Instructions to use fraternalilab/immunomatch-kappa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fraternalilab/immunomatch-kappa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fraternalilab/immunomatch-kappa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fraternalilab/immunomatch-kappa") model = AutoModelForSequenceClassification.from_pretrained("fraternalilab/immunomatch-kappa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fraternalilab/immunomatch-kappa: direct link, hf CLI and curl.
- Browser
- Download file 811 MB
-
https://huggingface.co/fraternalilab/immunomatch-kappa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fraternalilab/immunomatch-kappa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fraternalilab/immunomatch-kappa/resolve/main/pytorch_model.bin
811 MB
- Xet hash:
- 7486f16e8ff52d4025b4e4ef9efab1861c50c0b5746027168f892b56870f5356
- Size of remote file:
- 811 MB
- SHA256:
- 9650a580e936b16a84a2ecc9f7d5747e034ced14b86b58dde6942475c1640a54
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