Instructions to use belfner/vit_small_patch16_lingbot.robbyant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use belfner/vit_small_patch16_lingbot.robbyant with timm:
import timm model = timm.create_model("hf-hub:belfner/vit_small_patch16_lingbot.robbyant", pretrained=True) - Transformers
How to use belfner/vit_small_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_small_patch16_lingbot.robbyant")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("belfner/vit_small_patch16_lingbot.robbyant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from belfner/vit_small_patch16_lingbot.robbyant: direct link, hf CLI and curl.
- Browser
- Download file 86.5 MB
-
https://huggingface.co/belfner/vit_small_patch16_lingbot.robbyant/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://belfner/vit_small_patch16_lingbot.robbyant/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/belfner/vit_small_patch16_lingbot.robbyant/resolve/main/pytorch_model.bin
86.5 MB
- Xet hash:
- 46bc650fae198ce04dbfafe0d430c1e153110c9169f023cd9a46d9c5aa0c2812
- Size of remote file:
- 86.5 MB
- SHA256:
- 1ee09c96c84d9d7fb3332f41984da1b0083f78438652ce1ba02179074082d4cf
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