Instructions to use belfner/vit_large_patch16_lingbot.robbyant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use belfner/vit_large_patch16_lingbot.robbyant with timm:
import timm model = timm.create_model("hf-hub:belfner/vit_large_patch16_lingbot.robbyant", pretrained=True) - Transformers
How to use belfner/vit_large_patch16_lingbot.robbyant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="belfner/vit_large_patch16_lingbot.robbyant")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("belfner/vit_large_patch16_lingbot.robbyant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from belfner/vit_large_patch16_lingbot.robbyant: direct link, hf CLI and curl.
- Browser
- Download file 1.21 GB
-
https://huggingface.co/belfner/vit_large_patch16_lingbot.robbyant/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://belfner/vit_large_patch16_lingbot.robbyant/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/belfner/vit_large_patch16_lingbot.robbyant/resolve/main/pytorch_model.bin
1.21 GB
- Xet hash:
- 973cd1c4eba80977796e726a6c19d9e1875ab565f1efa9c28a040d991c24c865
- Size of remote file:
- 1.21 GB
- SHA256:
- b8a7ccc65491d472fdfc131ed1bf29316ca229920920821b8762525a9b885690
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