Instructions to use Migga/ViTBERT_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Migga/ViTBERT_V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Migga/ViTBERT_V1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Migga/ViTBERT_V1") model = AutoModel.from_pretrained("Migga/ViTBERT_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Migga/ViTBERT_V1: direct link, hf CLI and curl.
- Browser
- Download file 614 MB
-
https://huggingface.co/Migga/ViTBERT_V1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Migga/ViTBERT_V1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Migga/ViTBERT_V1/resolve/main/pytorch_model.bin
614 MB
- Xet hash:
- d02650d203cf3ea7c2f6183a8f830702a48a745a86dd9c96f8673d1189aa99aa
- Size of remote file:
- 614 MB
- SHA256:
- 8c52c02d9a5a79442a706e84567471c52b5cff8299fdccda6a1f908505c33b84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.