Instructions to use osanseviero/test_adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osanseviero/test_adapters with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="osanseviero/test_adapters")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("osanseviero/test_adapters") model = AutoModelForSequenceClassification.from_pretrained("osanseviero/test_adapters", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from osanseviero/test_adapters: direct link, hf CLI and curl.
- Browser
- Download file 445 MB
-
https://huggingface.co/osanseviero/test_adapters/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://osanseviero/test_adapters/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/osanseviero/test_adapters/resolve/main/pytorch_model.bin
445 MB
- Xet hash:
- 4b713001b06ad4ced1180ea1201d21c28a0afb6a98695ede5eebf8846d71ec3c
- Size of remote file:
- 445 MB
- SHA256:
- 49265a4e7144c2cafe7d0761558c088b44fc6046bb11d9a394876b892e5360a7
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