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 checkpoint-10000/pytorch_model.bin from jfernandez/cebuanoLanguageModel: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/checkpoint-10000/pytorch_model.bin
- Command line
-
hf download hf://jfernandez/cebuanoLanguageModel/checkpoint-10000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/checkpoint-10000/pytorch_model.bin
334 MB
- Xet hash:
- ed2255b83eeda97fa5804354da7d43055d81a706dbc204b3e2d84541b9a5dc04
- Size of remote file:
- 334 MB
- SHA256:
- fece542e3e1d5eee328cf5ab03fe4f57aaba90eeccf613b89be0ad0dcb792ef3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.