Nemotron-3-8B-QA-4k

Model Overview

License

The use of this model is governed by the NVIDIA AI Foundation Models Community License Agreement.

Description

Nemotron-3-8B-QA-4k is a 8 billion parameter generative language model customized on an 8B base model. It takes input with context length up to 4,096 tokens. The model has been further fine-tuned for instruction following Supervised Fine-tuning (SFT) using a method by NVIDIA specifically for Question and Answer method, to give concise and reliable answers.

Nemotron-3-8B-QA is part of Nemotron-3, which is a family of enterprise ready GPT-based decoder-only generative text models compatible with NVIDIA NeMo Framework. For other models in this collection, see the models page.

NVIDIA NeMo is an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.

References

Announcement Blog

Model Architecture

Architecture Type: Transformer

Network Architecture: Generative Pre-Trained Transformer (GPT-3)

Prompt Format

Single Turn

System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-1}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant:\n

Multi-Turn or Few-shot

System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-2}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant: {Answer-1}\n\nUser: {Question-2}\n\nAssistant:\n

Example prompt formation code

PROMPT_TEMPLATE = \"\"\"System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{context_1}\n\nUser: Please give a full and complete answer for the question. {question_1}\n\nAssistant:\"\"\"\n\ncontext_1 = \"Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil and gas.\"\nquestion_1 = \"What happened in the 1800s?\"\n\nprompt = PROMPT_TEMPLATE.format(context_1=context_1, question_1=question_1)\nprint(prompt)\n

Software Integration

Runtime Engine(s):\nNVIDIA AI Enterprise

Toolkit:\nNeMo Framework

See NeMo inference container documentation for details on how to setup an inference server with the pyTriton and TensorRT-LLM backend.

Sample Inference Code:

from nemo.deploy import NemoQuery\n\n# In this case, we run inference on the same machine\nnq = NemoQuery(url=\"localhost:8000\", model_name=\"Nemotron-3-8B-QA-4K\")\n\n# See above for prompt format\noutput = nq.query_llm(prompts=[prompt], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1)\nprint(output)\n

Supported Hardware:

H100
A100 80GB, A100 40GB

Model Version(s)

Nemotron-3-8B-QA-4k-SFT-BF16-1

Dataset

NVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.5 Trillion tokens of text. The dataset contains 53 different human languages and 37 programming languages. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.

Evaluation

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

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

Dataset and Metric	Nemotron-3-QA-8B, Zero-shot
Natural Questions, F1-Score	41.99%
Doc2Dial, BLEU-4	30.20

Intended use

The Nemotron-3-8B-QA model is best suited for Questions and Answering use cases and can be further customized on knowledge base data to achieve even better performance.

Ethical use

Technology can have a profound impact on people and the world, and NVIDIA is committed to enabling trust and transparency in AI development. NVIDIA encourages users to adopt principles of AI ethics and trustworthiness to guide your business decisions by following the guidelines in the NVIDIA AI Foundation Models Community License Agreement.

Limitations

The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

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It takes input with context length up to 4,096 tokens. The model has been further fine-tuned for instruction following Supervised Fine-tuning (SFT) using a method by NVIDIA specifically for Question and Answer method, to give concise and reliable answers.\n\nNemotron-3-8B-QA is part of Nemotron-3, which is a family of enterprise ready GPT-based decoder-only generative text models compatible with NVIDIA [NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/). For other models in this collection, see the [models page](registry.ngc.nvidia.com/models).\n\nNVIDIA NeMo is an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n\n#### References\n\n[Announcement Blog](https://developer.nvidia.com/blog/nvidia-ai-foundation-models-build-custom-enterprise-chatbots-and-co-pilots-with-production-ready-llms/)\n\n#### Model Architecture\n\n**Architecture Type:** Transformer\n\n**Network Architecture:** Generative Pre-Trained Transformer (GPT-3)\n\n#### Prompt Format\n\n##### Single Turn\n\n```text\nSystem: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-1}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant:\n```\n\n##### Multi-Turn or Few-shot\n\n```text\nSystem: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-2}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant: {Answer-1}\n\nUser: {Question-2}\n\nAssistant:\n```\n\n##### Example prompt formation code\n\n```python\nPROMPT_TEMPLATE = \"\"\"System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{context_1}\n\nUser: Please give a full and complete answer for the question. {question_1}\n\nAssistant:\"\"\"\n\ncontext_1 = \"Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil and gas.\"\nquestion_1 = \"What happened in the 1800s?\"\n\nprompt = PROMPT_TEMPLATE.format(context_1=context_1, question_1=question_1)\nprint(prompt)\n```\n\n#### Software Integration\n\n**Runtime Engine(s):**\nNVIDIA AI Enterprise\n\n**Toolkit:**\nNeMo Framework\n\nSee [NeMo inference container documentation](https://registry.ngc.nvidia.com/orgs/ea-bignlp/teams/ga-participants/containers/nemofw-inference) for details on how to setup an inference server with the pyTriton and TensorRT-LLM backend.\n\n**Sample Inference Code:**\n```python\nfrom nemo.deploy import NemoQuery\n\n# In this case, we run inference on the same machine\nnq = NemoQuery(url=\"localhost:8000\", model_name=\"Nemotron-3-8B-QA-4K\")\n\n# See above for prompt format\noutput = nq.query_llm(prompts=[prompt], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1)\nprint(output)\n```\n**Supported Hardware:**\n\n- H100\n- A100 80GB, A100 40GB\n\n#### Model Version(s)\n\n`Nemotron-3-8B-QA-4k-SFT-BF16-1`\n\n### Dataset\n\nNVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.5 Trillion tokens of text. The dataset contains 53 different human languages and 37 programming languages. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.\n\n### Evaluation\n\n| **Dataset and Metric** | **Nemotron-3-QA-8B, Zero-shot** |\n|:------------------------------------|:------------------------------------|\n| Natural Questions, F1-Score | 41.99% |\n| Doc2Dial, BLEU-4 | 30.20 |\n\n### Intended use\n\nThe Nemotron-3-8B-QA model is best suited for Questions and Answering use cases and can be further customized on knowledge base data to achieve even better performance.\n\n#### Ethical use\n\nTechnology can have a profound impact on people and the world, and NVIDIA is committed to enabling trust and transparency in AI development. NVIDIA encourages users to adopt principles of AI ethics and trustworthiness to guide your business decisions by following the guidelines in the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license).\n\n### Limitations\n\n- The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.\n- The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.","displayName","Nemotron-3-8B-QA-4k",["D",1731434388660],"publisher","Krzysztof_Pawelec_PL","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nemotron-3-8b-qa-4k","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",12634523105,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | Large Language Model Development\nModel Type: | Generative Pre-Trained Transformer (GPT)\nIntended User: | enterprise developers building large language models.\nOutput: | Text String(s)\nDescribe how the model works: | The network architecture of this model is Generative Pre-Trained Transformer-3 (GPT3). The decoder in GPT-3 generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. \nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nPerformance Metrics: | Throughput and Latency\nPotential Known Risks: | The Model may produce output that is biased and toxic based on how it is prompted, producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. The model may also amplify biases and return toxic responses especially when prompted with toxic prompts. \nLicensing: | [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | Neither\nWas consent obtained for any PII used? | Not Applicable\nPII used to create this model? | None Known\nHow often is dataset reviewed? | Before Every Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nAre we able to identify and trace source of dataset? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes, for NVIDIA collected data. \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes, for NVIDIA collected data.
No, for third-party vendor data.","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nVerified to have met prescribed quality standards: | Yes\nTarget Key Performance Indicator(s) (KPI(s)): | Accuracy, Latency, Throughput\nModel Application(s): | Conversation, Question Answering\nDescribe the physical safety impact (if present). | None Known\nUse Case Restrictions: | Model for Non-Commerical Use Only\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[],"displayLabels",[245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":254,"_248":254,"_249":255,"_251":252},{"_209":247,"_248":247,"_249":250,"_251":252},"NSPECT-VAUO-0GV7","value","filter",{"_253":252,"_248":247},"category","label","field","NSPECT-DKYF-6STS",{"_253":252,"_248":254},{"_118":1234,"_120":1235},"ngcApps",[1187],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1168":1169,"_1170":1171,"_1172":1173,"_1174":1175,"_1176":267,"_149":293,"_1177":1178,"_1179":59,"_1180":98,"_1181":98,"_1182":1183,"_1184":98,"_1185":1186},"amplitude",{"_1165":1166,"_806":-7,"_1167":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1159":1160,"_1161":1162,"_1163":1164},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787593421725,"1.12.0","defaultSearchSuggestions",{"_1046":374,"_1047":1048,"_1049":1050,"_1051":1052},"announcements",[],"consentState",{"_331":1036,"_1037":98,"_1038":34,"_1039":34,"_1040":1041},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1787593421726,[],[],[307,308,309,310,311],{"_48":62,"_50":73,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":68,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":312,"_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. 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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492],[355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371],{"_372":466,"_209":466,"_180":374,"_345":467},{"_372":462,"_209":462,"_180":374,"_345":463},{"_372":457,"_209":457,"_180":374,"_345":458},{"_372":453,"_209":453,"_180":374,"_345":454},{"_372":448,"_209":448,"_180":374,"_345":449},{"_372":443,"_209":443,"_180":374,"_345":444},{"_372":435,"_209":435,"_180":374,"_345":436},{"_372":427,"_209":427,"_180":374,"_345":428},{"_372":422,"_209":422,"_180":374,"_345":423},{"_372":417,"_209":417,"_180":374,"_345":418},{"_372":412,"_209":412,"_180":374,"_345":413},{"_372":407,"_209":407,"_180":374,"_345":408},{"_372":402,"_209":402,"_180":374,"_345":403},{"_372":395,"_209":395,"_180":374,"_345":396},{"_372":390,"_209":390,"_180":374,"_345":391},{"_372":383,"_209":383,"_180":374,"_345":384},{"_372":373,"_209":373,"_180":374,"_345":375},"hostName","amazon-adsystem.com","",[376,377],{"_77":382,"_379":373,"_380":34,"_180":381},{"_77":378,"_379":373,"_380":34,"_180":381},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[385,386],{"_77":389,"_379":383,"_380":34,"_180":388},{"_77":387,"_379":383,"_380":34,"_180":388},"IDE","This domain is owned by Doubleclick (Google). 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587],[534,535,536,537,538],{"_372":448,"_209":448,"_180":374,"_345":558},{"_372":422,"_209":422,"_180":374,"_345":553},{"_372":548,"_209":548,"_180":374,"_345":549},{"_372":543,"_209":543,"_180":374,"_345":544},{"_372":539,"_209":539,"_180":374,"_345":540},"nvidia.partners",[541],{"_77":542,"_379":539,"_380":34,"_180":416},"edge","app.hushly.com",[545],{"_77":546,"_379":543,"_380":34,"_180":547},"*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",[550],{"_77":551,"_379":548,"_380":34,"_180":552},"_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.",[646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674],[626,627,628,629],{"_372":641,"_209":641,"_180":374,"_345":642},{"_372":636,"_209":636,"_180":374,"_345":637},{"_372":435,"_209":435,"_180":374,"_345":633},{"_372":548,"_209":548,"_180":374,"_345":630},[631],{"_77":632,"_379":548,"_380":98,"_180":552},"_gh_sess",[634],{"_77":635,"_379":435,"_380":98,"_180":442},"YSC","bizible.com",[638],{"_77":639,"_379":636,"_380":34,"_180":640},"_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",[643],{"_77":644,"_379":641,"_380":34,"_180":645},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":716,"_379":494,"_380":34,"_180":717},{"_77":701,"_379":715,"_380":98,"_180":702},{"_77":684,"_379":497,"_380":34,"_180":609},{"_77":684,"_379":597,"_380":34,"_180":609},{"_77":713,"_379":494,"_380":34,"_180":714},{"_77":701,"_379":500,"_380":98,"_180":702},{"_77":711,"_379":494,"_380":34,"_180":712},{"_77":701,"_379":497,"_380":98,"_180":702},{"_77":709,"_379":596,"_380":34,"_180":710},{"_77":701,"_379":618,"_380":98,"_180":702},{"_77":681,"_379":497,"_380":34,"_180":683},{"_77":707,"_379":494,"_380":98,"_180":708},{"_77":705,"_379":494,"_380":34,"_180":706},{"_77":703,"_379":494,"_380":98,"_180":704},{"_77":701,"_379":592,"_380":98,"_180":702},{"_77":699,"_379":494,"_380":34,"_180":700},{"_77":697,"_379":494,"_380":34,"_180":698},{"_77":695,"_379":494,"_380":98,"_180":696},{"_77":693,"_379":601,"_380":34,"_180":694},{"_77":681,"_379":592,"_380":34,"_180":683},{"_77":691,"_379":494,"_380":34,"_180":692},{"_77":689,"_379":494,"_380":98,"_180":690},{"_77":687,"_379":494,"_380":34,"_180":688},{"_77":685,"_379":494,"_380":34,"_180":686},{"_77":684,"_379":494,"_380":34,"_180":609},{"_77":681,"_379":682,"_380":34,"_180":683},{"_77":679,"_379":494,"_380":34,"_180":680},{"_77":677,"_379":494,"_380":98,"_180":678},{"_77":675,"_379":494,"_380":34,"_180":676},"_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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