Robotics
Transformers
Safetensors
qwen3_vl
image-text-to-text
embodied-navigation
vision-language-navigation
visual-tracking
vision-language-action
qwen3-vl
Instructions to use LightOriginsHQ/LightNav-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LightOriginsHQ/LightNav-0 with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("LightOriginsHQ/LightNav-0") model = AutoModelForMultimodalLM.from_pretrained("LightOriginsHQ/LightNav-0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eff46f7f74167b811621faa8ed0dfda77308a7df7d7e9e3a19330913ff6777e8
- Size of remote file:
- 30.8 kB
- SHA256:
- dfde603f96b333cda4c676613ac2f772cec3906a8dbbb7a7415bfeb4aa04e4bf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.