Redistribution Information

Supported Runtime(s): TensorRT-LLM\nSupported Hardware(s): Ampere, Hopper\nSupported OS(s): Linux\nTerms of Use: By using this model, you are agreeing to the terms and conditions of the license.

Mistral 7B Instruct v0.3

Mistral-7B-v0.3 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 v0.3 checkpoints in NeMo Framework

While the examples below refer to Mistral-7B v0.1, they are directly compatible with Mistral-7B Instruct v0.3.

P-Tuning and LoRA

NeMo Framework offers support for various parameter-efficient fine-tuning (PEFT) methods for Mistral-7B Instruct v0.3.

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 v0.3 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 v0.3.

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 v0.3, which has been described in detail for Mistral-7B v0.1 in NeMo Framework user guide, but is directly compatible with Mistral-7B Instruct v0.3.

Optimized Deployment with TensorRT-LLM

Using TensorRT-LLM, NeMo Framework allows exporting Mistral-7B Instruct v0.3 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 v0.3. 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. This model is optimized through NVIDIA NeMo Framework, and is provided through a .nemo checkpoint.\n\n##### Benefits of using Mistral-7B Instruct v0.3 checkpoints in NeMo Framework\n\nWhile the examples below refer to Mistral-7B v0.1, they are directly compatible with Mistral-7B Instruct v0.3.\n\n#### P-Tuning and LoRA\n\nNeMo Framework offers support for various parameter-efficient fine-tuning (PEFT) methods for Mistral-7B Instruct v0.3.\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 v0.3 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 v0.3.\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 v0.3, 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 v0.3.\n\n#### Optimized Deployment with TensorRT-LLM\n\nUsing TensorRT-LLM, NeMo Framework allows exporting Mistral-7B Instruct v0.3 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 v0.3. This process has been described in detail in NeMo Framework user guide.\n\nDetailed information, including performance results are available [here](https://arxiv.org/pdf/2310.06825).","displayName","Mistral-7B-Instruct-v0.3",["D",1731538686851],"publisher","Mistral AI","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/mistral-7b-v03-instruct","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":240,"_241":240,"_242":243,"_244":245},"NSPECT-KZDZ-QLVI","value","filter",{"_246":245,"_241":240},"category","label","field",{"_107":1225,"_109":1226},"ngcApps",[1178],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1159":1160,"_1161":1162,"_1163":1164,"_1165":1166,"_1167":258,"_138":284,"_1168":1169,"_1170":56,"_1171":86,"_1172":86,"_1173":1174,"_1175":86,"_1176":1177},"amplitude",{"_1156":1157,"_796":-7,"_1158":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1150":1151,"_1152":1153,"_1154":1155},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787621026334,"1.12.0","defaultSearchSuggestions",{"_1036":224,"_1037":1038,"_1039":1040,"_1041":1042},"announcements",[],"consentState",{"_321":1026,"_1027":86,"_1028":31,"_1029":31,"_1030":1031},"consentConfig",{"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322},"showContactAdmin",1787621026335,[],[],[298,299,300,301,302],{"_45":60,"_47":71,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":102,"_51":-5,"_53":-5,"_55":56},"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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