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Redistribution Information

$\"NVIDIA$

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

Meta Terms of Use:\nBy using this model, you are agreeing to the terms and conditions of the license, acceptable use policy and Meta's privacy policy.

Llama 2

Llama 2 70B is one of a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters developed by Meta. Model details can be found here. This model is optimized through NVIDIA NeMo Framework, and is provided through a .nemo checkpoint.

Benefits of using Llama 2 checkpoints in NeMo Framework

The following resources reference different checkpoints of the Llama 2 family of models, but can be easily modified to apply to Llama 2 70B by changing the reference to the model!

P-Tuning and LoRA

NeMo Framework offers support for various parameter-efficient fine-tuning (PEFT) methods for Llama 2 model family.

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 Llama 2 and have been described in detail in NeMo Framework user guide, showing how to tune Llama-2 to answer biomedical questions based on PubMedQA.

Supervised Fine-tuning

NeMo Framework offers Supervised fine-tuning (SFT) support for Llama 2 model family.

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. One specific type of SFT is also referred to as instruction tuning where we use SFT to teach a model to follow instructions better.

NeMo Framework offers out-of-the-box SFT support for Llama 2, which has been described in detail in NeMo Framework user guide, showing how to tune Llama-2 to follow instructions based on databricks-dolly-15k.

SteerLM

NeMo Toolkit supports SteerLM for Llama 2 model family.

SteerLM ia an attribute-conditioned supervised fine-tuning method that is an (user-steerable) alternative to reinforcement learning from human feedback (RLHF) proposed by NVIDIA.\nModels tuned with SteerLM offer flexible alignment at inference time.

An example of SteerLM application to Llama-2 13B model is available on Hugging Face.

Optimized Deployment with TensorRT-LLM

Using TensorRT-LLM, NeMo Framework allows exporting Llama 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 Llama-2. This process has been described in detail in NeMo Framework user guide.

Detailed performance results are available here.

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Model details can be found [here](https://huggingface.co/meta-llama/Llama-2-70b). This model is optimized through NVIDIA NeMo Framework, and is provided through a `.nemo` checkpoint.\n\n## Benefits of using Llama 2 checkpoints in NeMo Framework\n\nThe following resources reference different checkpoints of the Llama 2 family of models, but can be easily modified to apply to Llama 2 70B by changing the reference to the model!\n\n### P-Tuning and LoRA\n\nNeMo Framework offers support for various parameter-efficient fine-tuning (PEFT) methods for Llama 2 model family.\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 Llama 2 and have been described in detail in [NeMo Framework user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2peft.html), showing how to tune Llama-2 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 Llama 2 model family.\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. One specific type of SFT is also referred to as `instruction tuning` where we use SFT to teach a model to follow instructions better.\n\nNeMo Framework offers out-of-the-box SFT support for Llama 2, which has been described in detail in [NeMo Framework user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2sft.html), showing how to tune Llama-2 to follow instructions based on [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k).\n\n### SteerLM\n\nNeMo Toolkit supports SteerLM for Llama 2 model family.\n\n[SteerLM](https://arxiv.org/abs/2310.05344) ia an attribute-conditioned supervised fine-tuning method that is an (user-steerable) alternative to reinforcement learning from human feedback (RLHF) proposed by NVIDIA.\nModels tuned with SteerLM offer flexible alignment at inference time.\n\nAn example of SteerLM application to Llama-2 13B model is available on [Hugging Face](https://huggingface.co/nvidia/SteerLM-llama2-13B).\n\n### Optimized Deployment with TensorRT-LLM\n\nUsing TensorRT-LLM, NeMo Framework allows exporting Llama 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 Llama-2. This process has been described in detail in [NeMo Framework user guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/deployingthenemoframeworkmodel.html).\n\nDetailed performance results are available [here](https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/llama.html).","displayName","Llama-2-70B",["D",1731434101588],"publisher","Meta","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/llama-2-70b","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":239,"_240":239,"_241":242,"_243":244},"NSPECT-AVQ3-KOHC","value","filter",{"_245":244,"_240":239},"category","label","field",{"_107":1421,"_109":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1355":1356,"_1357":1358,"_1359":1360,"_1361":1362,"_1363":257,"_138":281,"_1364":1365,"_1366":55,"_1367":86,"_1368":86,"_1369":1370,"_1371":86,"_1372":1373},"amplitude",{"_1352":1353,"_954":-7,"_1354":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1346":1347,"_1348":1349,"_1350":1351},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1229":223,"_1230":1231,"_1232":1233,"_1234":1235},"announcements",[],"consentState",{"_319":1219,"_1220":86,"_1221":30,"_1222":30,"_1223":1224},"consentConfig",{"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320},"showContactAdmin",1784308303563,[],[],[295,296,297,298,299,300],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":102,"_50":-5,"_52":-5,"_54":55},"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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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