Video-Text-to-Text
Transformers
Safetensors
English
qwen3_vl
image-text-to-text
video-understanding
video-temporal-grounding
multimodal
vision-language
Instructions to use zhengmh/TaRO-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhengmh/TaRO-8B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("zhengmh/TaRO-8B") model = AutoModelForMultimodalLM.from_pretrained("zhengmh/TaRO-8B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ab1daabc5cd14b92d14ce3737f3a058731ddc42edd8ea137807d519bb2263c7
- Size of remote file:
- 11.4 MB
- SHA256:
- 16d8b711f75cbf394b8c3b7a63be4751a9a9419598430817427f001a7cbd2e19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.