Text Classification
Transformers
Safetensors
English
llama
text-generation
content-moderation
safety
text-embeddings-inference
Instructions to use UnionStreet/VISION-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnionStreet/VISION-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UnionStreet/VISION-1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UnionStreet/VISION-1") model = AutoModelForCausalLM.from_pretrained("UnionStreet/VISION-1") - Notebooks
- Google Colab
- Kaggle
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README.md
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- **Training Data**: Specialized safety and content moderation dataset
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- **Model Type**: Decoder-only transformer
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- **Parameters**: 8 billion
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- **Training Infrastructure**: 2x NVIDIA
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- **License**: Same as base model
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## Usage
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- **Training Data**: Specialized safety and content moderation dataset
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- **Model Type**: Decoder-only transformer
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- **Parameters**: 8 billion
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- **Training Infrastructure**: 2x NVIDIA H200 SXM GPUs
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- **License**: Same as base model
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## Usage
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