Instructions to use jfernandez/cebuanoLanguageModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfernandez/cebuanoLanguageModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jfernandez/cebuanoLanguageModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jfernandez/cebuanoLanguageModel") model = AutoModelForMaskedLM.from_pretrained("jfernandez/cebuanoLanguageModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jfernandez/cebuanoLanguageModel: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jfernandez/cebuanoLanguageModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- 12cf139b5f538cabea667c78f9ca0e61d9b1ff8de378504fbc4c54454ed7db66
- Size of remote file:
- 334 MB
- SHA256:
- df804d392ccd2da7c023807b67106053ac068f20b5b58af56beda8dc3469a2a7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.