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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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Cosmos-1.0-Guardrail

Cosmos | Code | Paper

Model Overview

Description:

Cosmos World Foundation Models: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware videos and world states for physical AI development.

Cosmos Guardrail is a content safety model comprising of four components that enforce content safety. The components are as follows.

\n
Aegis-AI-Content-Safety-LlamaGuard-LLM-Defensive-1.0 which is a LLM content safety model. It is a parameter efficient instruction tuned version of Llama Guard based on Llama2-7B trained on Nvidia's content safety dataset Aegis Content Safety Dataset covering Nvidia's broad taxonomy of 13 critical safety risk categories. See model card here.
\n
\n
Blocklist: A set of human-curated keywords that are used to filter our corner-cases.
\n
\n
Video Content Safety Filter: A multi-class classifier model that is trained to distinguish between safe and unsafe frames of the generated video using SigLIP embeddings google/siglip-so400m-patch14-384.
\n
\n
Face Blur Filter: A pixelation filter that uses RetinaFace to identify facial regions with high confidence scores and apply pixelation to any detections larger than 20x20 pixels.
\n

Model Developer: NVIDIA

Model Versions

Cosmos 1.0 ships with Cosmos-1.0-Guardrail.

License:

This model is released under the NVIDIA Open Model License. For a custom license, please contact cosmos-license@nvidia.com.

Under the NVIDIA Open Model License, NVIDIA confirms:

Models are commercially usable.
You are free to create and distribute Derivative Models.
NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.

Important Note: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or\nassociated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained\nin the Model, your rights under NVIDIA Open Model License Agreement will automatically terminate.

Additional Information: LLAMA 2 COMMUNITY LICENSE AGREEMENT.

Model Architecture:

Architecture Type: Transformer, MLP
\nNetwork Architecture: Llama 2

Input/Output Specifications

Input Type(s): Text, Image, Video
Input Format(s):\n
- Text: String
- Image: jpg, png, jpeg, webp
- Video: mp4
\n
Input Parameters:\n
- Text: One-dimensional (1D)
- Image: Two-dimensional (2D)
- Video: Three-dimensional (3D)
\n

Output:

Output Type(s): Boolean, Text, Video
Input Format(s):\n
- Boolean: True for safe and False for unsafe
- Text: Reason for the unsafe determination
- Video: Model output where faces are blurred
\n

Software Integration:

Runtime Engine(s):

Cosmos

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
\nSupported Operating System(s): Linux

Model Version:

The initial release (v1.0) of Cosmos Guardrail contains the following model:

Cosmos-1.0-Guardrail

Software Integration

Runtime Engine(s):

Cosmos

Supported Hardware Microarchitecture Compatibility:

NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Ampere

Operating System(s):

Linux (We have not tested on other operating systems.)

Usage

See Cosmos on how to use the model.

Example for the prompt-checking portion of the Guardrail:

\n
Input: \"A dog is playing with a ball.\"
\n
\n
Output: Guardrail allows the generation of this video
\n
\n
Input: \"A man wearing only socks.\"
\n
\n
Output: Guardrail blocks generation of this video
\n

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.

For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[138,139],"resourceVersionsTotal",1,"hasMoreVersions","versionMetadata",{"_109":110,"_111":112,"_37":113,"_114":-5,"_115":116,"_117":118,"_119":120,"_121":122,"_123":20,"_124":-5,"_125":20,"_126":-5,"_127":20,"_128":-5,"_129":20,"_130":-5,"_131":20,"_132":-5,"_133":20,"_134":-5,"_135":20,"_136":137},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/cosmos/models/cosmos-1.0-guardrail/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/cosmos/cosmos-1.0-guardrail:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/cosmos/models/cosmos-1.0-guardrail/-/version-history","accessCheckResult",{"_108":90},"hasRelatedCollections",true,"context",{"_104":105,"_106":107},1787526386188,[],[],[97,98,99,100,101,102],{"_33":48,"_35":57,"_37":50,"_39":51,"_41":58,"_43":44},{"_33":48,"_35":55,"_37":50,"_39":51,"_41":56,"_43":44},{"_33":48,"_35":53,"_37":50,"_39":51,"_41":103,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":51,"_41":52,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},68,"orgName","nvidia","teamName","cosmos","allowed","versionId","1.0","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1736226327835],"updatedDate",["D",1740776436181],"totalSizeInBytes",34454484020,"totalFileCount",146,"isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_140":80,"_141":143,"_114":-5,"_123":20,"_125":20,"_136":142,"_127":20},{"_140":110,"_141":110,"_114":-5,"_123":20,"_125":20,"_136":142,"_127":20},"id","name",[],"Latest (1.0)","nvidia/cosmos/cosmos-1.0-guardrail","key","nvidia/cosmos/models/cosmos-1.0-guardrail","resourceType","models","resourceId","cosmos-1.0-guardrail","shortDescription","Cosmos Guardrail is a content safety model comprising of four components that enforce content safety.","# **Cosmos-1.0-Guardrail**\n\n[**Cosmos**](https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6) | [**Code**](https://github.com/NVIDIA/Cosmos) | [**Paper**](https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai)\n\n# Model Overview\n\n## Description:\n**Cosmos World Foundation Models**: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware videos and world states for physical AI development.\n\nCosmos Guardrail is a content safety model comprising of four components that enforce content safety. The components are as follows.\n\n1. Aegis-AI-Content-Safety-LlamaGuard-LLM-Defensive-1.0 which is a LLM content safety model. It is a parameter efficient instruction tuned version of Llama Guard based on Llama2-7B trained on Nvidia's content safety dataset Aegis Content Safety Dataset covering Nvidia's broad taxonomy of 13 critical safety risk categories. See model card [here](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0).\n\n2. Blocklist: A set of human-curated keywords that are used to filter our corner-cases.\n\n3. Video Content Safety Filter: A multi-class classifier model that is trained to distinguish between safe and unsafe frames of the generated video using SigLIP embeddings [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384).\n\n4. Face Blur Filter: A pixelation filter that uses [RetinaFace](https://github.com/biubug6/Pytorch_Retinaface) to identify facial regions with high confidence scores and apply pixelation to any detections larger than 20x20 pixels.\n\n**Model Developer**: NVIDIA\n\n## Model Versions\n\nCosmos 1.0 ships with [Cosmos-1.0-Guardrail](https://huggingface.co/nvidia/Cosmos-1.0-Guardrail).\n\n## License:\nThis model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). For a custom license, please contact [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com).\n\nUnder the NVIDIA Open Model License, NVIDIA confirms:\n\n* Models are commercially usable.\n* You are free to create and distribute Derivative Models.\n* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.\n\n**Important Note**: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or\nassociated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained\nin the Model, your rights under [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) will automatically terminate.\n\n**Additional Information**: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://github.com/meta-llama/llama-models/blob/main/models/llama2/LICENSE).\n\n## Model Architecture:\n**Architecture Type:** Transformer, MLP
\n**Network Architecture:** Llama 2
\n\n## Input/Output Specifications\n\n * **Input Type(s)**: Text, Image, Video\n * **Input Format(s)**:\n * Text: String\n * Image: jpg, png, jpeg, webp\n * Video: mp4\n * **Input Parameters**:\n * Text: One-dimensional (1D)\n * Image: Two-dimensional (2D)\n * Video: Three-dimensional (3D)\n\n## Output:\n * **Output Type(s):** Boolean, Text, Video
\n * **Input Format(s)**:\n * Boolean: True for safe and False for unsafe\n * Text: Reason for the unsafe determination\n * Video: Model output where faces are blurred\n\n\n## Software Integration:\n**Runtime Engine(s):**\n* [Cosmos](https://github.com/NVIDIA/Cosmos)\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n**Supported Operating System(s):** Linux
\n\n## Model Version:\nThe initial release (v1.0) of Cosmos Guardrail contains the following model:\n- [Cosmos-1.0-Guardrail](https://huggingface.co/nvidia/Cosmos-1.0-Guardrail)\n\n## Software Integration\n**Runtime Engine(s):**\n* [Cosmos](https://github.com/NVIDIA/Cosmos)\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Blackwell\n* NVIDIA Hopper\n* NVIDIA Ampere\n\n**Operating System(s):**\n* Linux (We have not tested on other operating systems.)\n\n\n# Usage\n\nSee [Cosmos](https://github.com/NVIDIA/Cosmos) on how to use the model.\n\nExample for the prompt-checking portion of the Guardrail:\n\n* Input: `\"A dog is playing with a ball.\"`\n* Output: Guardrail allows the generation of this video\n\n* Input: `\"A man wearing only socks.\"`\n* Output: Guardrail blocks generation of this video\n\n## Ethical Considerations\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\nFor more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Cosmos-1.0-Guardrail",["D",1736232664282],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/cosmos/models/cosmos-1.0-guardrail","rawImageUrl","https://nvdam.widen.net/content/xbsnft8sm2/jpeg/cosmos-thumbnail-274x152.jpeg?position=c&color=ffffffff&quality=100&u=hdilxr&use=c2ccj","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | Content moderation for world generation\nModel Type: | Ensemble\nIntended Users: | Generative AI developers for world generation models\nOutput: | Boolean\nDescribe how the model works: | Check safety of input prompts or generated videos and output a safety classification\nTechnical Limitations: | The model may not moderate input prompt accurately and may have incorrect responses.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Human Evaluation\nPotential Known Risks: | The model's output can potentially classify content considered toxic, offensive, or indecent as safe.\nLicensing: | Governing Terms: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). Additional Information: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://github.com/meta-llama/llama-models/blob/main/models/llama2/LICENSE).","privacy","Field | Response\n:------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal information? | None Known\nProtected class data used to create this model? | None Known\nWas consent obtained for any personal data used? | None Known\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Prompt moderation for world generation\nDescribe the life critical impact (if present). | None Known\nUse Case Restrictions: | Governing Terms: Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). Additional Information: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://github.com/meta-llama/llama-models/blob/main/models/llama2/LICENSE).\nModel 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. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.","featureLabels",[],"displayLabels",[190],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_154":191,"_192":191,"_193":194,"_195":196},"NSPECT-72FQ-Z3GK","value","filter",{"_197":196,"_192":191},"category","label","field","appConfig",{"_1179":1180,"_232":1181},"ngcApps",[1132],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1113":1114,"_1115":1116,"_1117":1118,"_1119":1120,"_1121":210,"_79":239,"_1122":1123,"_1124":44,"_1125":90,"_1126":90,"_1127":1128,"_1129":90,"_1130":1131},"amplitude",{"_1110":1111,"_751":-7,"_1112":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1104":1105,"_1106":1107,"_1108":1109},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"now",1787526386055,"1.12.0","defaultSearchSuggestions",{"_991":112,"_992":993,"_994":995,"_996":997},"announcements",[],"consentState",{"_277":981,"_982":90,"_983":20,"_984":20,"_985":986},"consentConfig",{"_259":260,"_261":262,"_263":264,"_265":266,"_267":268,"_269":270,"_271":272,"_273":274,"_275":276,"_277":278},"showContactAdmin",1787526386056,[],[],[253,254,255,256,257],{"_33":48,"_35":57,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":258,"_43":44},3,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","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. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532],[479,480,481,482,483],{"_318":393,"_154":393,"_111":112,"_291":503},{"_318":367,"_154":367,"_111":112,"_291":498},{"_318":493,"_154":493,"_111":112,"_291":494},{"_318":488,"_154":488,"_111":112,"_291":489},{"_318":484,"_154":484,"_111":112,"_291":485},"nvidia.partners",[486],{"_141":487,"_324":484,"_325":20,"_111":361},"edge","app.hushly.com",[490],{"_141":491,"_324":488,"_325":20,"_111":492},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[495],{"_141":496,"_324":493,"_325":20,"_111":497},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619],[571,572,573,574],{"_318":586,"_154":586,"_111":112,"_291":587},{"_318":581,"_154":581,"_111":112,"_291":582},{"_318":380,"_154":380,"_111":112,"_291":578},{"_318":493,"_154":493,"_111":112,"_291":575},[576],{"_141":577,"_324":493,"_325":90,"_111":497},"_gh_sess",[579],{"_141":580,"_324":380,"_325":90,"_111":387},"YSC","bizible.com",[583],{"_141":584,"_324":581,"_325":20,"_111":585},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[588],{"_141":589,"_324":586,"_325":20,"_111":590},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_141":661,"_324":439,"_325":20,"_111":662},{"_141":646,"_324":660,"_325":90,"_111":647},{"_141":629,"_324":442,"_325":20,"_111":554},{"_141":629,"_324":542,"_325":20,"_111":554},{"_141":658,"_324":439,"_325":20,"_111":659},{"_141":646,"_324":445,"_325":90,"_111":647},{"_141":656,"_324":439,"_325":20,"_111":657},{"_141":646,"_324":442,"_325":90,"_111":647},{"_141":654,"_324":541,"_325":20,"_111":655},{"_141":646,"_324":563,"_325":90,"_111":647},{"_141":626,"_324":442,"_325":20,"_111":628},{"_141":652,"_324":439,"_325":90,"_111":653},{"_141":650,"_324":439,"_325":20,"_111":651},{"_141":648,"_324":439,"_325":90,"_111":649},{"_141":646,"_324":537,"_325":90,"_111":647},{"_141":644,"_324":439,"_325":20,"_111":645},{"_141":642,"_324":439,"_325":20,"_111":643},{"_141":640,"_324":439,"_325":90,"_111":641},{"_141":638,"_324":546,"_325":20,"_111":639},{"_141":626,"_324":537,"_325":20,"_111":628},{"_141":636,"_324":439,"_325":20,"_111":637},{"_141":634,"_324":439,"_325":90,"_111":635},{"_141":632,"_324":439,"_325":20,"_111":633},{"_141":630,"_324":439,"_325":20,"_111":631},{"_141":629,"_324":439,"_325":20,"_111":554},{"_141":626,"_324":627,"_325":20,"_111":628},{"_141":624,"_324":439,"_325":20,"_111":625},{"_141":622,"_324":439,"_325":90,"_111":623},{"_141":620,"_324":439,"_325":20,"_111":621},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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