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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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Model Overview

Description:

The Nemovision-4B-Instruct model uses the latest NVIDIA recipe for distilling, pruning and quantizing to make it small enough to be performant on a broad range of RTX GPUs with the accuracy developers need. This is a model for generating responses for roleplaying, retrieval augmented generation, and function calling with vision understanding and reasoning capabilities. VRAM usage has been minimized to approximately 3.5 GB, providing fast Time to First Token.\nThis model is ready for commercial use.

License/Terms of Use

The use of this model is governed by\nthe NVIDIA AI Foundation Models Community License Agreement

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture

Vision Encoder: openai/clip-vit-large-patch14
Language Encoder: Nemotron-Mini-4B-Instruct

Input

Input Type(s): Image(s), Text
\nInput Format(s): Red, Green, Blue (RGB), String
\nInput Parameters: 2D, 1D
\nOther Properties Related to Input: The model has a maximum of 4096 input tokens.

Output

Output Type(s): Text
\nOutput Format(s): String
\nOutput Parameters: 1D
\nOther Properties Related to Input: The model has a maximum of 4096 input tokens. Maximum output for both versions can be set apart from input.

Prompt Format:

Single Turn

System\n{system prompt}\n\nUser\n\n{prompt}\nAssistant\\n\n\n\">

<extra_id_0>System\n{system prompt}\n\n<extra_id_1>User\n<image>\n{prompt}\n<extra_id_1>Assistant\\n\n\n

System\n{system prompt}\n\nUser\n{prompt}\nAssistant\\n\n\n\">

<extra_id_0>System\n{system prompt}\n\n<extra_id_1>User\n{prompt}\n<extra_id_1>Assistant\\n\n\n

Multi-image

System\n{system prompt}\n\nUser\n\n\n\n{prompt}\nAssistant\\n\n\n\">

<extra_id_0>System\n{system prompt}\n\n<extra_id_1>User\n<image>\n<image>\n<image>\n{prompt}\n<extra_id_1>Assistant\\n\n\n

Multi-Turn or Few-shot

System\n{system prompt}\n\n ... \n ... \n\nUser\n{prompt}\nAssistant\n ... \nTool\n{tool response}\nAssistant\\n\n\n\">

<extra_id_0>System\n{system prompt}\n\n<tool> ... </tool>\n<context> ... </context>\n\n<extra_id_1>User\n{prompt}\n<extra_id_1>Assistant\n<toolcall> ... </toolcall>\n<extra_id_1>Tool\n{tool response}\n<extra_id_1>Assistant\\n\n\n

Software Integration:

Runtime(s): AI Inference Manager (NVAIM) Version 1.0.0

Supported Hardware Microarchitecture Compatibility: GPU supporting DirectX 11/12 and Vulkan 1.2 or higher

[Preferred/Supported] Operating System(s):

Windows

Software Integration: (Cloud)

[Preferred/Supported] Operating System(s):

Linux

Training & Evaluation:

Training Dataset:

NV-Pretraining and NV-VILA-SFT data were used.\nAdditionally,the following datasets were used:

Data Collection Method by dataset:

Hybrid: Automated, Human

Labeling Method by dataset:

Hybrid: Automated, Human

Properties:

NV-Pretraining data was collected from 5M subsampled NV-CLIP dataset. Stage 3 NV-SFT data has 3.47M unique images and 3.78M annotations on images that only have commercial license. Trained on commercial text dataset.

Evaluation Dataset:

Data Collection Method by dataset

Hybrid: Automated, Human

Labeling Method by dataset

Human

Properties:

A collection of different benchmarks, including academic VQA benchmarks and recent benchmarks specifically proposed for language understanding and reasoning, instruction-following, and function calling LMMs.

\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	VQAv2	GQA	SQA Image	Text VQA	POPE (Popular)	MMBench-en	SEED	SEED Image	MMMU val (beam 5)
Accuracy	73.92	53.47	69.81	57.03	87.13	59.96	58.89	66.18	36.8

Berkeley Function Calling

\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	Simple	Multiple Functions	Parallel Functions	Parallel Multiple	Relevance
Accuracy	85.25	90	77.5	76.5	17.08

Instruction Following Eval

\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 Level Accuracy	Instruction Level Accuracy
Accuracy	46.95	57.79

Inference:

Test Hardware:

H100
A100
A10g
L40s

Supported Hardware Platform(s): L40s, A10g, A100, H100

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 report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/ace/models/nemovision-4b-instruct/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/ace/nemovision-4b-instruct:1.0.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/ace/models/nemovision-4b-instruct/-/version-history","accessCheckResult",{"_103":104,"_105":106,"_107":20},"hasRelatedCollections","context",{"_99":100,"_101":102},1783756311273,[],[],[92,93,94,95,96,97],{"_33":47,"_35":57,"_37":49,"_39":50,"_41":56,"_43":44},{"_33":47,"_35":54,"_37":38,"_39":55,"_41":56,"_43":44},{"_33":47,"_35":52,"_37":38,"_39":55,"_41":98,"_43":44},{"_33":47,"_35":48,"_37":49,"_39":50,"_41":51,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":42,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},61,"orgName","nvidia","teamName","ace","reason","subscription_required","requiredProducts",[108],"allowed","ace-ea","id","nvidia/ace/nemovision-4b-instruct","key","nvidia/ace/models/nemovision-4b-instruct","resourceType","models","resourceId","nemovision-4b-instruct","shortDescription","Generates responses for roleplaying, retrieval augmented generation, and function calling with vision understanding and reasoning capabilities.","description","# Model Overview\n\n## Description:\n\nThe Nemovision-4B-Instruct model uses the latest NVIDIA recipe for distilling, pruning and quantizing to make it small enough to be performant on a broad range of RTX GPUs with the accuracy developers need. This is a model for generating responses for roleplaying, retrieval augmented generation, and function calling with vision understanding and reasoning capabilities. VRAM usage has been minimized to approximately 3.5 GB, providing fast Time to First Token.\nThis model is ready for commercial use.\n\n## License/Terms of Use\n\nThe use of this model is governed by\nthe [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license)\n\n## Model Architecture:\n\n**Architecture Type:** Transformer
\n**Network Architecture**\n- Vision Encoder: openai/clip-vit-large-patch14\n- Language Encoder: Nemotron-Mini-4B-Instruct\n\n\n## Input\n**Input Type(s):** Image(s), Text
\n**Input Format(s):** Red, Green, Blue (RGB), String
\n**Input Parameters:** 2D, 1D
\n**Other Properties Related to Input:** The model has a maximum of 4096 input tokens.
\n\n## Output\n**Output Type(s):** Text
\n**Output Format(s):** String
\n**Output Parameters:** 1D
\n**Other Properties Related to Input:** The model has a maximum of 4096 input tokens. Maximum output for both versions can be set apart from input.
\n\n## Prompt Format:\n\n\n**Single Turn**\n\n```\nSystem\n{system prompt}\n\nUser\n\n{prompt}\nAssistant\\n\n\n```\n\n```\nSystem\n{system prompt}\n\nUser\n{prompt}\nAssistant\\n\n\n```\n\n**Multi-image**\n\n```\nSystem\n{system prompt}\n\nUser\n\n\n\n{prompt}\nAssistant\\n\n\n```\n\n**Multi-Turn or Few-shot**\n\n```\nSystem\n{system prompt}\n\n ... \n ... \n\nUser\n{prompt}\nAssistant\n ... \nTool\n{tool response}\nAssistant\\n\n\n```\n\n## Software Integration:\n\n**Runtime(s):** AI Inference Manager (NVAIM) Version 1.0.0
\n\n**Supported Hardware Microarchitecture Compatibility:** GPU supporting DirectX 11/12 and Vulkan 1.2 or higher
\n\n**[Preferred/Supported] Operating System(s):**
\n* Windows
\n\n## Software Integration: (Cloud)\n\n**[Preferred/Supported] Operating System(s):**
\n* Linux
\n\n# Training & Evaluation: \n\n## Training Dataset:\nNV-Pretraining and NV-VILA-SFT data were used.\nAdditionally,the following datasets were used:\n* [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1)\n* [OASST2](https://huggingface.co/datasets/OpenAssistant/oasst2)\n* [Localized Narratives](https://google.github.io/localized-narratives/)\n* [TextCaps](https://textvqa.org/textcaps/dataset/)\n* [TextVQA](https://textvqa.org/dataset/)\n* [RefCOCO](https://github.com/lichengunc/refer)\n* [VQAv2](https://visualqa.org/)\n* [GQA](https://cs.stanford.edu/people/dorarad/gqa/index.html)\n* [SynthDoG-en](https://huggingface.co/datasets/naver-clova-ix/synthdog-en)\n* [A-OKVQ](https://github.com/allenai/aokvqa)\n* [WIT](https://github.com/google-research-datasets/wit)\n* [CLEVR](https://cs.stanford.edu/people/jcjohns/clevr/)\n* [CLEVR-X](https://github.com/ExplainableML/CLEVR-X)\n* [CLEVR-Math](https://huggingface.co/datasets/dali-does/clevr-math)\n\n**Data Collection Method by dataset:**
\n- Hybrid: Automated, Human
\n\n**Labeling Method by dataset:**
\n- Hybrid: Automated, Human
\n\n**Properties:**
\n\nNV-Pretraining data was collected from 5M subsampled NV-CLIP dataset. Stage 3 NV-SFT data has 3.47M unique images and 3.78M annotations on images that only have commercial license. Trained on commercial text dataset.\n\n## Evaluation Dataset:\n**Data Collection Method by dataset**
\n* Hybrid: Automated, Human
\n\n**Labeling Method by dataset**
\n* Human
\n\n**Properties:** \n\nA collection of different benchmarks, including academic VQA benchmarks and recent benchmarks specifically proposed for language understanding and reasoning, instruction-following, and function calling LMMs.
\n\n* [VQAv2](https://visualqa.org/)\n* [GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html)\n* [ScienceQA Image](https://scienceqa.github.io/)\n* [Text VQA](https://textvqa.org/)\n* [POPE](https://github.com/AoiDragon/POPE)\n* [MMBench](https://github.com/open-compass/MMBench)\n* [SEED-Bench](https://github.com/AILab-CVC/SEED-Bench)\n* [MMMU](https://mmmu-benchmark.github.io/)\n* [IF-Eval](https://github.com/google-research/google-research/tree/master/instruction_following_eval)\n* [BFCL](https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html)\n\n|Benchmark|VQAv2|GQA |SQA Image|Text VQA|POPE (Popular)|MMBench-en|SEED |SEED Image|MMMU val (beam 5)|\n|---------|-----|------|---------|--------|--------------|----------|-----|----------|-----------------|\n|Accuracy |73.92|53.47 |69.81 |57.03 |87.13 |59.96 |58.89|66.18 |36.8 |\n\nBerkeley Function Calling\n|Benchmark|Simple |Multiple Functions|Parallel Functions|Parallel Multiple|Relevance|\n|---------|-----------|-------------------|------------------|-----------------|---------|\n|Accuracy |85.25 |90 |77.5 |76.5 |17.08 |\n\nInstruction Following Eval\n|Benchmark|Prompt Level Accuracy|Instruction Level Accuracy\t |\n|---------|---------------------|------------------------------|\n|Accuracy |46.95 |57.79 |\n\n## Inference:\n**Test Hardware:**
\n* H100
\n* A100
\n* A10g
\n* L40s
\n\n**Supported Hardware Platform(s):** L40s, A10g, A100, H100
\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\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Nemovision-4B-Instruct","updatedDate",["D",1734722958200],"publisher","NVIDIA","latestVersion","1.0.0","versions","collectionCounts","imageUrl","/api/image/nvidia/ace/models/nemovision-4b-instruct","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",9375625310,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","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","privacy","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Visual Question Answering. |\n| Model Type: | Vision Language Model |\n| Intended Users: | The primary intended users of the model are practitioners and researchers in computer vision, natural language processing, machine learning, and artificial intelligence. |\n| Output: | Text |\n| Describe how the model works: | Chat based on image content. Chat for roleplaying, retrieval augmented generation, and function calling. |\n| Technical Limitations: | This model may not perform well on domain specific images. |\n| Known Risk: | The Model may produce output that is biased, toxic, or incorrect responses. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The Model may also generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text, producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Visual Question Answering (VQA), GQA, MMBench-en, MMMU, SQA-Image, etc. Text based evaluations include Automatic Evaluation - Commonsense Reasoning & Language Understanding, Automatic Evaluation - Open LLM Leaderboard, Instruction Following, Berkeley Function Calling, and ChatRAG-Bench. |\n| Potential Known Risks: | None Known |\n| Licensing: | [NVIDIA AI Foundation Models Community License](https://docs.nvidia.com/ai-foundation-models-community-license.pdf) |","safetyAndSecurity","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any personal data used? | Not Applicable\nPersonal data used to create this model? | Datasets used for fine-tuning did not introduce any personal data that did not exist in the base model.\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 is collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf personal data is 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 is 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","featureLabels",[196],"displayLabels",[167,168,169,170,171,172],"policyLabels",[],"systemLabels",[],"products",[160],"hasNimMetadata",{"_161":108,"_121":162,"_117":163,"_164":165},"name","NVIDIA ACE Early Access","NVIDIA ACE Early Access is for assets towards the ACE early access. 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The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1172":1173,"_1174":1175,"_1176":1177,"_1178":1179},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[281,282,283,284,285],{"_286":823,"_161":824,"_119":825,"_290":467,"_291":826,"_293":827,"_295":828},{"_286":613,"_161":614,"_119":615,"_290":20,"_291":42,"_293":616,"_295":617},{"_286":513,"_161":514,"_119":515,"_290":20,"_291":260,"_293":516,"_295":517},{"_286":297,"_161":298,"_119":299,"_290":20,"_291":300,"_293":301,"_295":302},{"_286":287,"_161":288,"_119":289,"_290":20,"_291":292,"_293":294,"_295":296},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. 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.",5,[430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464],[303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319],{"_320":426,"_121":426,"_119":142,"_293":427},{"_320":421,"_121":421,"_119":142,"_293":422},{"_320":417,"_121":417,"_119":142,"_293":418},{"_320":412,"_121":412,"_119":142,"_293":413},{"_320":407,"_121":407,"_119":142,"_293":408},{"_320":395,"_121":395,"_119":142,"_293":396},{"_320":383,"_121":383,"_119":142,"_293":384},{"_320":378,"_121":378,"_119":142,"_293":379},{"_320":367,"_121":367,"_119":142,"_293":368},{"_320":362,"_121":362,"_119":142,"_293":363},{"_320":357,"_121":357,"_119":142,"_293":358},{"_320":352,"_121":352,"_119":142,"_293":353},{"_320":347,"_121":347,"_119":142,"_293":348},{"_320":342,"_121":342,"_119":142,"_293":343},{"_320":337,"_121":337,"_119":142,"_293":338},{"_320":330,"_121":330,"_119":142,"_293":331},{"_320":321,"_121":321,"_119":142,"_293":322},"hostName","amazon-adsystem.com",[323,324],{"_161":329,"_326":321,"_327":20,"_119":328},{"_161":325,"_326":321,"_327":20,"_119":328},"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",[332,333],{"_161":336,"_326":330,"_327":20,"_119":335},{"_161":334,"_326":330,"_327":20,"_119":335},"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.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. 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. 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.",[553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573],[518,519,520,521,522],{"_320":412,"_121":412,"_119":142,"_293":544},{"_320":378,"_121":378,"_119":142,"_293":539},{"_320":532,"_121":532,"_119":142,"_293":533},{"_320":528,"_121":528,"_119":142,"_293":529},{"_320":523,"_121":523,"_119":142,"_293":524},"nvcrm.my.site.com",[525],{"_161":526,"_326":523,"_327":20,"_119":527},"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",[530],{"_161":531,"_326":528,"_327":20,"_119":361},"edge","github.com",[534,535],{"_161":538,"_326":532,"_327":20,"_119":537},{"_161":536,"_326":532,"_327":20,"_119":537},"logged_in","This domain is owned by Github. 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Information collected with these cookies is used to measure and find ways to improve website 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