Instructions to use timm/vit_large_patch14_clip_224.openai_ft_in12k 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_in12k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_large_patch14_clip_224.openai_ft_in12k", pretrained=True) - Transformers
How to use timm/vit_large_patch14_clip_224.openai_ft_in12k 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_in12k") 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_in12k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_large_patch14_clip_224.openai_ft_in12k: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in12k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_large_patch14_clip_224.openai_ft_in12k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in12k/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 16c1c4d6e5eeed2adb074e1375418d913d4aab2120d4f335c1be2ce321b978d7
- Size of remote file:
- 1.26 GB
- SHA256:
- 913ffbb2b5518dcfdc546ee5fbef7f2c2d4edb3931caab1d7fbe3aa1868ce5a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.