Model Overview

Terms of use:

GOVERNING TERMS: By accessing this engine, you are agreeing to NVIDIA TensorRT Engine License.

ATTRIBUTION: MIT License (https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE)

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case.

Description:

Phi-2 is a 2.7 billion parameter language model developed by Microsoft Research. The phi-2 model is best suited for prompts using the Question-Answer (QA) format, the chat format, and the code format. TensorRT engines optimizing Phi-2 are generated by TensorRT-Cloud.

These TensorRT engines are ready for commercial use.

References(s):

Phi-2 on HuggingFace
Microsoft Research blog post about Phi-2

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Phi

Input:

Input Type(s): Text
\nInput Format: Text
\nInput Parameters: Temperature, TopP
\nOther Properties Related to Input: None

Output:

Output Type(s): Text
\nOutput Format: Text
\nOutput Parameters: Max output tokens
\nOther Properties Related to Output: None

Inference:

TRT-LLM Inference Engine

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.

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\n\n**ATTRIBUTION:** MIT License ([https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE)) \n### Third-Party Community Consideration \nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case. \n\n### Description: \nPhi-2 is a 2.7 billion parameter language model developed by Microsoft Research. The phi-2 model is best suited for prompts using the Question-Answer (QA) format, the chat format, and the code format. TensorRT engines optimizing Phi-2 are generated by TensorRT-Cloud. \n\nThese TensorRT engines are ready for commercial use. \n\n### References(s): \n* Phi-2 on [HuggingFace](https://huggingface.co/microsoft/phi-2)
\n* Microsoft Research [blog post](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/) about Phi-2 \n\n### Model Architecture: \n**Architecture Type:** Transformer
\n**Network Architecture:** Phi \n\n### Input: \n**Input Type(s):** Text
\n**Input Format:** Text
\n**Input Parameters:** Temperature, TopP
\n**Other Properties Related to Input:** None \n \n### Output: \n**Output Type(s):** Text
\n**Output Format:** Text
\n**Output Parameters:** Max output tokens
\n**Other Properties Related to Output:** None \n\n### Inference: \n[TRT-LLM Inference Engine](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/phi)\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. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","Phi-2 (TensorRT LLM)",["D",1718393041676],"/api/image/nvidia/-/models/phi_2_trtllm",5131806100,"bias","explainability","privacy","safetyAndSecurity",[],[268,269],[],[],[],{"_88":279,"_271":280,"_273":281,"_275":276},{"_88":270,"_271":272,"_273":274,"_275":276},"TensorRT","value","pltfm_tensorrt","filter",{"_277":278,"_271":272},"category","label","field","platform","AI","soln_ai",{"_277":282,"_271":280},"technology",{"_147":1257,"_149":1258},"ngcApps",[1210],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1192":1193,"_1194":1195,"_1196":1197,"_1198":1199,"_1200":294,"_179":318,"_1201":142,"_1202":51,"_1203":123,"_1204":123,"_1205":1206,"_1207":123,"_1208":1209},"amplitude",{"_1189":1190,"_831":-7,"_1191":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1183":1184,"_1185":1186,"_1187":1188},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1071":204,"_1072":1073,"_1074":1075,"_1076":1077},"announcements",[],"consentState",{"_357":1061,"_1062":123,"_1063":26,"_1064":26,"_1065":1066},"consentConfig",{"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358},"showContactAdmin",1784672880964,[],[],[332,333,334,335,336,337,338],{"_40":54,"_42":64,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":63,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":61,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":60,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":60,"_46":-5,"_48":-5,"_50":51},"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. 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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