Model Overview

Terms of use

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

ATRIBUTTION: Apache License 2.0 | Choose a 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:

Mistral-7B-Instruct is a language model that can follow instructions, complete requests, and generate creative text formats. It is an instruct version of the Mistral-7B-v0.2 generative text model fine-tuned using a variety of publicly available conversation datasets. TensorRT engines optimizing Mistral-7B-Instruct are generated by TensorRT-Cloud.

These TensorRT engines are ready for commercial use.

References(s):

Mistral 7B Instruct Model Card on Hugging Face
\nMistral 7B paper
\nMistral 7B blogpost

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Mistral-7B
\nModel Version: v0.1

Input:

Input Format: Text
\nInput Parameters: Max Tokens, Temperature, Top P

Output:

Output Format: Text
\nOutput Parameters: None

Software Integration:

Check the version tag for engine support matrix

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**ATRIBUTTION:** [Apache License 2.0 | Choose a License](https://choosealicense.com/licenses/apache-2.0/) \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: \nMistral-7B-Instruct is a language model that can follow instructions, complete requests, and generate creative text formats. It is an instruct version of the Mistral-7B-v0.2 generative text model fine-tuned using a variety of publicly available conversation datasets. TensorRT engines optimizing Mistral-7B-Instruct are generated by TensorRT-Cloud. \n\nThese TensorRT engines are ready for commercial use. \n\n### References(s): \nMistral 7B Instruct [Model Card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on Hugging Face
\nMistral 7B [paper](https://arxiv.org/abs/2310.06825)
\nMistral 7B [blogpost](https://mistral.ai/news/announcing-mistral-7b/)\n\n### Model Architecture: \n**Architecture Type:** Transformer
\n**Network Architecture:** Mistral-7B
\n**Model Version:** v0.1 \n\n### Input: \n**Input Format:** Text
\n**Input Parameters:** Max Tokens, Temperature, Top P
\n### Output: \n**Output Format:** Text
\n**Output Parameters:** None \n### Software Integration: \nCheck the version tag for engine support matrix \n### Inference: \n[TRT-LLM Inference Engine](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llama#mistral-v01)\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/).","Mistral 7B Instruct (TensorRT LLM)",["D",1718393026057],"/api/image/nvidia/-/models/mistral-7b-it-v02_trtllm","bias","explainability","privacy","safetyAndSecurity",[],[264,265],[],[],[],{"_91":275,"_267":276,"_269":277,"_271":272},{"_91":266,"_267":268,"_269":270,"_271":272},"TensorRT","value","pltfm_tensorrt","filter",{"_273":274,"_267":268},"category","label","field","platform","AI","soln_ai",{"_273":278,"_267":276},"technology",{"_147":1448,"_149":1449},"ngcApps",[1401],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":290,"_179":314,"_1391":1392,"_1393":51,"_1394":126,"_1395":126,"_1396":1397,"_1398":126,"_1399":1400},"amplitude",{"_1379":1380,"_985":-7,"_1381":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1373":1374,"_1375":1376,"_1377":1378},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1260":205,"_1261":1262,"_1263":1264,"_1265":1266},"announcements",[],"consentState",{"_352":1250,"_1251":26,"_1252":26,"_1253":126,"_1254":1255},"consentConfig",{"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353},"showContactAdmin",1783598796339,[],[],[328,329,330,331,332,333],{"_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":49,"_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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