Instructions to use StreamFormer/OmniStream with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StreamFormer/OmniStream with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="StreamFormer/OmniStream")# Load model directly from transformers import VFMMultiFrameTransformer model = VFMMultiFrameTransformer.from_pretrained("StreamFormer/OmniStream", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b3a8916279f10c01f66525136cbe15cefb8dec149b43d6be88075f6a1a15cfa
- Size of remote file:
- 1.21 GB
- SHA256:
- f9da399c61360115d956466fd1dda29f457ae4a37ffb0ff9b3a9a357bc0033d2
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