Instructions to use timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k", pretrained=True) - Transformers
How to use timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/vit_medium_patch16_rope_reg1_gap_256.sbb_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_medium_patch16_rope_reg1_gap_256.sbb_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k: direct link, hf CLI and curl.
- Browser
- Download file 155 MB
-
https://huggingface.co/timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k/resolve/4bd496e6ba4e41fde41d8e6d07273f9ba025e3c8/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k@4bd496e6ba4e41fde41d8e6d07273f9ba025e3c8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_medium_patch16_rope_reg1_gap_256.sbb_in1k/resolve/4bd496e6ba4e41fde41d8e6d07273f9ba025e3c8/pytorch_model.bin
155 MB
- Xet hash:
- b4dad5ba9f75072853b9b43ed4e719197b24f523ac29f816ef149b3bab1c82dc
- Size of remote file:
- 155 MB
- SHA256:
- 4e39eab10369cb0416a76f0fcb43da8a8f7ffe1f1e0fee5f59e80e4d54868d0c
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