Instructions to use ghazishazan/VideoMolmo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghazishazan/VideoMolmo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="ghazishazan/VideoMolmo", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ghazishazan/VideoMolmo", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update model card with detailed information and pipeline tag
#2
by nielsr HF Staff - opened
This PR significantly enhances the model card by:
- Updating the
pipeline_tagfromvideo-text-to-texttoimage-segmentationto more accurately reflect the model's functionality of producing segmentation masks from video input. This will improve its discoverability on the Hugging Face Hub. - Integrating detailed sections from the project's GitHub README, including an expanded overview, key highlights, architectural diagrams, benchmark results, and practical inference steps.
- Adding visual elements from the GitHub README to make the model card more informative and engaging.
ghazishazan changed pull request status to merged