Instructions to use Bainbridge/vilt-b32-mlm-mami with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bainbridge/vilt-b32-mlm-mami with Transformers:
# Load model directly from transformers import AutoProcessor, ViltForImageTextClassification processor = AutoProcessor.from_pretrained("Bainbridge/vilt-b32-mlm-mami") model = ViltForImageTextClassification.from_pretrained("Bainbridge/vilt-b32-mlm-mami", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Bainbridge/vilt-b32-mlm-mami: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/Bainbridge/vilt-b32-mlm-mami/resolve/main/training_args.bin
- Command line
-
hf download hf://Bainbridge/vilt-b32-mlm-mami/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Bainbridge/vilt-b32-mlm-mami/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 4306fe69cd9559844066c01686a0dce24c7ee674f08f42fccdf63444ad148bc2
- Size of remote file:
- 4.09 kB
- SHA256:
- 1528d4e8e623b28e3541aed2cfde0d6a884ef9ffbe86f7f85838b0d0428c592c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.