Instructions to use InternRobotics/VLN-PE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InternRobotics/VLN-PE with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InternRobotics/VLN-PE", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add robotics pipeline tag, license, paper, project, and code links
#3
by nielsr HF Staff - opened
This PR enhances the model card by:
- Adding the
pipeline_tag: roboticsto better categorize the model on the Hub. - Specifying the
license: mitfor the model's code, as indicated in the GitHub repository. - Including a direct link to the paper: Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities.
- Adding a link to the project page: https://crystalsixone.github.io/vln_pe.github.io/.
- Adding a link to the GitHub repository: https://github.com/InternRobotics/InternNav.
- Adding the paper's abstract for better context.
These additions will make the model more discoverable and provide clearer information for users.
kew1046 changed pull request status to closed