Video-Text-to-Text
Transformers
Safetensors
llava_onevision
image-text-to-text
multimodal
multilingual
vlm
translation
Instructions to use utter-project/TowerVideo-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utter-project/TowerVideo-2B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("utter-project/TowerVideo-2B") model = AutoModelForImageTextToText.from_pretrained("utter-project/TowerVideo-2B") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 9041e4faa1f804cf10dd33aced69971fd5d7ce417e67046b1a753ed24d90e37a
- Size of remote file:
- 154 kB
- SHA256:
- 066b7bab00a7e2a6ff593739ba81af99f2a58c9a380b0acfc0a398881906a957
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.