Instructions to use microsoft/xclip-base-patch32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch32")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch32") model = AutoModel.from_pretrained("microsoft/xclip-base-patch32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/xclip-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 787 MB
-
https://huggingface.co/microsoft/xclip-base-patch32/resolve/c032045517cf2ffd284b2f4de2d8d18ae17ae258/pytorch_model.bin
- Command line
-
hf download hf://microsoft/xclip-base-patch32@c032045517cf2ffd284b2f4de2d8d18ae17ae258/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/xclip-base-patch32/resolve/c032045517cf2ffd284b2f4de2d8d18ae17ae258/pytorch_model.bin
787 MB
- Xet hash:
- 3e018f861e74e9f263cf364b74de11859488be6e04cee8e60d84e83939dab7e4
- Size of remote file:
- 787 MB
- SHA256:
- d2840b05bd4ed269688ff76a239e703dd930db4a160726c5b79b9ef26f173452
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.