Instructions to use jyb/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jyb/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jyb/vit-base-beans") 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("jyb/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("jyb/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jyb/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/jyb/vit-base-beans/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jyb/vit-base-beans/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jyb/vit-base-beans/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- eb1bdae9e6708323df8a8ff60a44538463ecd3dd8848b00021066b654c92972f
- Size of remote file:
- 343 MB
- SHA256:
- 5905c55d530b3e6d6342660d65b3208c6c0f939fb3d719972798f4178193759d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.