Instructions to use hoomancat/mizu-gado with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hoomancat/mizu-gado with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hoomancat/mizu-gado")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hoomancat/mizu-gado") model = AutoModelForSequenceClassification.from_pretrained("hoomancat/mizu-gado", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hoomancat/mizu-gado: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/hoomancat/mizu-gado/resolve/main/training_args.bin
- Command line
-
hf download hf://hoomancat/mizu-gado/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/hoomancat/mizu-gado/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 9489d2840cdca21649999b4f5549fc72714e78cccf5bf251f4795979cece3eaf
- Size of remote file:
- 5.3 kB
- SHA256:
- ecbdccafe85ed62662e75a565f9757e58248ba53a80179020acbfe0a1a32af7c
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