Instructions to use spottedzebra/quantized_onnx_skill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spottedzebra/quantized_onnx_skill with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="spottedzebra/quantized_onnx_skill")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("spottedzebra/quantized_onnx_skill") model = AutoModelForTokenClassification.from_pretrained("spottedzebra/quantized_onnx_skill", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_optimized.onnx from spottedzebra/quantized_onnx_skill: direct link, hf CLI and curl.
- Browser
- Download file 931 MB
-
https://huggingface.co/spottedzebra/quantized_onnx_skill/resolve/main/model_optimized.onnx
- Command line
-
hf download hf://spottedzebra/quantized_onnx_skill/model_optimized.onnx
-
curl -L -o model_optimized.onnx https://huggingface.co/spottedzebra/quantized_onnx_skill/resolve/main/model_optimized.onnx
931 MB
- Xet hash:
- c42a2d48c0043899ea76545dce051c1cc861a00739e950f8687e2e54c41eaa26
- Size of remote file:
- 931 MB
- SHA256:
- 5d287826996e69aed5c0b3b46f20f2325d1ac2e26208a77238d3e9d62fdce403
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