Instructions to use LanguageBind/LanguageBind_Audio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LanguageBind/LanguageBind_Audio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="LanguageBind/LanguageBind_Audio") 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_Audio", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LanguageBind/LanguageBind_Audio: direct link, hf CLI and curl.
- Browser
- Download file 1.73 GB
-
https://huggingface.co/LanguageBind/LanguageBind_Audio/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LanguageBind/LanguageBind_Audio/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LanguageBind/LanguageBind_Audio/resolve/main/pytorch_model.bin
1.73 GB
- Xet hash:
- f4c96ab6f0c7d0b1a65ffa7f88f87a20aa288e23734032ebc3c175ec8c62ade2
- Size of remote file:
- 1.73 GB
- SHA256:
- 3ac9eaedddafed4708bc734d7969c76ae132d29966392e26c6a29b4e740bd4e7
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