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 model.safetensors 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/model.safetensors
- Command line
-
hf download hf://timm/vit_large_patch14_clip_224.openai_ft_in12k/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in12k/resolve/main/model.safetensors
1.26 GB
- Xet hash:
- 0a24e8c23d4d79e8ffd6a74555a6495f45cb4ad0c63fdfd7f2001058f85c880c
- Size of remote file:
- 1.26 GB
- SHA256:
- 71fabc172630aaa31313554d5a5f80d724ba920dc0d7d0f0469f0f2e8e06282f
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