Instructions to use fraternalilab/immunomatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fraternalilab/immunomatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fraternalilab/immunomatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fraternalilab/immunomatch") model = AutoModelForSequenceClassification.from_pretrained("fraternalilab/immunomatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fraternalilab/immunomatch: direct link, hf CLI and curl.
- Browser
- Download file 811 MB
-
https://huggingface.co/fraternalilab/immunomatch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fraternalilab/immunomatch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fraternalilab/immunomatch/resolve/main/pytorch_model.bin
811 MB
- Xet hash:
- e84c68dfad7264acfc75e8392bdc2f7e3a0d33868114a675472d9138028a387b
- Size of remote file:
- 811 MB
- SHA256:
- 1bdd2320d7126bd656d4f4d660a011b839dfbe0b759ba8866adee829d2f32e9a
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