Instructions to use WishArdently/InternVideo2Stage2-VisionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WishArdently/InternVideo2Stage2-VisionEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="WishArdently/InternVideo2Stage2-VisionEncoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WishArdently/InternVideo2Stage2-VisionEncoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e8589232bd2eca2ec1c28e8b083b9c1bcbd212f2fe4b3d6d50cb79bdf3b90d3
- Size of remote file:
- 2.1 GB
- SHA256:
- b846fa2b0540df04a40b8e54568a667de8b03c2d2d8c0062aaa4b606a23fc174
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