enguard / tiny-guard-8m-en-response-safety-nvidia-aegis

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Model's Last Updated: October 31 2025
text-classification

Introduction of tiny-guard-8m-en-response-safety-nvidia-aegis

Model Details of tiny-guard-8m-en-response-safety-nvidia-aegis

enguard/tiny-guard-8m-en-response-safety-nvidia-aegis

This model is a fine-tuned Model2Vec classifier based on minishlab/potion-base-8m for the response-safety found in the nvidia/Aegis-AI-Content-Safety-Dataset-2.0 dataset.

Installation
pip install model2vec[inference]
Usage
from model2vec.inference import StaticModelPipeline

model = StaticModelPipeline.from_pretrained(
  "enguard/tiny-guard-8m-en-response-safety-nvidia-aegis"
)

model.predict(["Example sentence"])
model.predict_proba(["Example sentence"])
Why should you use this model?
  • Optimized for precision to reduce false positives.
  • Extremely fast inference using static embeddings powered by Model2Vec.
This model variant

Below is a quick overview of the model variant and core metrics.

Field Value
Classifies response-safety
Base Model minishlab/potion-base-8m
Precision 0.7679
Full metrics (JSON)
{
  "safe": {
    "precision": 0.7769784172661871,
    "recall": 0.7714285714285715,
    "f1-score": 0.7741935483870968,
    "support": 420.0
  },
  "unsafe": {
    "precision": 0.7581863979848866,
    "recall": 0.7639593908629442,
    "f1-score": 0.7610619469026548,
    "support": 394.0
  },
  "accuracy": 0.7678132678132679,
  "macro avg": {
    "precision": 0.7675824076255369,
    "recall": 0.7676939811457578,
    "f1-score": 0.7676277476448758,
    "support": 814.0
  },
  "weighted avg": {
    "precision": 0.767882525869587,
    "recall": 0.7678132678132679,
    "f1-score": 0.7678374660961015,
    "support": 814.0
  }
}
Other model variants

Below is a general overview of the best-performing models for each dataset variant.

Resources
Citation

If you use this model, please cite Model2Vec:

@software{minishlab2024model2vec,
  author       = {Stephan Tulkens and {van Dongen}, Thomas},
  title        = {Model2Vec: Fast State-of-the-Art Static Embeddings},
  year         = {2024},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17270888},
  url          = {https://github.com/MinishLab/model2vec},
  license      = {MIT}
}

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