Robotics
Transformers
Safetensors
qwen2_5_vl
text-generation
vision-language-model
video-language-model
navigation
text-generation-inference
Instructions to use InternRobotics/InternVLA-N1-wo-dagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InternRobotics/InternVLA-N1-wo-dagger with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("InternRobotics/InternVLA-N1-wo-dagger") model = AutoModelForCausalLM.from_pretrained("InternRobotics/InternVLA-N1-wo-dagger") - Notebooks
- Google Colab
- Kaggle
Update model card: Add main paper link, update project page and citation
#1
by nielsr HF Staff - opened
This PR improves the model card for InternVLA-N1 by:
- Adding a direct link to the main research paper: Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation.
- Updating the project page link to the correct URL:
https://internrobotics.github.io/internvla-n1-dualvln.github.io/. - Reorganizing the top links for better readability and clarity, including the GitHub repository, updated project page, and technical report for InternVLA-N1.
- Updating the BibTeX citation to reflect the "Ground Slow, Move Fast" paper details.
- Ensuring no sample usage code snippets are added, as per guidelines requiring explicit evidence in the GitHub README.
Please review and merge if these improvements align with our goals for model card completeness.