Instructions to use DevShubham/vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevShubham/vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DevShubham/vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("DevShubham/vit") model = AutoModelForImageClassification.from_pretrained("DevShubham/vit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from DevShubham/vit: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/DevShubham/vit/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://DevShubham/vit/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/DevShubham/vit/resolve/main/flax_model.msgpack
346 MB
- Xet hash:
- b24e3e3f997afcca3cfd21be6fd07bf1f140599a56d7eadaa531bfe0b1645a39
- Size of remote file:
- 346 MB
- SHA256:
- e0c809a1fe1d79c51c4da8a9ebd6c0923bac317ca469a33c6620977054149471
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.