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 1.72 GB
-
https://huggingface.co/LanguageBind/LanguageBind_Audio/resolve/838f59e3a5c23e76fcf06fed8d44bbc08dcd864d/pytorch_model.bin
- Command line
-
hf download hf://LanguageBind/LanguageBind_Audio@838f59e3a5c23e76fcf06fed8d44bbc08dcd864d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LanguageBind/LanguageBind_Audio/resolve/838f59e3a5c23e76fcf06fed8d44bbc08dcd864d/pytorch_model.bin
1.72 GB
- Xet hash:
- 5f3204e7744395c5b60f854257ab2b8d340aebf78fa26a063e8c162be11befe7
- Size of remote file:
- 1.72 GB
- SHA256:
- 0e57a6fddaaeaff2f74036a9d6de3636676186cea291c9f0c77eae14e3060214
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