Instructions to use philschmid/distilbert-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/distilbert-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="philschmid/distilbert-onnx")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("philschmid/distilbert-onnx") model = AutoModelForQuestionAnswering.from_pretrained("philschmid/distilbert-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from philschmid/distilbert-onnx: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/philschmid/distilbert-onnx/resolve/main/model.onnx
- Command line
-
hf download hf://philschmid/distilbert-onnx/model.onnx
-
curl -L -o model.onnx https://huggingface.co/philschmid/distilbert-onnx/resolve/main/model.onnx
261 MB
- Xet hash:
- 9488aaeb2e31869e3069db72bb96adfc794ceeb3e66aa5672bf1ea68dbfa50bc
- Size of remote file:
- 261 MB
- SHA256:
- 6fdcb418daa489b890ce39df16a5cf6755e4026845ac09e1c35bfcd6a2c1f5d4
路
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