Instructions to use osanseviero/khipu_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osanseviero/khipu_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="osanseviero/khipu_example")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("osanseviero/khipu_example") model = AutoModelForSequenceClassification.from_pretrained("osanseviero/khipu_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from osanseviero/khipu_example: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/osanseviero/khipu_example/resolve/main/training_args.bin
- Command line
-
hf download hf://osanseviero/khipu_example/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/osanseviero/khipu_example/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 999977bf1fe2eefe80e6003bf3871d8656ef7dbb9f412d2352a899ef7e125c1b
- Size of remote file:
- 3.52 kB
- SHA256:
- dc80858324e80fb61b185f77f6ca44f85f9d41899d8c9bbdc8fe086438cc4e74
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