Instructions to use LanguageBind/LanguageBind_Video_merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LanguageBind/LanguageBind_Video_merge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="LanguageBind/LanguageBind_Video_merge") 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_merge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LanguageBind/LanguageBind_Video_merge: direct link, hf CLI and curl.
- Browser
- Download file 2.11 GB
-
https://huggingface.co/LanguageBind/LanguageBind_Video_merge/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://LanguageBind/LanguageBind_Video_merge@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LanguageBind/LanguageBind_Video_merge/resolve/refs%2Fpr%2F2/pytorch_model.bin
2.11 GB
- Xet hash:
- 94b7c22bdce0c7da61032e9a7ede745111bd2acab68128f28975f751facbbedb
- Size of remote file:
- 2.11 GB
- SHA256:
- ef677a2ffe018ff22021dd166bc26ffe9196eb414626cbd9a2ac7231308bd52d
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