Instructions to use microsoft/deberta-v3-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-v3-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/deberta-v3-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/deberta-v3-small", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from microsoft/deberta-v3-small: direct link, hf CLI and curl.
- Browser
- Download file 565 MB
-
https://huggingface.co/microsoft/deberta-v3-small/resolve/main/tf_model.h5
- Command line
-
hf download hf://microsoft/deberta-v3-small/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/microsoft/deberta-v3-small/resolve/main/tf_model.h5
565 MB
- Xet hash:
- cd797c535d0506564bd43b8eec87e9fccbfd4b134d85102fe7202a6258043ee7
- Size of remote file:
- 565 MB
- SHA256:
- 0239ab1f2d2be4a7b184cc37fe6e58e36bc536d2b2b24efe0ce654d203ef0c94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.