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
Improve model card: add metadata, links and sample usage
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the community science team at Hugging Face. I've opened this PR to improve the documentation for OmniStream.
The changes include:
- Adding metadata for better discoverability (
pipeline_tag,library_name, andlicense). - Adding links to the research paper, project page, and official GitHub repository.
- Including a sample usage snippet derived from your official README to help users get started with the model.
Thanks for the PR, merging now.
StreamFormer changed pull request status to merged