Instructions to use centaur31/vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use centaur31/vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="centaur31/vit-base")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("centaur31/vit-base") model = AutoModel.from_pretrained("centaur31/vit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from centaur31/vit-base: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/centaur31/vit-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://centaur31/vit-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/centaur31/vit-base/resolve/main/flax_model.msgpack
346 MB
- Xet hash:
- afcefd3a4ccbbfea04ece7761d38e8c93bde1b37d98286acb7cc518399e05a13
- Size of remote file:
- 346 MB
- SHA256:
- aa00da5f7abb687576e6f9138d48cbb0fe7489f2bc9768793d262c3987b3db32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.