Instructions to use thinkingface/test-onnx-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thinkingface/test-onnx-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="thinkingface/test-onnx-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("thinkingface/test-onnx-2") model = AutoModelForTokenClassification.from_pretrained("thinkingface/test-onnx-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from thinkingface/test-onnx-2: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/thinkingface/test-onnx-2/resolve/main/model.onnx
- Command line
-
hf download hf://thinkingface/test-onnx-2/model.onnx
-
curl -L -o model.onnx https://huggingface.co/thinkingface/test-onnx-2/resolve/main/model.onnx
1.42 GB
- Xet hash:
- f529e8348f560751114cb98aa479188f50fc784b67a2fe969907870f45b116da
- Size of remote file:
- 1.42 GB
- SHA256:
- b55db9a5cdc8b20a0e6435399996070855410bf88a26111c6a2a7c3a89de52d4
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