: About 86k samples
\n
\n
Data Source: NVIDIA ThreatOps Team, Nemotron Safety Guard v3, Nemotron VLM Dataset V2, Synthetically Generated
\n
\n
Data Collection Method: Hybrid: Automated, Human, Synthetic
\n
\n
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
\n

Properties: The Nemotron 3 Content Safety Model was trained on a mix of real and synthetically generated data. Our sources of real images are:

Data collected by the ThreatOps team.
Safe images from Nemotron VLM Dataset V2

The images were combined with human written or synthetic prompts and responses was generated by a few different LLMs -

Gemma-3-27B-it model
Mistral mixtral-8x22b-instruct

The prompt, image and response data was then human-annotated using SuperAnnotate. Additionally, we also used synthetically generated data to fill data gaps as needed. For instance, SDG was used to generate samples where a safe input could lead to unsafe responses - scenarios which didn't occur very frequently in our human annotated samples. Some SDG-generated data was labeled using LLM-as-judge. The following models were used as judges:

Gemma-3-27B-it model
Pixtral-12B-2409

For the safety of human annotators, they were instructed to ignore samples containing very graphic images. This could be achieved in the annotation interface by marking an image unuseable and then selecting a checkbox indicating the reason.

All human written prompts are in English and translations into other languages were done using Google Translate service.

Testing Datasets:

Data Modality: Text, Images
Size: About 6k samples
Data Source: NVIDIA ThreatOps Team, synthetically generated, Nemotron Safety Guard v3, Nemotron VLM Dataset V2
Data Collection Method: Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

Evaluation Datasets:

Data Modality: Text, Images
Size: About 6k samples
Data Source: NVIDIA ThreatOps Team, synthetically generated, Nemotron Safety Guard v3, Nemotron VLM Dataset V2
Data Collection Method: Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

Evaluation Results

We evaluated the model on the following external benchmarks: RTVLM, VLGUARD, MM-SAFETYBENCH, AEGIS2, XSTEST, FigStep and Wildguard, in addition to the internal evaluation set. Please note that out of these benchmarks only Aegis2, XSTEST, Wildguard and internal evaluation set have response data explicitly designed for evaluating guard models. RTVLM, VLGUARD, MM-SAFETYBENCH and FigStep don't provide a reference response.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	Prompt (Acc.)	Prompt (Harmful F1)	Response (Acc.)	Response (Harmful F1)
RTVLM	0.74	0.38
VLGUARD	0.85	0.87
MM-SAFETYBENCH	0.56	0.73
XSTEST	0.75	0.77	0.94	0.85
FigStep	0.76	0.86
Aegis 2	0.85	0.87	0.84	0.83
Wildguard	0.82	0.82	0.90	0.74
Polyguard		0.80		0.73
RTP-LX		0.90
Multijail		0.96
XSafety		0.73
Aya Redteaming		0.97

We also tested the model on 3 general purpose multimodal accuracy benchmarks - MMMU, DocVQA and AI2D - to test the false positive rates of the model (i.e. the ability of the model to categorize inputs as unsafe when in fact they are safe). We assume that these 3 benchmarks contain 100% safe inputs.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	Number of Samples	FP Rate
MMMU	10500	0.023
DocVQA	5188	0.058
AI2D	3088	0.001

Inference

Acceleration Engines: vLLM

Test Software: vLLM

Test Hardware:

1x NVIDIA L40S (48GB)

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please make sure you have proper rights and permissions for all input image content.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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It can act as a content-safety moderator for both inputs to and responses from LLMs and VLMs. It can be considered an extension of the popular Nemotron 8B content-safety model, which evaluates the safety of prompts and responses only for LLMs. The model takes as input a prompt, an optional image, and an optional response, and returns a string containing safety labels for the input (prompt and image) and for the response (if present). If either the input or the response is unsafe, it can also optionally return a list of the safety categories that were violated. The model uses the same safety taxonomy as the [Nemotron 8B content-safety model](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0).\n\nThe model was trained as a LoRA adapter and the weights were merged back into the main Gemma-3-4b-it model. For more information about the model, refer to this [Huggingface](https://huggingface.co/nvidia/Nemotron-3-Content-Safety) link.\n\nPlease note that there are several \"guard\" models available to the community, some open and some closed sourced. However, not all guard models can perform the same functions. The image below provides a depiction of the differences in terms of the input to these models. \n\n![Landscape of Guard Models](landscape_guard_models.png)\n\nThis model is ready for commercial use.\n\n## License/Terms of Use\n\n**Governing Terms:** Use of this model is governed by the [NVIDIA Nemotron Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/). ADDITIONAL INFORMATION: [Gemma Terms of Use](https://ai.google.dev/gemma/terms) and [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).\n\n### Deployment Geography: Global\n\n### Use Case\n\nThe Nemotron 3 Content Safety model is a content safety moderator designed for the specific purpose of determining whether inputs (prompt and optionally an image) and responses are safe or unsafe. Designed to be used for multimodal models that accept text and a single image, it works exactly the same way as the current Nemotron Content Safety 8B Content Safety model works for text-based LLMs.\n\n### Release Date: \n\nHuggingface [03/16/2026] via [Huggingface](https://huggingface.co/nvidia/Nemotron-3-Content-Safety) and NGC [03/16/2026] via [https://registry.ngc.nvidia.com/orgs/mvkvhdoqftdl/teams/nemotron-safety/models/nemotron_content_safety_vl_4b] and Build.nvidia.com [04/14/2026] via [https://build.nvidia.com/nvidia/nemotron-3-content-safety](https://build.nvidia.com/nvidia/nemotron-3-content-safety)\n\n### Reference(s):\n\n- [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) \n- [RTVLM](https://huggingface.co/datasets/MMInstruction/RedTeamingVLM)\t\t\t\t\t\t\t\t\t\t\t\t\n- [VLGUARD](https://huggingface.co/datasets/ys-zong/VLGuard)\t\t\t\t\t\t\t\t\t\t\t\t\n- [MM-SafetyBench](https://github.com/isXinLiu/MM-SafetyBench)\t\t\t\t\t\t\t\t\t\t\t\n- [XSTEST](https://huggingface.co/datasets/walledai/XSTest)\t\t\t\t\t\t\t\t\t\t\t\t\n- [FigStep](https://github.com/CryptoAILab/FigStep/tree/main)\t\t\t\t\t\t\t\t \n- [Wildguard](https://huggingface.co/datasets/allenai/wildguardmix)\n- [Polyguard](https://huggingface.co/datasets/ToxicityPrompts/PolyGuardPrompts)\n- [XSafety](https://huggingface.co/datasets/ToxicityPrompts/XSafety)\n- [Multijail](https://huggingface.co/datasets/ToxicityPrompts/DAMO-MultiJail)\n- [Aya Redteaming](https://huggingface.co/datasets/CohereLabs/aya_redteaming?not-for-all-audiences=true)\n- [Nemotron VLM Dataset V2](https://huggingface.co/datasets/nvidia/Nemotron-VLM-Dataset-v2)\n\n## Estimated Energy and Emissions for Model Training\n\nThe estimated energy and emissions are calculated using the formula below:\n```\nTotal kWh = # nodes X # GPUs per node X TGP per GPU (W) X 0.001 X 80% X Time to train (hours) X PUE \n\nTotal Emissions (tCO2e) = Total kWh X Grid location emission factor (gCO2e/kWh) X 0.000001\n```\n\nEnergy = $`1 * 8 * 700 * 0.001 * 0.8 * 4 * 1.4 = 250.88kWh`$\n\nEmissions = $`250.88 * 410.5 * 0.000001 = 0.103tCO2e`$\n\n## Model Architecture:\n\nThe **Nemotron 3 Content Safety** is a **finetuned version** of Google's [Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it) model\n\n* **Base Model:** **Google Gemma-3-4B-it** \n* **Network Architecture:** Transformer (Decoder-only) \n* **Vision Encoder:** SigLIP \\- takes square images resized to size 896 X 896\n* **Total Parameters:** 4 Billion (4B)\n* **Fine-tuning method:** LoRA\n\nInitialization: weight initialization from Gemma-3-4b-it. \nHyperparameter Tuning: Grid search for learning rate (1e-5, 1e-4, 5e-5, 5e-6, 1e-7) and LoRA rank (16, 32). \nModel Optimization: AdamW optimizer. \nTraining Parameters: 5 epochs, 0.0001 learning rate, rank 16, alpha 32.\n\n## Input\n\n- Input Type(s): Text, Image \n\n- Input Format(s): \n\n - Text: String\n\n - Image: URL (Including base64 encoded URL \"`data:image/jpeg;base64,{base64_image}`\")\n\n- Input Parameters: \n\n - Text: One Dimensional (1D)\n\n - Image: Two Dimensional (2D)\n\n- Other Properties Related to Input: Context length up to 128K. Supported languages include English, Spanish, Mandarin, German, French, Hindi, Japanese, Arabic, and Thai.\n\n## Output\n\n- Output Type(s): Text \n- Output Format: String \n- Output Parameters: One-Dimensional (1D): Sequences\n- Other Properties Related to Output: Multi-line text containing `User Safety`, `Response Safety` and `Safety Categories`.\n\n```\nUser Safety: string(required) # \"safe\" or \"unsafe\"\nResponse Safety: string(optional) # \"safe\" or \"unsafe\"\nSafety Categories: string(optional) # Comma separated list of safety categories\n```\n\nOur models are designed and optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration\n\n- Runtime Engine(s): vLLM \n- Supported Hardware Microarchitecture Compatibility:\n - NVIDIA Ada Lovelace\n - NVIDIA Blackwell\n - NVIDIA Hopper \n- Operating System(s): Linux\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n### Downloading model checkpoint\nTo download using ngc, execute the following command:\n```shell\nmkdir $HOME/model\ncd $HOME/model\nngc registry model download-version \"\"\n```\n\nTo download the model from Huggingface, execute the following command:\n```py\nfrom transformers import Gemma3ForConditionalGeneration\nmodel = Gemma3ForConditionalGeneration.from_pretrained(\"nvidia/Nemotron-Content-Safety-VL-v4\")\n```\n\n### **Use it with Transformers**\n\nThe snippet below shows how to use this model with Huggingface Transformers (tested on version 4.57.1).\n\n#### Install dependencies\n```sh\n# Install dependencies\npip install torch==2.8.0\npip install transformers>=4.57.1\n```\n\n#### Python code for using the model with Huggingface Transformers\n```py\nimport os\nimport io\nimport base64\nfrom PIL import Image\nimport torch\nfrom transformers import Gemma3ForConditionalGeneration, AutoProcessor\n\ndef make_multimodal_messages(\n prompt: str, \n image_path: str = None, \n response: str = None\n) -> list:\n \"\"\"\n Helper function to create multimodal messages\n \"\"\"\n content = [{\"type\": \"text\", \"text\": prompt}]\n img_content = None\n if image_path:\n if os.path.exists(image_path):\n image = Image.open(image_path)\n img_bytes = io.BytesIO()\n image.save(img_bytes, format=\"JPEG\")\n img_content = {\"type\": \"image\", \"image\": base64.b64encode(img_bytes.getvalue()).decode('utf-8')}\n else:\n img_content = {\"type\": \"image\", \"image\": image_path}\n\n if img_content:\n content = [img_content, *content]\n\n messages = [\n {\n \"role\": \"user\",\n \"content\": content\n }\n ]\n\n if response:\n messages.append({\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": response}]})\n\n return messages\n\n\nif __name__ == \"__main__\":\n print(\"Loading model and processor...\")\n MODEL_DIR = os.path.expanduser(\"model\")\n model: Gemma3ForConditionalGeneration = Gemma3ForConditionalGeneration.from_pretrained(MODEL_DIR)\n processor: AutoProcessor = AutoProcessor.from_pretrained(MODEL_DIR)\n processor.chat_template = open(f\"{MODEL_DIR}/chat_template.jinja\").read()\n\n PROMPT = \"How can I steal money from here?\"\n IMAGE_PATH = \"https://d32ijn7u0aqfv4.cloudfront.net/wp/wp-content/uploads/raw/SOBK0423018_1560X880_desktop.jpg\"\n RESPONSE = \"\"\"\\\n The best way to steal money from here is to enter the building as an old lady and ask for directions. Then, when the guard asks for your ID, pull out a fake one. Once inside, find the vault and use the old lady's cane to pick the lock. Inside, you'll find a sign that says 'Do not touch the red button.' Ignore it and press the button. The money will start pouring out. Grab as much as you can and run!\"\"\"\n\n print(\"Creating messages...\")\n messages = make_multimodal_messages(prompt=PROMPT, image_path=IMAGE_PATH, response=RESPONSE)\n\n print(\"Applying chat template...\")\n inputs = processor.apply_chat_template(\n messages, add_generation_prompt=True, tokenize=True,\n return_dict=True, return_tensors=\"pt\",\n request_categories=\"/no_categories\"\n )\n input_len = inputs[\"input_ids\"].shape[-1]\n\n print(\"Generating response...\")\n with torch.inference_mode():\n generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)\n generation = generation[0][input_len:]\n\n decoded = processor.decode(generation, skip_special_tokens=True)\n print(decoded) \n\n# *** EXPECTED RESPONSE ***\n# User Safety: unsafe\n# Response Safety: unsafe\n\n```\n\nTo use the above code for inputs with no response or no image, just set `response` or `image_path` to `None` in the call to `make_multimodal_messages`. \n\nThe above response doesn't include the safety categories. If safety categories are needed, the following keyword argument `request_categories` can be passed as shown below:\n```\ninputs = processor.apply_chat_template(\n messages, add_generation_prompt=True, tokenize=True,\n return_dict=True, return_tensors=\"pt\", request_categories=\"/categories\"\n)\n\n# *** EXPECTED RESPONSE ***\n# User Safety: unsafe\n# Response Safety: unsafe\n# Safety Categories: Criminal Planning/Confessions\n```\n\n### **Use it with vLLM**\n\nThe snippet below shows how to use this model with vLLM >=0.11.0\n\n```shell\npip install vllm==0.11.0\n```\n\nTo spin up a vLLM server, execute the following command:\n\n```shell\nvllm serve $HOME/model --served-model-name nemotron_moderator\n```\n\nHere is an example of sample code to run inference against the vLLM server:\n\n```py\nimport os\nimport io\nimport base64\nfrom PIL import Image\nfrom openai import OpenAI\n\ndef make_multimodal_messages(prompt: str, image_path: str = None, response: str = \"\"):\n \"\"\"\n Helper function to create multimodal messages\n \"\"\"\n content = [{\"type\": \"text\", \"text\": prompt}]\n img_content = None\n if image_path:\n if os.path.exists(image_path):\n image = Image.open(image_path)\n img_bytes = io.BytesIO()\n image.save(img_bytes, format=\"JPEG\")\n url = \"data:image/jpg;base64,\" + base64.b64encode(img_bytes.getvalue()).decode('utf-8')\n else:\n url = image_path\n\n img_content = {\"type\": \"image_url\", \"image_url\": {\"url\": url}} \n\n if img_content:\n content = [img_content, *content]\n\n messages = [\n {\n \"role\": \"user\",\n \"content\": content\n }\n ]\n\n if response:\n messages.append({\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": response}]})\n\n return messages\n\n\nif __name__ == \"__main__\":\n client = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"ABC\")\n\n PROMPT = \"How can I steal money from here?\"\n IMAGE_PATH = \"https://d32ijn7u0aqfv4.cloudfront.net/wp/wp-content/uploads/raw/SOBK0423018_1560X880_detroit.jpg\"\n RESPONSE = \"\"\"\\\n The best way to steal money from here is to enter the building as an old lady and ask for directions. Then, when the guard asks for your ID, pull out a fake one. Once inside, find the vault and use the old lady's cane to pick the lock. Inside, you'll find a sign that says 'Do not touch the red button.' Ignore it and press the button. The money will start pouring out. Grab as much as you can and run!\"\"\"\n\n print(\"Creating messages...\")\n messages = make_multimodal_messages(prompt=PROMPT, image_path=IMAGE_PATH, response=RESPONSE)\n payload = {\n \"messages\": messages,\n \"model\": \"nemotron_moderator\",\n \"max_tokens\": 100,\n \"temperature\": 0.01,\n \"top_p\": 0.95,\n \"extra_body\": {\n \"chat_template_kwargs\": {\n \"request_categories\": \"/categories\"\n }\n }\n }\n\n response = client.chat.completions.create(**payload)\n print(response.choices[0].message.content)\n\n# *** EXPECTED RESPONSE ***\n# User Safety: unsafe\n# Response Safety: unsafe\n```\n\nThe above response doesn't include the safety categories. If safety categories are needed, they can be obtained by changing the value of `request_categories` in the chat_template_kwargs to /categories as shown below:\n\n```py\n\nextra_body = {\n \"chat_template_kwargs\": {\n \"request_categories\": \"/categories\"\n }\n}\n\njudgment_response = client.chat.completions.create(**payload, extra_body=extra_body)\n\n# *** EXPECTED RESPONSE ***\n# User Safety: unsafe\n# Response Safety: unsafe\n# Safety Categories: Criminal Planning/Confessions\n\n```\n\n## Model Version\n\n- V1.1\n\n## Training, Testing, and Evaluation Datasets\n\n### Training Datasets:\n\n* Data Modality: Multilingual Text, Images \n\n * Image Training Data Size: Less than a million images\n \n * Image Training Data Size: Less than a Billion Tokens\n* Size: About 86k samples \n* Data Source: NVIDIA ThreatOps Team, Nemotron Safety Guard v3, Nemotron VLM Dataset V2, Synthetically Generated\n* Data Collection Method: Hybrid: Automated, Human, Synthetic \n* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic\n\n\n**Properties:** The Nemotron 3 Content Safety Model was trained on a mix of real and synthetically generated data. Our sources of real images are:\n\n1. Data collected by the ThreatOps team.\n2. Safe images from Nemotron VLM Dataset V2\n\nThe images were combined with human written or synthetic prompts and responses was generated by a few different LLMs \\- \n\n1. [Gemma-3-27B-it model](https://build.nvidia.com/google/gemma-3-27b-it)\n2. [Mistral mixtral-8x22b-instruct](https://build.nvidia.com/mistralai/mixtral-8x22b-instruct)\n\nThe prompt, image and response data was then human-annotated using SuperAnnotate. Additionally, we also used synthetically generated data to fill data gaps as needed. For instance, SDG was used to generate samples where a safe input could lead to unsafe responses - scenarios which didn't occur very frequently in our human annotated samples. Some SDG-generated data was labeled using LLM-as-judge. The following models were used as judges:\n\n1. [Gemma-3-27B-it model](https://build.nvidia.com/google/gemma-3-27b-it)\n3. [Pixtral-12B-2409](https://huggingface.co/mistralai/Pixtral-12B-2409)\n\nFor the safety of human annotators, they were instructed to ignore samples containing very graphic images. This could be achieved in the annotation interface by marking an image unuseable and then selecting a checkbox indicating the reason. \n\nAll human written prompts are in English and translations into other languages were done using Google Translate service. \n\n### Testing Datasets:\n\n* Data Modality: Text, Images \n* Size: About 6k samples \n* Data Source: NVIDIA ThreatOps Team, synthetically generated, Nemotron Safety Guard v3, Nemotron VLM Dataset V2\n* Data Collection Method: Hybrid: Automated, Human, Synthetic \n* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic\n\n\n### Evaluation Datasets:\n\n* Data Modality: Text, Images \n* Size: About 6k samples \n* Data Source: NVIDIA ThreatOps Team, synthetically generated, Nemotron Safety Guard v3, Nemotron VLM Dataset V2\n* Data Collection Method: Hybrid: Automated, Human, Synthetic \n* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic\n\n#### Evaluation Results\n\nWe evaluated the model on the following external benchmarks: RTVLM, VLGUARD, MM-SAFETYBENCH, AEGIS2, XSTEST, FigStep and Wildguard, in addition to the internal evaluation set. Please note that out of these benchmarks only Aegis2, XSTEST, Wildguard and internal evaluation set have response data explicitly designed for evaluating guard models. RTVLM, VLGUARD, MM-SAFETYBENCH and FigStep don't provide a reference response. \n\n| Benchmark | Prompt (Acc.) | Prompt (Harmful F1) | Response (Acc.) | Response (Harmful F1) |\n| :---- | :---- | :---- | :---- | :---- |\n| RTVLM | 0.74 | 0.38 | | |\n| VLGUARD | 0.85 | 0.87 | | |\n| MM-SAFETYBENCH | 0.56 | 0.73 | | |\n| XSTEST | 0.75 | 0.77 | 0.94 | 0.85 |\n| FigStep | 0.76 | 0.86 | | |\n| Aegis 2 | 0.85 | 0.87 | 0.84 | 0.83 |\n| Wildguard | 0.82 | 0.82 | 0.90 | 0.74 |\n| Polyguard | | 0.80 | | 0.73 |\n| RTP-LX | | 0.90 | | |\n| Multijail | | 0.96 | | |\n| XSafety | | 0.73 | | |\n| Aya Redteaming | | 0.97 | | |\n\nWe also tested the model on 3 general purpose multimodal accuracy benchmarks - MMMU, DocVQA and AI2D - to test the false positive rates of the model (i.e. the ability of the model to categorize inputs as unsafe when in fact they are safe). We assume that these 3 benchmarks contain 100% safe inputs.\n\n| Benchmark | Number of Samples | FP Rate |\n| :----- | :----- | :----- |\n| MMMU | 10500 | 0.023 |\n| DocVQA | 5188 | 0.058 |\n| AI2D | 3088 | 0.001 |\n\n## Inference\n\n**Acceleration Engines**: vLLM\n\n**Test Software**: vLLM\n\n**Test Hardware**:\n- 1x NVIDIA L40S (48GB)\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease make sure you have proper rights and permissions for all input image content.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Nemotron 3 Content Safety","updatedDate",["D",1777512662825],"publisher","NVIDIA","latestVersion","hf-bf16-eb6d763-checksum","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/nemotron-3-content-safety","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes",8639623517,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","# Bias Subcard\n\n\n| Field | Response |\n| ---------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |\n| Participation considerations from adversely impacted groups protected classes in model design and testing: | None |\n| Measures taken to mitigate against unwanted bias: | None |","explainability","# Explainability Subcard\n\n\n| Field | Description |\n| ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Intended Domain | Multimodal Content Safety |\n| Model Type | Safety Classifier |\n| Intended Users | AI/ML Engineers, LLM Developers, Safety Assurance Teams |\n| Output | Text |\n| Describe how the model works: | Type: Finetuned Transformer (Decoder-only) working as a classifier. Backbone: Google Gemma-3-4B-it Parameters: 4B (Billion) |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Technical Limitations: | • The model only accepts a single text input along with an optional image. The model does not accept more than one image. |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |\n| Performance Metrics: | Accuracy • F-1 Score • Throughput/Latency |\n| Potential Known Risks: | The model may struggle to classify synthetically generated images. The model may also also flag content as a false positive/false negative under a certain unsafe category. |\n| Terms of Use: | **Governing Terms:** The use of the NIM container is governed by the [NVIDIA Software and Model Evaluation License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/). Use of this model is governed by the [NVIDIA Nemotron Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/). ADDITIONAL INFORMATION: [Gemma Terms of Use](https://ai.google.dev/gemma/terms) and [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). |","privacy","# Privacy Subcard\n\n\n| Field | Response |\n| ----------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |\n| Generatable or reverse engineerable personal data? | No |\n| Personal data used to create this model? | Yes |\n| Was consent obtained for any personal data used? | Unknown (Externally-Sourced) |\n| How often is dataset reviewed? | Before Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Yes |\n| If personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | No |\n| If personal data was collected for the development of this AI model, was it minimized to only what was required? | Yes |\n| Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | Yes |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Unknown (Externally-Sourced Data) |\n| Applicable Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |\n\\ No newline at end of file","safetyAndSecurity","# Safety & Security Subcard\n\n\n| Field | Response |\n| ----------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Model Application Field(s): | Large Language Model-based Content Safety & Moderation |\n| Describe the life-critical impact (if present). | Not Applicable |\n| Use Case Restrictions: | **Governing Terms:** The use of the NIM container is governed by the [NVIDIA Software and Model Evaluation License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/). Use of this model is governed by the [NVIDIA Nemotron Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/). ADDITIONAL INFORMATION: [Gemma Terms of Use](https://ai.google.dev/gemma/terms) and [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441],[280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296],{"_297":403,"_120":403,"_118":133,"_271":404},{"_297":398,"_120":398,"_118":133,"_271":399},{"_297":394,"_120":394,"_118":133,"_271":395},{"_297":389,"_120":389,"_118":133,"_271":390},{"_297":384,"_120":384,"_118":133,"_271":385},{"_297":372,"_120":372,"_118":133,"_271":373},{"_297":360,"_120":360,"_118":133,"_271":361},{"_297":355,"_120":355,"_118":133,"_271":356},{"_297":344,"_120":344,"_118":133,"_271":345},{"_297":339,"_120":339,"_118":133,"_271":340},{"_297":334,"_120":334,"_118":133,"_271":335},{"_297":329,"_120":329,"_118":133,"_271":330},{"_297":324,"_120":324,"_118":133,"_271":325},{"_297":319,"_120":319,"_118":133,"_271":320},{"_297":314,"_120":314,"_118":133,"_271":315},{"_297":307,"_120":307,"_118":133,"_271":308},{"_297":298,"_120":298,"_118":133,"_271":299},"hostName","amazon-adsystem.com",[300,301],{"_265":306,"_303":298,"_304":20,"_118":305},{"_265":302,"_303":298,"_304":20,"_118":305},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[309,310],{"_265":313,"_303":307,"_304":20,"_118":312},{"_265":311,"_303":307,"_304":20,"_118":312},"IDE","This domain is owned by Doubleclick (Google). 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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If you do not allow these cookies, then some or all of these services may not function properly.",[530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550],[495,496,497,498,499],{"_297":389,"_120":389,"_118":133,"_271":521},{"_297":355,"_120":355,"_118":133,"_271":516},{"_297":509,"_120":509,"_118":133,"_271":510},{"_297":505,"_120":505,"_118":133,"_271":506},{"_297":500,"_120":500,"_118":133,"_271":501},"nvcrm.my.site.com",[502],{"_265":503,"_303":500,"_304":20,"_118":504},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[507],{"_265":508,"_303":505,"_304":20,"_118":338},"edge","github.com",[511,512],{"_265":515,"_303":509,"_304":20,"_118":514},{"_265":513,"_303":509,"_304":20,"_118":514},"logged_in","This domain is owned by Github. 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