Instructions to use fav-kky/FERNET-CC_sk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fav-kky/FERNET-CC_sk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fav-kky/FERNET-CC_sk")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fav-kky/FERNET-CC_sk") model = AutoModelForMaskedLM.from_pretrained("fav-kky/FERNET-CC_sk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fav-kky/FERNET-CC_sk: direct link, hf CLI and curl.
- Browser
- Download file 654 MB
-
https://huggingface.co/fav-kky/FERNET-CC_sk/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fav-kky/FERNET-CC_sk/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fav-kky/FERNET-CC_sk/resolve/main/pytorch_model.bin
654 MB
- Xet hash:
- cd002fd030e89821aa364d7ea64401eb224e0b561e190520a163e323c7113d05
- Size of remote file:
- 654 MB
- SHA256:
- dc9302f9aa9e8f871e3d2b606e4d34678b378de5438d8018f78281d597904f05
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