Instructions to use hf-tiny-model-private/tiny-random-VisualBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-VisualBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-VisualBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-VisualBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-VisualBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a50ae6997d981babe6593b5db72288a57ad55406c28cd1eb110e3192150d55c
- Size of remote file:
- 436 kB
- SHA256:
- f52d7cd92d534e2e036237d2e2e3c3473d45191dfc08a78fdbc623b093b795ac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.