Redistribution Information

$\"NVIDIA$

Supported Runtime(s): TensorRT-LLM
Supported Hardware(s): Ampere, Hopper
Supported OS(s): Linux

Terms of Use:\nBy using this model, you are agreeing to the terms and conditions of the license.

Mistral 7B Instruct v0.2

Mistral-7B-v0.2 is an instruction-tuned generative text model developed by Mistral AI. Model details can be found here. This model is optimized through NVIDIA NeMo Framework, and is provided through a .nemo checkpoint.

Benefits of using Mistral-7B Instruct v0.2 checkpoints in NeMo Framework

While the examples below refer to Mistral-7B v0.1, they are directly compatible with Mistral-7B Instruct v0.2.

P-Tuning and LoRA

NeMo Framework offers support for various parameter-efficient fine-tuning (PEFT) methods for Mistral-7B Instruct v0.2.

PEFT techniques allow customizing foundation models to improve performance on specific tasks.

Two of them, P-Tuning and Low-Rank Adaptation (LoRA), are supported out of the box for Mistral-7B Instruct v0.2 and have been described in detail in NeMo Framework user guide, showing how to tune Mistral-7B-v0.1 to answer biomedical questions based on PubMedQA.

Supervised Fine-tuning

NeMo Framework offers Supervised fine-tuning (SFT) support for Mistral-7B Instruct v0.2.

Fine-tuning refers to how one can modify the weights of a pre-trained foundation model with additional custom data. Supervised fine-tuning (SFT) refers to unfreezing all the weights and layers in tuned model and training on a newly labeled set of examples. One can fine-tune to incorporate new, domain-specific knowledge or teach the foundation model what type of response to provide.

NeMo Framework offers out-of-the-box SFT support for Mistral-7B Instruct v0.2, which has been described in detail for Mistral-7B v0.1 in NeMo Framework user guide, but is directly compatible with Mistral-7B Instruct v0.2.

Optimized Deployment with TensorRT-LLM

Using TensorRT-LLM, NeMo Framework allows exporting Mistral-7B Instruct v0.2 checkpoints to formats that are optimized for deployment on NVIDIA GPUs.

TensorRT-LLM is a library that allows building TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.

Thanks to that, it is possible to reach state-of-the-art performance using export and deployment methods that NVIDIA built for Mistral-7B Instruct v0.2. This process has been described in detail in NeMo Framework user guide.

Detailed information, including performance results are available here.

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Model details can be found [here](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). This model is optimized through NVIDIA NeMo Framework, and is provided through a `.nemo` checkpoint.\n\n## Benefits of using Mistral-7B Instruct v0.2 checkpoints in NeMo Framework\n\nWhile the examples below refer to Mistral-7B v0.1, they are directly compatible with Mistral-7B Instruct v0.2.\n\n### P-Tuning and LoRA\n\nNeMo Framework offers support for various parameter-efficient fine-tuning (PEFT) methods for Mistral-7B Instruct v0.2.\n\nPEFT techniques allow customizing foundation models to improve performance on specific tasks.\n\nTwo of them, P-Tuning and Low-Rank Adaptation (LoRA), are supported out of the box for Mistral-7B Instruct v0.2 and have been described in detail in [NeMo Framework user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/mistralpeft.html), showing how to tune Mistral-7B-v0.1 to answer biomedical questions based on [PubMedQA](https://pubmedqa.github.io/).\n\n### Supervised Fine-tuning\n\nNeMo Framework offers Supervised fine-tuning (SFT) support for Mistral-7B Instruct v0.2.\n\nFine-tuning refers to how one can modify the weights of a pre-trained foundation model with additional custom data. Supervised fine-tuning (SFT) refers to unfreezing all the weights and layers in tuned model and training on a newly labeled set of examples. One can fine-tune to incorporate new, domain-specific knowledge or teach the foundation model what type of response to provide.\n\nNeMo Framework offers out-of-the-box SFT support for Mistral-7B Instruct v0.2, which has been described in detail for Mistral-7B v0.1 in [NeMo Framework user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/mistralsft.html), but is directly compatible with Mistral-7B Instruct v0.2.\n\n### Optimized Deployment with TensorRT-LLM\n\nUsing TensorRT-LLM, NeMo Framework allows exporting Mistral-7B Instruct v0.2 checkpoints to formats that are optimized for deployment on NVIDIA GPUs.\n\n[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) is a library that allows building TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.\n\nThanks to that, it is possible to reach state-of-the-art performance using export and deployment methods that NVIDIA built for Mistral-7B Instruct v0.2. This process has been described in detail in [NeMo Framework user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/mistral/deploy.html).\n\nDetailed information, including performance results are available [here](https://arxiv.org/pdf/2310.06825.pdf).","displayName","Mistral-7B-Instruct-v0.2",["D",1732745389851],"publisher","Mistral AI","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/mistral-7b-instruct","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":251,"_245":251,"_246":252,"_248":249},{"_210":244,"_245":244,"_246":247,"_248":249},"NSPECT-YDAW-FMDD","value","filter",{"_250":249,"_245":244},"category","label","field","NSPECT-97U0-V28M",{"_250":249,"_245":251},{"_120":1426,"_122":1427},"ngcApps",[1379],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1360":1361,"_1362":1363,"_1364":1365,"_1366":1367,"_1368":264,"_151":288,"_1369":1370,"_1371":59,"_1372":99,"_1373":99,"_1374":1375,"_1376":99,"_1377":1378},"amplitude",{"_1357":1358,"_959":-7,"_1359":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1351":1352,"_1353":1354,"_1355":1356},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1234":227,"_1235":1236,"_1237":1238,"_1239":1240},"announcements",[],"consentState",{"_325":1224,"_1225":99,"_1226":34,"_1227":34,"_1228":1229},"consentConfig",{"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326},"showContactAdmin",1784063563631,[],[],[302,303,304,305,306],{"_48":63,"_50":74,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":115,"_54":-5,"_56":-5,"_58":59},"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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