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Meta Llama 3 8B Instruct

\n\nimg {\n display: inline;\n}\n\n

$\"Model$ $\"Model$

Meta Llama 3 8B Instruct Terms of Use

This model is licensed under the Meta Llama 3 Community License Agreement.

Model Description

Meta-Llama-3-8B-Instruct is an instruct-tuned decoder-only, text-to-text model. It was trained on 15 trillion tokens of data from publicly available sources. The instruction-tuning uses supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Meta-Llama-3-8B-Instruct builds on the existing work done for the Llama, and Llama 2, family of models. It makes a few key improvements:

128K token vocabulary, allowing for language to be encoded more efficiently
Grouped-Query Attention (GQA) for all sizes of the model
8,192 context window for training with cross-document masking

In their development of these models, Meta took great care to optimize for helpfulness and safety.

More details on the model can be found here.

This model is optimized through NVIDIA NeMo Framework, and is provided through a .nemo checkpoint.

Getting Started

Step 1: Fetching the model file

You can download the model from NGC using the CLI tool.

ngc registry model download-version nvidia/nemo/llama-3-8b-instruct-nemo:1.0\n

(Optional, Recommended) Pre-extract the model from the .nemo file. This will reduce the model loading time overhead because the NeMo scripts will not have to extract the model from the .nemo everytime they are run. This step is not required though, and NeMo scripts can still ingest the .nemo file directly as well.

mkdir llama_3_8B_instruct\ntar -xvf 8b_instruct_nemo_bf16.nemo -C llama_3_8B_instruct\n

\n
You can find the instructions to install and configure the NGC CLI tool here
\n

Step 2: Fetch the NeMo Framework container

You can use the NeMo Framework container available on NGC which comes preloaded with all the required dependencies.

export NEMO_IMAGE=\"nvcr.io/nvidia/nemo:24.05\"\ndocker pull $NEMO_IMAGE\n

Step 3: Run the NeMo container

Run the NeMo framework container, mounting the model.

# Full path to the extracted model directory\nexport MODEL_DIR=$(pwd)/llama_3_8B_instruct\ndocker run --gpus all -it --rm  -v ${MODEL_DIR}:/model $NEMO_IMAGE\n

Step 4: Follow The Desired Playbooks (Coming Soon!)

We're hard at work preparing resources to enable you to most easily use the Meta-Llama-3-8B-Instruct .nemo checkpoint!

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The instruction-tuning uses supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.","## Meta Llama 3 8B Instruct\n\n\n\n[![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)[![Model size](https://img.shields.io/badge/Params-8B-green)](#model-architecture)\n\n### Meta Llama 3 8B Instruct Terms of Use\n\nThis model is licensed under the [Meta Llama 3 Community License Agreement](https://llama.meta.com/llama3/license/).\n\n### Model Description\n\nMeta-Llama-3-8B-Instruct is an instruct-tuned decoder-only, text-to-text model. It was trained on 15 trillion tokens of data from publicly available sources. The instruction-tuning uses supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.\n\nMeta-Llama-3-8B-Instruct builds on the existing work done for the Llama, and Llama 2, family of models. It makes a few key improvements:\n\n- 128K token vocabulary, allowing for language to be encoded more efficiently\n- Grouped-Query Attention (GQA) for all sizes of the model\n- 8,192 context window for training with cross-document masking\n\nIn their development of these models, Meta took great care to optimize for helpfulness and safety.\n\nMore details on the model can be found [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).\n\nThis model is optimized through NVIDIA NeMo Framework, and is provided through a `.nemo` checkpoint.\n\n### Getting Started\n\n#### Step 1: Fetching the model file\n\n1. You can download the model from NGC using the CLI tool.\n\n```bash\nngc registry model download-version nvidia/nemo/llama-3-8b-instruct-nemo:1.0\n```\n\n2. (Optional, Recommended) Pre-extract the model from the ``.nemo`` file. This will reduce the model loading time overhead because the NeMo scripts will not have to extract the model from the .nemo everytime they are run. This step is not required though, and NeMo scripts can still ingest the `.nemo` file directly as well.\n\n```bash\nmkdir llama_3_8B_instruct\ntar -xvf 8b_instruct_nemo_bf16.nemo -C llama_3_8B_instruct\n```\n\n> You can find the instructions to install and configure the NGC CLI tool [here](https://org.ngc.nvidia.com/setup/installers/cli)\n\n#### Step 2: Fetch the NeMo Framework container\n\nYou can use the NeMo Framework [container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo) available on NGC which comes preloaded with all the required dependencies.\n\n```bash\nexport NEMO_IMAGE=\"nvcr.io/nvidia/nemo:24.05\"\ndocker pull $NEMO_IMAGE\n```\n\n#### Step 3: Run the NeMo container\n\nRun the NeMo framework container, mounting the model.\n\n```bash\n# Full path to the extracted model directory\nexport MODEL_DIR=$(pwd)/llama_3_8B_instruct\ndocker run --gpus all -it --rm -v ${MODEL_DIR}:/model $NEMO_IMAGE\n```\n\n#### Step 4: Follow The Desired Playbooks (Coming Soon!)\n\nWe're hard at work preparing resources to enable you to most easily use the Meta-Llama-3-8B-Instruct `.nemo` checkpoint!","displayName","Meta Llama 3 8B Instruct",["D",1731434129705],"publisher","Meta","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/llama-3-8b-instruct-nemo","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-JKGK-TIOP","value","filter",{"_245":244,"_240":239},"category","label","field",{"_106":1421,"_108":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1355":1356,"_1357":1358,"_1359":1360,"_1361":1362,"_1363":257,"_137":281,"_1364":1365,"_1366":56,"_1367":85,"_1368":85,"_1369":1370,"_1371":85,"_1372":1373},"amplitude",{"_1352":1353,"_953":-7,"_1354":31},"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",{"_1228":223,"_1229":1230,"_1231":1232,"_1233":1234},"announcements",[],"consentState",{"_318":1218,"_1219":31,"_1220":31,"_1221":85,"_1222":1223},"consentConfig",{"_300":301,"_302":303,"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319},"showContactAdmin",1783644137137,[],[],[295,296,297,298,299],{"_45":59,"_47":70,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":101,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":101,"_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. 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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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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