NemoCurator Instruction Data Guard

Model Overview

Description:

Instruction Data Guard is a deep-learning classification model that helps identify LLM poisoning attacks in datasets.\nIt is trained on an instruction:response dataset and LLM poisoning attacks of such data.\nNote that optimal use for Instruction Data Guard is for instruction:response datasets.

License/Terms of Use:

NVIDIA Open Model License Agreement

Reference:

The Internal State of an LLM Knows When It's Lying: https://arxiv.org/pdf/2304.13734

Model Architecture:

Architecture Type: FeedForward MLP
\nNetwork Architecture: 4 Layer MLP

Input:

Input Type(s): Text Embeddings
\nInput Format(s): Numerical Vectors
\nInput Parameters: 1D Vectors
\nOther Properties Related to Input: The text embeddings are generated from the Aegis Defensive Model. The length of the vectors is 4096.

Output:

Output Type(s): Classification Scores
\nOutput Format: Array of shape 1
\nOutput Parameters: 1D
\nOther Properties Related to Output: Classification scores represent the confidence that the input data is poisoned or not.

Software Integration:

Runtime Engine(s):

NeMo Curator: https://github.com/NVIDIA/NeMo-Curator
Aegis: https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper

Preferred Operating System(s):

Linux
Windows

Model Version(s):

v1.0

Training, Testing, and Evaluation Datasets:

Data Collection Method by Dataset:

Synthetic
Hybrid: derived, open-source

Labeling Method by Dataset:

Synthetic

Evaluation Benchmarks:

Instruction Data Guard is evaluated based on two overarching criteria:

Success on identifying LLM poisoning attacks, after the model was trained on examples of the attacks.
Success on identifying LLM poisoning attacks, but without training on examples of those attacks, at all.

Success is defined as having an acceptable catch rate (recall scores for each attack) over a high specificity score (ex. 95%). Acceptable catch rates need to be high enough to identify at least several poisoned records in the attack.

Inference:

Engine: NeMo Curator and Aegis
\nTest Hardware:

A100 80GB GPU

How to Use in NeMo Curator:

The inference code is available on NeMo Curator's GitHub repository.
\nCheck out this example notebook to get started.

Ethical Considerations:

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.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n\n## Model Architecture:\n**Architecture Type:** FeedForward MLP
\n**Network Architecture:** 4 Layer MLP
\n\n## Input:\n**Input Type(s):** Text Embeddings
\n**Input Format(s):** Numerical Vectors
\n**Input Parameters:** 1D Vectors
\n**Other Properties Related to Input:** The text embeddings are generated from the [Aegis Defensive Model](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0). The length of the vectors is 4096.
\n\n## Output:\n**Output Type(s):** Classification Scores
\n**Output Format:** Array of shape 1
\n**Output Parameters:** 1D
\n**Other Properties Related to Output:** Classification scores represent the confidence that the input data is poisoned or not.
\n\n## Software Integration:\n**Runtime Engine(s):** \n* NeMo Curator: https://github.com/NVIDIA/NeMo-Curator
\n* Aegis: https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n\n**Preferred Operating System(s):**
\n* Linux
\n* Windows
\n\n## Model Version(s):\nv1.0
\n\n## Training, Testing, and Evaluation Datasets:\n\n**Data Collection Method by Dataset:**
\n* Synthetic
\n* Hybrid: derived, open-source
\n\n**Labeling Method by Dataset:**
\n* Synthetic
\n\n## Evaluation Benchmarks:\nInstruction Data Guard is evaluated based on two overarching criteria:
\n* Success on identifying LLM poisoning attacks, after the model was trained on examples of the attacks.
\n* Success on identifying LLM poisoning attacks, but without training on examples of those attacks, at all.
\n\nSuccess is defined as having an acceptable catch rate (recall scores for each attack) over a high specificity score (ex. 95%). Acceptable catch rates need to be high enough to identify at least several poisoned records in the attack.
\n\n## Inference:\n**Engine:** NeMo Curator and Aegis
\n**Test Hardware:**
\n* A100 80GB GPU
\n\n## How to Use in NeMo Curator:\nThe inference code is available on [NeMo Curator's GitHub repository](https://github.com/NVIDIA/NeMo-Curator).
\nCheck out [this example notebook](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/distributed_data_classification) to get started.\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\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","NemoCurator Instruction Data Guard",["D",1738603601797],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/instruction-data-guard-classifier","rawImageUrl","https://huggingface.co/nvidia/quality-classifier-deberta/resolve/main/llm-blog-data-curator-2847806-1920x1080.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[238,239,240,241,242],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":260,"_244":261,"_245":262,"_247":248},{"_205":257,"_244":257,"_245":258,"_247":248},{"_205":253,"_244":254,"_245":255,"_247":248},{"_205":250,"_244":250,"_245":251,"_247":248},{"_205":243,"_244":243,"_245":246,"_247":248},"NSPECT-M9O6-IPSK","value","filter",{"_249":248,"_244":243},"category","label","field","NeMo",{"_249":252,"_244":250},"framework","Natural Language Processing","uscs_natural_language_processing",{"_249":256,"_244":254},"application","English",{"_249":259,"_244":257},"language","Annotation","uscs_annotation",{"_249":256,"_244":261},{"_106":1438,"_108":1439},"ngcApps",[1391],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1372":1373,"_1374":1375,"_1376":1377,"_1378":1379,"_1380":274,"_137":298,"_1381":1382,"_1383":55,"_1384":85,"_1385":85,"_1386":1387,"_1388":85,"_1389":1390},"amplitude",{"_1369":1370,"_972":-7,"_1371":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1363":1364,"_1365":1366,"_1367":1368},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1247":223,"_1248":1249,"_1250":1251,"_1252":1253},"announcements",[],"consentState",{"_337":1237,"_1238":85,"_1239":30,"_1240":30,"_1241":1242},"consentConfig",{"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338},"showContactAdmin",1784104773483,[],[],[312,313,314,315,316,317],{"_44":59,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":67,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":318,"_54":55},6,"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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