Instructions to use hf-tiny-model-private/tiny-random-DebertaV2Model 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-DebertaV2Model 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-DebertaV2Model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 449ac80495d3dbb8ea56550e026acbd48fbce2b3c8c6f2729f89810f5fa7d091
- Size of remote file:
- 16.7 MB
- SHA256:
- 40c5dc571f353b9d5f03161677fd990fd99cbe011b8fcd28a88ae2ebf1777634
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