Instructions to use hf-internal-testing/tiny-random-MobileBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MobileBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MobileBertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MobileBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3e1a9b4f0399db671ea93c95f4dede588f4478cd9379b979e0486180138e347
- Size of remote file:
- 2.88 MB
- SHA256:
- 62faa62808d51d96b26b30ec49d2979d9f59d209b000d6d3610519c8e675a0a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.