Instructions to use Enverrr/ViT_exp_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Enverrr/ViT_exp_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Enverrr/ViT_exp_1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Enverrr/ViT_exp_1") model = AutoModelForImageClassification.from_pretrained("Enverrr/ViT_exp_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Enverrr/ViT_exp_1: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Enverrr/ViT_exp_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Enverrr/ViT_exp_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Enverrr/ViT_exp_1/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- c03186ce52cf4d50742f3a051db4e160eba72758c572c62ac6b18797256e568a
- Size of remote file:
- 343 MB
- SHA256:
- 2433761299abbc65c696fb912bcee972c49f03a27ec1c926b953120de21ff6b7
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