Instructions to use google/vit-huge-patch14-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-huge-patch14-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="google/vit-huge-patch14-224-in21k")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/vit-huge-patch14-224-in21k") model = AutoModel.from_pretrained("google/vit-huge-patch14-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/vit-huge-patch14-224-in21k: direct link, hf CLI and curl.
- Browser
- Download file 2.53 GB
-
https://huggingface.co/google/vit-huge-patch14-224-in21k/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/vit-huge-patch14-224-in21k/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/vit-huge-patch14-224-in21k/resolve/main/flax_model.msgpack
2.53 GB
- Xet hash:
- 8c1531654eb23b95ee892cefef3defa36901200fc0c830a6904226b8339d184c
- Size of remote file:
- 2.53 GB
- SHA256:
- 4c7c1efdbde7374a23b7ed3dbbd33e945ee5874c2c0e0358e99508b8b7224a5d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.