Instructions to use facebook/vjepa2-vitg-fpc64-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/vjepa2-vitg-fpc64-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="facebook/vjepa2-vitg-fpc64-384")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("facebook/vjepa2-vitg-fpc64-384") model = AutoModelForMultimodalLM.from_pretrained("facebook/vjepa2-vitg-fpc64-384") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a377d41cec53b8aeb652a5aa1ad6692b5fac257553f1692d640edfec068334c5
- Size of remote file:
- 4.14 GB
- SHA256:
- 861d9fafec3cf764f01f00ed26bf53bda531116cc6b67d7dbadd80e8be814e9b
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