Meta Llama 3 8B Instruct

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$\"Model$ $\"Model$

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","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",[240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_208":241,"_242":241,"_243":244,"_245":246},"NSPECT-JKGK-TIOP","value","filter",{"_247":246,"_242":241},"category","label","field",{"_119":1421,"_121":1422},"ngcApps",[1374],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1355":1356,"_1357":1358,"_1359":1360,"_1361":1362,"_1363":259,"_150":283,"_1364":1365,"_1366":59,"_1367":98,"_1368":98,"_1369":1370,"_1371":98,"_1372":1373},"amplitude",{"_1352":1353,"_955":-7,"_1354":34},"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",{"_1230":225,"_1231":1232,"_1233":1234,"_1235":1236},"announcements",[],"consentState",{"_321":1220,"_1221":98,"_1222":34,"_1223":34,"_1224":1225},"consentConfig",{"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322},"showContactAdmin",1783641902628,[],[],[297,298,299,300,301],{"_48":63,"_50":73,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":69,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":302,"_58":59},4,"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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