Instructions to use tdunlap607/vfc-identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdunlap607/vfc-identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tdunlap607/vfc-identification", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tdunlap607/vfc-identification", trust_remote_code=True) model = AutoModel.from_pretrained("tdunlap607/vfc-identification", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:65ca3f722041c91df13a7b37c37029dc4ab7503839a5493f350249be0c9d8333
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size 498613516
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