Instructions to use kobe/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kobe/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kobe/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("kobe/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("kobe/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kobe/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/kobe/vit-base-beans/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://kobe/vit-base-beans@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kobe/vit-base-beans/resolve/refs%2Fpr%2F2/pytorch_model.bin
343 MB
- Xet hash:
- ca137ecb49bf1980ceb69ddbb3b16d78c12a278d2c2742e187673e161edef1b0
- Size of remote file:
- 343 MB
- SHA256:
- 2d9de3deb2dfca5584141a3b0150c1ab84b9f42d02de17b4fa7dd143fd3e04ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.