Instructions to use LanguageBind/LanguageBind_Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LanguageBind/LanguageBind_Video with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="LanguageBind/LanguageBind_Video") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForZeroShotImageClassification model = AutoModelForZeroShotImageClassification.from_pretrained("LanguageBind/LanguageBind_Video", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LanguageBind/LanguageBind_Video: direct link, hf CLI and curl.
- Browser
- Download file 2.13 GB
-
https://huggingface.co/LanguageBind/LanguageBind_Video/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LanguageBind/LanguageBind_Video/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LanguageBind/LanguageBind_Video/resolve/main/pytorch_model.bin
2.13 GB
- Xet hash:
- 849900ea00f2569c4f9cf0259e7bc4f9d259a7c4f42d001dcc8c9380b0268096
- Size of remote file:
- 2.13 GB
- SHA256:
- 4fa6663eafe03922ba4b94eda8a18cd3e25276b9af4540e7c995fceb221a029b
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