Video-Text-to-Text
Transformers
Safetensors
llava_onevision
image-text-to-text
multimodal
multilingual
vlm
translation
Instructions to use utter-project/TowerVideo-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utter-project/TowerVideo-9B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("utter-project/TowerVideo-9B") model = AutoModelForMultimodalLM.from_pretrained("utter-project/TowerVideo-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from utter-project/TowerVideo-9B: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/utter-project/TowerVideo-9B/resolve/main/tokenizer.json
- Command line
-
hf download hf://utter-project/TowerVideo-9B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/utter-project/TowerVideo-9B/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- a358c37b0b386a3cfb252f1990a42b0c1b34a06f7e3e4bd3198e52f2eec51d92
- Size of remote file:
- 34.4 MB
- SHA256:
- a3883f610173b9dde5d8efddcda4e558f2c44095400f3639fa7123d8b00ebfbc
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