Image Feature Extraction
Transformers
Safetensors
mage_vit
feature-extraction
mage-vl
vision-encoder
codec-vit
video-understanding
custom_code
Instructions to use microsoft/Mage-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Mage-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="microsoft/Mage-ViT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/Mage-ViT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from microsoft/Mage-ViT: direct link, hf CLI and curl.
- Browser
- Download file 631 MB
-
https://huggingface.co/microsoft/Mage-ViT/resolve/main/model.safetensors
- Command line
-
hf download hf://microsoft/Mage-ViT/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/microsoft/Mage-ViT/resolve/main/model.safetensors
631 MB
- Xet hash:
- a92a0cde8ec7489c6a061dedb801029b4a1d3cc702645c22314e526556901739
- Size of remote file:
- 631 MB
- SHA256:
- 7848ad33832ef5a49c58c0d67a83d9269ace91349585e9a797364f8a0c535799
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.