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_quantized.onnx from thinkingface/test-onnx-2: direct link, hf CLI and curl.
- Browser
- Download file 356 MB
-
https://huggingface.co/thinkingface/test-onnx-2/resolve/main/model_quantized.onnx
- Command line
-
hf download hf://thinkingface/test-onnx-2/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/thinkingface/test-onnx-2/resolve/main/model_quantized.onnx
356 MB
- Xet hash:
- c2a324f1bc324f79f8a58e30c98aa62de02b608dc23baa35fad1435f69c20dfd
- Size of remote file:
- 356 MB
- SHA256:
- 7c7bce0d8328cfd7e2f83b33200d43ee5462cc16253d8ddab1681f8cda64cf73
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