Instructions to use hf-tiny-model-private/tiny-random-DebertaModel 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-DebertaModel 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-DebertaModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-DebertaModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51afaff9704944cd70370d62ae2d126a42646df813cf92bbc687681566bf7d54
- Size of remote file:
- 438 kB
- SHA256:
- ce0c70269b805a8be678b6963ae3de499318a0960aab422ffc37287fa2fdf636
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