Instructions to use timm/vit_large_patch14_clip_224.openai_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_large_patch14_clip_224.openai_ft_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_large_patch14_clip_224.openai_ft_in1k", pretrained=True) - Transformers
How to use timm/vit_large_patch14_clip_224.openai_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_large_patch14_clip_224.openai_ft_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_large_patch14_clip_224.openai_ft_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_large_patch14_clip_224.openai_ft_in1k: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_large_patch14_clip_224.openai_ft_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in1k/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- 287f9f51077974954c249e584ffb9c4a4974b0d0e081b1fa1c591d54291b6439
- Size of remote file:
- 1.22 GB
- SHA256:
- fa5128e5434a96a4174d968fcaf6ce5fa5d2aa1ddf75e8bf3215189dc29b7dfc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.