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:
- 4eed3c3f110bef677f0f3963fb30ef13c25dbb90136703633a73cd1fba4d926e
- Size of remote file:
- 2.82 MB
- SHA256:
- e76da9ed0d884c50a6bd8cc0545f72faa35fc82167b9163b9d837e5299f334b4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.