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:
- 1e7bf585c67ea9de2fd85e10ae3f12398f01bd21c3356ee3ae24cfb2b38d0e34
- Size of remote file:
- 16.6 MB
- SHA256:
- 9acc5180920cd415b2c00a59749c2d92e7f2e7834c6f8729b636db5884f3c1ef
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