Instructions to use belfner/vit_base_patch16_lingbot.robbyant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use belfner/vit_base_patch16_lingbot.robbyant with timm:
import timm model = timm.create_model("hf_hub:belfner/vit_base_patch16_lingbot.robbyant", pretrained=True) - Transformers
How to use belfner/vit_base_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_base_patch16_lingbot.robbyant")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("belfner/vit_base_patch16_lingbot.robbyant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d9cef3d8beaa88537d72c265bcf1cd356d6bae47ad4f1b23864c543262f2845
- Size of remote file:
- 343 MB
- SHA256:
- 8acbd811a656551113f5fe1a4d62980d8aad5bd44275d89bc449be83ca2c1f99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.