Model Overview

Description:

Built with Meta Llama 3.1 - The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained, and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases.

We downloaded Meta Llama 3.1 8B instruct model in Pytorch bfloat16 format from HuggingFace. We used AutoAWQ to convert it to Meta Llama 3.1 8B Pytorch INT4 model. We used Onnxruntime-GenAI SDK to convert Meta Llama 3.1 8B Pytorch INT4 model to Meta Llama 3.1 8B ONNX INT4 model. We have posted this Meta Llama 3.1 8B ONNX INT4 model files here.

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-3.1-8B-Instruct Model Card.

Terms of use

GOVERNING TERMS: This model is governed by the NVIDIA Agreements | Enterprise Software | NVIDIA Community Model License.

ADDITIONAL INFORMATION: Meta Llama 3.1 Community License Agreement, acceptable use policy, and Meta’s privacy policy Built with Meta Llama 3.1.

References(s):

Meta Llama 3 Model Card on Hugging Face
Meta Llama 3 blogpost

Model Architecture:

Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback(RLHF) to align with human preferences for helpfulness and safety.

Architecture Type: Transformer
\nNetwork Architecture: Llama 3.1

Input:

Input Type: Text and Code
\nInput Format: Text
\nInput Parameters: Temperature, TopP
\nOther Properties Related to Input: Supports English, German, Italian, Portuguese, Hindi, Spanish, and Thai

Output:

Output Type(s): Text and Code
\nOutput Format: Text and code
\nOutput Parameters: Max output tokens

Software Integration:

Runtime(s): N/A
\nSupported Hardware Platform(s): RTX 4090. 6GB or higher VRAM gpus are recommended. Higher VRAM may be required for larger context length use cases.
\nSupported Operating System(s): Windows
\nSupported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Model Version:1.0

Training, Testing, and Evaluation Datasets:

Refer to [Llama 3.1 Model Card](llama-models/models/llama3_1/MODEL_CARD.md at main · meta-llama/llama-models · GitHub) for the details.

Inference:

We used GenAI ORT->DML backend for inference. The instructions to use this backend are given in readme.txt file available under “Files” tab.

MMLU Accuracy ( 5 shot ) :

With GenAI ORT->DML backend, we got below accuracy numbers on a desktop RTX 4090 GPU system.

\"overall_accuracy\": 66.61

Evaluation Dataset:

Link: https://people.eecs.berkeley.edu/~hendrycks/data.tar .
\nData Collection Method by dataset - Unknown
\nLabeling Method by dataset - Not Applicable

Test Hardware:

GPU - RTX 4090 Desktop system

OS – Windows 11 23H2

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The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases. \n \nWe downloaded Meta Llama 3.1 8B instruct model in Pytorch bfloat16 format from HuggingFace. We used AutoAWQ to convert it to Meta Llama 3.1 8B Pytorch INT4 model. We used Onnxruntime-GenAI SDK to convert Meta Llama 3.1 8B Pytorch INT4 model to Meta Llama 3.1 8B ONNX INT4 model. We have posted this Meta Llama 3.1 8B ONNX INT4 model files here.\n\n### Third-Party Community Consideration \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-3.1-8B-Instruct Model Card](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). \n\n### Terms of use\n\nGOVERNING TERMS: This model is governed by the [NVIDIA Agreements | Enterprise Software | NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). \n\nADDITIONAL INFORMATION: [Meta Llama 3.1 Community License Agreement](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE), [acceptable use policy](https://www.llama.com/llama3_1/use-policy/), and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/) Built with Meta Llama 3.1. \n\n\n### References(s):\n\n* Meta Llama 3 [Model Card](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on Hugging Face \n* Meta Llama 3 [blogpost](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1) \n \n\n### Model Architecture:\nLlama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback(RLHF) to align with human preferences for helpfulness and safety.\n\n**Architecture Type:** Transformer \n**Network Architecture:** Llama 3.1\n\n### Input:\n**Input Type:** Text and Code \n**Input Format:** Text \n**Input Parameters:** Temperature, TopP \n**Other Properties Related to Input:** Supports English, German, Italian, Portuguese, Hindi, Spanish, and Thai \n\n### Output:\n**Output Type(s):** Text and Code \n**Output Format:** Text and code \n**Output Parameters:** Max output tokens \n\n\n### Software Integration:\n\n**Runtime(s):** N/A \n**Supported Hardware Platform(s):** RTX 4090. 6GB or higher VRAM gpus are recommended. Higher VRAM may be required for larger context length use cases. \n**Supported Operating System(s):** Windows \n**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.\n\n### Model Version:1.0\n\n### Training, Testing, and Evaluation Datasets: \nRefer to [Llama 3.1 Model Card](llama-models/models/llama3_1/MODEL_CARD.md at main · meta-llama/llama-models · GitHub) for the details. \n\n### Inference:\n\nWe used GenAI ORT->DML backend for inference. The instructions to use this backend are given in readme.txt file available under “Files” tab. \n\n[MMLU Accuracy](https://paperswithcode.com/dataset/mmlu) ( 5 shot ) : \n\nWith GenAI ORT->DML backend, we got below accuracy numbers on a desktop RTX 4090 GPU system. \n\n\"overall_accuracy\": 66.61 \n\n### Evaluation Dataset: \n**Link:** https://people.eecs.berkeley.edu/~hendrycks/data.tar . \nData Collection Method by dataset - Unknown \nLabeling Method by dataset - Not Applicable\n\n### Test Hardware:\nGPU - RTX 4090 Desktop system \n\nOS – Windows 11 23H2","displayName","Meta Llama 3.1 8b Instruct ONNX INT4 RTX",["D",1729271771185],"publisher","Meta","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/meta-llama-3_1-8b-instruct","rawImageUrl","https://catalog.ngc.nvidia.com/demos/llama2.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[252],"displayLabels",[239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":248,"_242":249,"_243":250,"_245":246},{"_207":241,"_242":241,"_243":244,"_245":246},"NSPECT-M8UV-KC1M","value","filter",{"_247":246,"_242":241},"category","label","field","AI","soln_ai",{"_247":251,"_242":249},"technology",{"_207":253,"_242":254,"_243":255,"_245":256},"Runs on RTX","pltfm_runs_on_rtx",{"_247":257,"_242":254},"feature","platform",{"_121":1431,"_123":1432},"ngcApps",[1384],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1365":1366,"_1367":1368,"_1369":1370,"_1371":1372,"_1373":269,"_152":293,"_1374":1375,"_1376":59,"_1377":100,"_1378":100,"_1379":1380,"_1381":100,"_1382":1383},"amplitude",{"_1362":1363,"_964":-7,"_1364":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1356":1357,"_1358":1359,"_1360":1361},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1239":224,"_1240":1241,"_1242":1243,"_1244":1245},"announcements",[],"consentState",{"_331":1229,"_1230":100,"_1231":34,"_1232":34,"_1233":1234},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1784312082063,[],[],[307,308,309,310,311,312],{"_48":63,"_50":74,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":116,"_54":-5,"_56":-5,"_58":59},"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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If you do not allow these cookies, then some or all of these services may not function properly.",[603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623],[568,569,570,571,572],{"_371":463,"_207":463,"_178":224,"_344":594},{"_371":429,"_207":429,"_178":224,"_344":589},{"_371":582,"_207":582,"_178":224,"_344":583},{"_371":578,"_207":578,"_178":224,"_344":579},{"_371":573,"_207":573,"_178":224,"_344":574},"nvcrm.my.site.com",[575],{"_78":576,"_377":573,"_378":34,"_178":577},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[580],{"_78":581,"_377":578,"_378":34,"_178":412},"edge","github.com",[584,585],{"_78":588,"_377":582,"_378":34,"_178":587},{"_78":586,"_377":582,"_378":34,"_178":587},"logged_in","This domain is owned by Github. 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