Instructions to use google/vit-large-patch16-224-in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-large-patch16-224-in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="google/vit-large-patch16-224-in21k")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("google/vit-large-patch16-224-in21k") model = AutoModel.from_pretrained("google/vit-large-patch16-224-in21k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/vit-large-patch16-224-in21k: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/google/vit-large-patch16-224-in21k/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/vit-large-patch16-224-in21k/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/vit-large-patch16-224-in21k/resolve/main/flax_model.msgpack
1.22 GB
- Xet hash:
- fdc40f7a5e8a6c01a4b50a050cb18589efc9f475dfaabd87fa12271e84f8d68f
- Size of remote file:
- 1.22 GB
- SHA256:
- 456242dd3659795f4391ef9d34571012d92b34320eab96726fc6b174089fee34
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.