Model Overview

Terms of use:

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

ATTRIBUTION: Gemma Terms of Use available at https://ai.google.dev/gemma/terms; and Gemma Prohibited Use Policy available at https://ai.google.dev/gemma/prohibited_use_policy.

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:

Gemma-2B is a 2.5B parameter model from Gemma family of models from Google. It has been instruction-tuned so it can respond to prompts in a conversational manner. Nvidia has converted original Gemma weights and format into weights and format that can be consumed by TensorRT-LLM. TensorRT engines optimizing Gemma-2B are generated by TensorRT-Cloud.

These TensorRT engines are ready for commercial use. \n

References(s):

Gemma Model Card
Gemma blogpost

Input:

Input Format: Text
\nInput Parameters: None \n

Output:

Output Format: Text
\nOutput Parameters: None

Software Integration:

Check the version tag for engine support matrix\n

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:** Gemma Terms of Use available at [https://ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms); and Gemma Prohibited Use Policy available at [https://ai.google.dev/gemma/prohibited_use_policy](https://ai.google.dev/gemma/prohibited_use_policy).\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### Description:\n\nGemma-2B is a 2.5B parameter model from Gemma family of models from Google. It has been instruction-tuned so it can respond to prompts in a conversational manner. Nvidia has converted original Gemma weights and format into weights and format that can be consumed by TensorRT-LLM. TensorRT engines optimizing Gemma-2B are generated by TensorRT-Cloud. \n\nThese TensorRT engines are ready for commercial use. \n \n### References(s): \n* Gemma [Model Card](https://ai.google.dev/gemma/docs/model_card) \n* Gemma [blogpost](https://blog.google/technology/developers/gemma-open-models/)
\n### Input: \n\n**Input Format:** Text
\n**Input Parameters:** None \n \n### Output: \n**Output Format:** Text
\n**Output Parameters:** None \n\n### Software Integration: \nCheck the version tag for engine support matrix\n \n### Inference: \n\n[TRT-LLM Inference Engine](https://github.com/NVIDIA/TensorRT-LLM/tree/gemma/examples/gemma)\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. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ","Gemma 2B Instruct (TensorRT LLM)",["D",1718392535902],"/api/image/nvidia/-/models/gemma_2b_it_trtllm","bias","explainability","privacy","safetyAndSecurity",[],[271,272],[],[],[],{"_91":282,"_274":283,"_276":284,"_278":279},{"_91":273,"_274":275,"_276":277,"_278":279},"TensorRT","value","pltfm_tensorrt","filter",{"_280":281,"_274":275},"category","label","field","platform","AI","soln_ai",{"_280":285,"_274":283},"technology",{"_147":1457,"_149":1458},"ngcApps",[1410],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1391":1392,"_1393":1394,"_1395":1396,"_1397":1398,"_1399":297,"_179":321,"_1400":1401,"_1402":51,"_1403":126,"_1404":126,"_1405":1406,"_1407":126,"_1408":1409},"amplitude",{"_1388":1389,"_994":-7,"_1390":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1382":1383,"_1384":1385,"_1386":1387},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1269":203,"_1270":1271,"_1272":1273,"_1274":1275},"announcements",[],"consentState",{"_359":1259,"_1260":126,"_1261":26,"_1262":26,"_1263":1264},"consentConfig",{"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360},"showContactAdmin",1783608167730,[],[],[335,336,337,338,339,340],{"_40":55,"_42":66,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_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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