Instructions to use radned/model1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radned/model1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="radned/model1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("radned/model1") model = AutoModelForSequenceClassification.from_pretrained("radned/model1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from radned/model1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/radned/model1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://radned/model1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/radned/model1/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e929bcc89dc8ffa7721f25721ec05e6d558482307f3714779e7a8ef032e63e11
- Size of remote file:
- 438 MB
- SHA256:
- 8a126e71de7983910eba3efe7682ba5cc2719f0e297a73bfea204125e4c9a70a
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