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

Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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Model Overview

Description:

Mistral-NeMo-Minitron-4B-128K-Instruct is a model for generating responses for various text-generation tasks including roleplaying, retrieval augmented generation, and function calling. It is a fine-tuned version of nvidia/Mistral-NeMo-Minitron-8B-Base, which was pruned and distilled from Mistral-NeMo 12B using our LLM compression technique. The model was trained using a multi-stage Supervised Fine-tuning (SFT) and preference-based alignment technique with NeMo Aligner. For details on the alignment technique, please refer to the Nemotron-4 340B Technical Report. The model supports a context length of 131,072 tokens.

This model is ready for commercial use.

License/Terms of Use:

NVIDIA Open Model License

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Decoder-only

Input:

Input Type(s): Text (Prompt)
\nInput Format(s): String
\nInput Parameters: One Dimensional (1D)
\nOther Properties Related to Input: The model has a maximum of 131,072 input tokens.

Output:

Output Type(s): Text (Response)
\nOutput Format: String
\nOutput Parameters: 1D
\nOther Properties Related to Output: The model has a maximum of 131,072 input tokens. Maximum output for both versions can be set apart from input.

Prompt Format:

We recommend using the following prompt template, which was used to fine-tune the model. The model may not perform optimally without it.

System\n{system prompt}\n~~User\n{user prompt}~~\nAssistant\\n\n\">
<s>System\n{system prompt}</s>\n<s>User\n{user prompt}</s>\n<s>Assistant\\n\n

\n
\n
Note that a newline character (\\n) should be added after <s>Assistant as a generation prompt
\n
\n
Evaluation Results
\n
TBD
\n
Software Integration: (Cloud)
\n
Runtime Engine: NeMo Framework 24.09
\n
Supported Hardware Microarchitecture Compatibility:
\n
\n
[NVIDIA Ampere]
\n
[NVIDIA Blackwell]
\n
[NVIDIA Hopper]
\n
[NVIDIA Lovelace]
\n
\n
[Preferred/Supported] Operating System(s):
\n
\n
Linux
\n
\n
Model Version(s)
\n
Mistral-NeMo-Minitron 4B-128K Instruct
\n
Training & Evaluation:
\n
Training Dataset:
\n
** Data Collection Method by dataset
\n
\n
Hybrid: Automated, Human
\n
\n
** Labeling Method by dataset
\n
\n
Hybrid: Automated, Human
\n
\n
Evaluation Dataset:
\n
** Data Collection Method by dataset
\n
\n
Hybrid: Automated, Human
\n
\n
** Labeling Method by dataset
\n
\n
Human
\n
\n
Inference:
\n
Engine: TRT-LLM
\nTest Hardware:
\n
\n
A100
\n
A10G
\n
H100
\n
L40S
\n
\n
Supported Hardware Platform(s): L40S, A10G, A100, H100
\n
Ethical Considerations:
\n
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
\n
For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.
\n
Please report security vulnerabilities or NVIDIA AI Concerns here.
","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/ace/models/mistral-nemo-minitron-4b-128k-instruct/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/ace/mistral-nemo-minitron-4b-128k-instruct:1.0.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/ace/models/mistral-nemo-minitron-4b-128k-instruct/-/version-history","accessCheckResult",{"_102":103,"_104":105,"_106":20},"hasRelatedCollections","context",{"_98":99,"_100":101},1783758911907,[],[],[92,93,94,95,96,97],{"_33":47,"_35":56,"_37":49,"_39":50,"_41":57,"_43":44},{"_33":47,"_35":54,"_37":38,"_39":55,"_41":51,"_43":44},{"_33":47,"_35":52,"_37":38,"_39":55,"_41":57,"_43":44},{"_33":47,"_35":48,"_37":49,"_39":50,"_41":51,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":42,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},"orgName","nvidia","teamName","ace","reason","subscription_required","requiredProducts",[107],"allowed","ace-ea","id","nvidia/ace/mistral-nemo-minitron-4b-128k-instruct","key","nvidia/ace/models/mistral-nemo-minitron-4b-128k-instruct","resourceType","models","resourceId","mistral-nemo-minitron-4b-128k-instruct","shortDescription","Generates responses for various text-generation tasks including roleplaying, retrieval augmented generation, and function calling.","description","# Model Overview\n\n## Description:\n[//]: # ([Provide additional details about the algorithm/model; include supporting image/video and/or reference blog/article, if available.] [This model is ready for commercial/non-commercial use.] OR [This model is for research and development only.] OR [This model is for demonstration purposes and not for production usage.]
)\n\nMistral-NeMo-Minitron-4B-128K-Instruct is a model for generating responses for various text-generation tasks including roleplaying, retrieval augmented generation, and function calling. It is a fine-tuned version of [nvidia/Mistral-NeMo-Minitron-8B-Base](https://huggingface.co/nvidia/Mistral-NeMo-Minitron-8B-Base), which was pruned and distilled from [Mistral-NeMo 12B](https://huggingface.co/nvidia/Mistral-NeMo-12B-Base) using [our LLM compression technique](https://arxiv.org/abs/2407.14679). The model was trained using a multi-stage Supervised Fine-tuning (SFT) and preference-based alignment technique with [NeMo Aligner](https://github.com/NVIDIA/NeMo-Aligner). For details on the alignment technique, please refer to the [Nemotron-4 340B Technical Report](https://arxiv.org/abs/2406.11704). The model supports a context length of 131,072 tokens. \n\nThis model is ready for commercial use.\n\n\n### License/Terms of Use: \n[NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)\n\n## Model Architecture:\n**Architecture Type:** Transformer
\n**Network Architecture:** Decoder-only
\n\n\n## Input: \n**Input Type(s):** Text (Prompt)
\n**Input Format(s):** String
\n**Input Parameters:** One Dimensional (1D)
\n**Other Properties Related to Input:** The model has a maximum of 131,072 input tokens.
\n \n## Output: \n**Output Type(s):** Text (Response)
\n**Output Format:** String
\n**Output Parameters:** 1D
\n**Other Properties Related to Output:** The model has a maximum of 131,072 input tokens. Maximum output for both versions can be set apart from input.
\n\n\n## Prompt Format:\n\nWe recommend using the following prompt template, which was used to fine-tune the model. The model may not perform optimally without it.\n\n```\n~~System\n{system prompt}~~\n~~User\n{user prompt}~~\nAssistant\\n\n```\n- Note that a newline character (\\n) should be added after `Assistant` as a generation prompt\n\n\n## Evaluation Results\n\nTBD\n\n\n## Software Integration: (Cloud)\n**Runtime Engine:** NeMo Framework 24.09
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* [NVIDIA Ampere]
\n* [NVIDIA Blackwell]
\n* [NVIDIA Hopper]
\n* [NVIDIA Lovelace]
\n\n\n**[Preferred/Supported] Operating System(s):**
\n* Linux
\n\n### Model Version(s)\nMistral-NeMo-Minitron 4B-128K Instruct\n\n# Training & Evaluation: \n\n## Training Dataset:\n\n** Data Collection Method by dataset
\n* Hybrid: Automated, Human
\n\n** Labeling Method by dataset
\n* Hybrid: Automated, Human
\n\n## Evaluation Dataset:\n\n** Data Collection Method by dataset
\n* Hybrid: Automated, Human
\n\n** Labeling Method by dataset
\n* Human
\n\n## Inference:\n**Engine:** TRT-LLM
\n**Test Hardware:**
\n* A100
\n* A10G
\n* H100
\n* L40S
\n\n**Supported Hardware Platform(s):** L40S, A10G, A100, H100
\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Mistral-Nemo-Minitron-4B-128k-Instruct","updatedDate",["D",1734558756462],"publisher","NVIDIA","latestVersion","1.0.0","versions","collectionCounts","imageUrl","/api/image/nvidia/ace/models/mistral-nemo-minitron-4b-128k-instruct","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",10831213824,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","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","privacy","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Domain: | Game Non-Playable Character (NPC) Development\nModel Type: | Generative Pre-Trained Transformer (GPT)-3\nIntended User: | Enterprise developers building game NPCs.\nOutput: | Text String(s)\nDescribe how the model works: | Generates a response using the input text and context such as NPC background information. \nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Accuracy, Latency, and Throughput\nPotential Known Risks: | 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. This issue could be exacerbated without the use of the recommended prompt template. The model may also amplify biases and return toxic responses especially when prompted with toxic prompts. If you are going to use this model in an agentic workflow, validate that the imported packages are from a trusted source to ensure end-to-end security.\nLicensing: | [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)\nTechnical Limitations: | The model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text. This model may generate inaccurate responses in non-English languages.","safetyAndSecurity","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any personal data used? | Not Applicable\nPersonal data used to create this model? | Datasets used for fine-tuning did not introduce any personal data that did not exist in the base model.\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf personal data is collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf personal data is collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf personal data is 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\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable","featureLabels",[198],"displayLabels",[166,167,168,169,170,171,172],"policyLabels",[],"systemLabels",[],"products",[159],"hasNimMetadata",{"_160":107,"_120":161,"_116":162,"_163":164},"name","NVIDIA ACE Early Access","NVIDIA ACE Early Access is for assets towards the ACE early access. 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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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