Instructions to use Jiqing/tiny-random-tvp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jiqing/tiny-random-tvp with Transformers:
# Load model directly from transformers import AutoProcessor, TvpForVideoGrounding processor = AutoProcessor.from_pretrained("Jiqing/tiny-random-tvp") model = TvpForVideoGrounding.from_pretrained("Jiqing/tiny-random-tvp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jiqing/tiny-random-tvp: direct link, hf CLI and curl.
- Browser
- Download file 21.2 MB
-
https://huggingface.co/Jiqing/tiny-random-tvp/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jiqing/tiny-random-tvp/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jiqing/tiny-random-tvp/resolve/main/pytorch_model.bin
21.2 MB
- Xet hash:
- 6787ba27ecb6d0476e333d4627dbcbf47c4b4f29f7714a6d4bf7f8f5e88c3956
- Size of remote file:
- 21.2 MB
- SHA256:
- 39da9aa9349666f165113ea02a324a51d8f1736a034c876e518e50ef9506ba27
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.