Instructions to use hf-internal-testing/tiny-random-AlbertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-AlbertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-AlbertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-AlbertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-AlbertModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 391d31836499db746311577da43fab859a4e5627cd1d6cb54c4dc0e44e5881cf
- Size of remote file:
- 15.9 MB
- SHA256:
- 04a290543b8085f3d0cffd4086c87810165a9de82fe18e3107e19de7c75a1c92
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.