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
- Xet hash:
- da070f8626ee36c29d81d14f7a18b0a943ba6477aaa86b433d0f865f98bf8392
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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