Llama-3.1-8B-Instruct

Description:

This container houses the Llama-3.1-8B-Instruct, which is an 8 billion parameter, instruction-tuned large language model created by Meta. This model is part of the Llama 3.1 family of open-access models and is specifically optimized for dialogue and conversational use cases, making it highly capable of following user instructions to perform a wide variety of natural language processing tasks.

The container components are ready for commercial/non-commercial use.

Documentation

Visit the NIM Container LLM page for release documentation, deployment guides, and more.

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; see link to Non-NVIDIA [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).

License/Terms of Use:

GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for AI Products.

The use of any models in this container is governed by the NVIDIA Open Model Agreement in addition to any terms which govern the specific models used.

You are responsible for ensuring that your use of any provided models complies with all applicable laws.

Use of this model is governed by the NVIDIA Open Model Agreement. The underlying model is also licensed under the Llama 3.1 Community License Agreement. Built with Llama.

Deployment Geography:

Global

Release Date:

Build.NVIDIA.com 07/23/2024 via
\nhttps://build.nvidia.com/meta/llama-3_1-8b-instruct

Huggingface 07/23/2024 via
\nhttps://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

Program Classes

Llama-3.1-8B-Instruct Container includes the following model:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name & Link	Use Case	How to Pull the Model
Llama-3.1-8B-Instruct	A conversational AI model for instruction-following, content generation, summarization, and coding assistance.	Automatic

Deployment Details:

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

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 developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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(latest)","nim/meta/llama-3.1-8b-instruct","nim/meta/containers/llama-3.1-8b-instruct","llama-3.1-8b-instruct","NVIDIA NIM for GPU accelerated Llama-3.1-8B-Instruct inference through OpenAI compatible APIs","# **Llama-3.1-8B-Instruct**\n\n## **Description:**\n\nThis container houses the **Llama-3.1-8B-Instruct**, which is an 8 billion parameter, instruction-tuned large language model created by Meta. This model is part of the Llama 3.1 family of open-access models and is specifically optimized for dialogue and conversational use cases, making it highly capable of following user instructions to perform a wide variety of natural language processing tasks.\n\nThe container components are ready for commercial/non-commercial use.\n\n**Documentation**\n\nVisit the [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) page for release documentation, deployment guides, and more.\n\n\n## **Third-Party Community Consideration**\n\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; see link to Non-NVIDIA \\[meta-llama/Llama-3.1-8B-Instruct\\](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).\n\nThe use of any models in this container is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/) in addition to any terms which govern the specific models used.\n\nYou are responsible for ensuring that your use of any provided models complies with all applicable laws.\n\nUse of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/). The underlying model is also licensed under the [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.\n\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Release Date:**\n\nBuild.NVIDIA.com 07/23/2024 via \n[https://build.nvidia.com/meta/llama-3_1-8b-instruct](https://build.nvidia.com/meta/llama-3_1-8b-instruct)\n\nHuggingface 07/23/2024 via \n[https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)\n\n## Program Classes\n\nLlama-3.1-8B-Instruct Container includes the following model:\n\n| Model Name & Link | Use Case | How to Pull the Model |\n| :---- | :---- | :---- |\n| [Llama-3.1-8B-Instruct](https://catalog.ngc.nvidia.com/orgs/nim/teams/meta/models/llama-3.1-8b-instruct) | A conversational AI model for instruction-following, content generation, summarization, and coding assistance. | Automatic |\n\n## **Deployment Details:**\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.\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 developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## **Get Help**\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","Llama-3.1-8B-Instruct",["D",1784768126426],[364,362,360,358,356,354,352,350,348,344,340,346,336,334,328,209,332,330,342,366,325,321,319,317,323,315,313,311,309,307,305,303,299,301,297],"/api/image/nim/meta/containers/llama-3.1-8b-instruct",11481488524,{"_419":420,"_421":422,"_423":424,"_425":26,"_426":427},"hasSignedTag",[410,411],[394,395,396,397,398],[],[387,388],[],{"_92":392,"_142":392,"_143":393,"_145":391},{"_92":389,"_142":389,"_143":390,"_145":391},"signed images",{"_147":391,"_142":389},"system","Elastic NIM",{"_147":391,"_142":392},{"_92":93,"_142":407,"_143":408,"_145":146},{"_92":405,"_142":405,"_143":406,"_145":146},{"_92":403,"_142":403,"_143":404,"_145":146},{"_92":401,"_142":401,"_143":402,"_145":146},{"_92":399,"_142":399,"_143":400,"_145":146},"NSPECT-ZP6P-5LPY",{"_147":146,"_142":399},"NIM",{"_147":146,"_142":401},"Llama3.1-8b-instruct",{"_147":146,"_142":403},"Llama3.1",{"_147":146,"_142":405},"gpu_type_dgx_spark",{"_147":409,"_142":407},"gpuType",{"_92":417,"_142":417,"_143":418,"_145":415},{"_92":412,"_142":413,"_143":414,"_145":415},"NVIDIA NIM","nimmcro_nvidia_nim",{"_147":416,"_142":413},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_147":146,"_142":417},"governingTerms","

GOVERNING DOWNLOAD TERMS:

The NIM container is governed by the NVIDIA Software License Agreement (https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the Product-Specific Terms for AI Products (https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).

The use of any models in this container is governed by the NVIDIA Open Model License Agreement (found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) in addition to any terms which govern the specific models used.

You are responsible for ensuring that your use of any provided models complies with all applicable laws.

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If you do not allow these cookies, then some or all of these services may not function properly.",[748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772],[719,720,721,722,723],{"_558":633,"_92":633,"_91":105,"_531":743},{"_558":607,"_92":607,"_91":105,"_531":738},{"_558":733,"_92":733,"_91":105,"_531":734},{"_558":728,"_92":728,"_91":105,"_531":729},{"_558":724,"_92":724,"_91":105,"_531":725},"nvidia.partners",[726],{"_124":727,"_564":724,"_565":26,"_91":601},"edge","app.hushly.com",[730],{"_124":731,"_564":728,"_565":26,"_91":732},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[735],{"_124":736,"_564":733,"_565":26,"_91":737},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859],[811,812,813,814],{"_558":826,"_92":826,"_91":105,"_531":827},{"_558":821,"_92":821,"_91":105,"_531":822},{"_558":620,"_92":620,"_91":105,"_531":818},{"_558":733,"_92":733,"_91":105,"_531":815},[816],{"_124":817,"_564":733,"_565":155,"_91":737},"_gh_sess",[819],{"_124":820,"_564":620,"_565":155,"_91":627},"YSC","bizible.com",[823],{"_124":824,"_564":821,"_565":26,"_91":825},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[828],{"_124":829,"_564":826,"_565":26,"_91":830},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_124":901,"_564":679,"_565":26,"_91":902},{"_124":886,"_564":900,"_565":155,"_91":887},{"_124":869,"_564":682,"_565":26,"_91":794},{"_124":869,"_564":782,"_565":26,"_91":794},{"_124":898,"_564":679,"_565":26,"_91":899},{"_124":886,"_564":685,"_565":155,"_91":887},{"_124":896,"_564":679,"_565":26,"_91":897},{"_124":886,"_564":682,"_565":155,"_91":887},{"_124":894,"_564":781,"_565":26,"_91":895},{"_124":886,"_564":803,"_565":155,"_91":887},{"_124":866,"_564":682,"_565":26,"_91":868},{"_124":892,"_564":679,"_565":155,"_91":893},{"_124":890,"_564":679,"_565":26,"_91":891},{"_124":888,"_564":679,"_565":155,"_91":889},{"_124":886,"_564":777,"_565":155,"_91":887},{"_124":884,"_564":679,"_565":26,"_91":885},{"_124":882,"_564":679,"_565":26,"_91":883},{"_124":880,"_564":679,"_565":155,"_91":881},{"_124":878,"_564":786,"_565":26,"_91":879},{"_124":866,"_564":777,"_565":26,"_91":868},{"_124":876,"_564":679,"_565":26,"_91":877},{"_124":874,"_564":679,"_565":155,"_91":875},{"_124":872,"_564":679,"_565":26,"_91":873},{"_124":870,"_564":679,"_565":26,"_91":871},{"_124":869,"_564":679,"_565":26,"_91":794},{"_124":866,"_564":867,"_565":26,"_91":868},{"_124":864,"_564":679,"_565":26,"_91":865},{"_124":862,"_564":679,"_565":155,"_91":863},{"_124":860,"_564":679,"_565":26,"_91":861},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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