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:
- 38ad7d010ebd8fc690dc9f30f7bb4863155c97070719b72c6df7f1c0e190f319
- Size of remote file:
- 360 kB
- SHA256:
- 0086ab55e72a0b111f05fe23384e07c3d6af871fae4b9229f98d0f1dc7f30af2
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