Instructions to use hf-tiny-model-private/tiny-random-ElectraModel 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-ElectraModel 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-ElectraModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ElectraModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ElectraModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46f4cbd2e50ca0302595c81ff0419d23ef333e2da489c207f863284de38463f4
- Size of remote file:
- 1.12 MB
- SHA256:
- aa52c4c9af66810440128af23c6d89753ecaac43b34adcbd61a0397fb0d5ac98
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.