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")# 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 model.safetensors from microsoft/xclip-base-patch32: direct link, hf CLI and curl.
- Browser
- Download file 786 MB
-
https://huggingface.co/microsoft/xclip-base-patch32/resolve/main/model.safetensors
- Command line
-
hf download hf://microsoft/xclip-base-patch32/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/microsoft/xclip-base-patch32/resolve/main/model.safetensors
786 MB
- Xet hash:
- e973fdcbadc844673fa3eb13a57e626d6914ed64199695da882963422ccc3f80
- Size of remote file:
- 786 MB
- SHA256:
- abf286e8cdd0612761c3e42d3a55eca998382dfa67a04a0f3fdcdfa4f150cdbb
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