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/optimizer.pt from jfernandez/cebuanoLanguageModel: direct link, hf CLI and curl.
- Browser
- Download file 668 MB
-
https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/checkpoint-10000/optimizer.pt
- Command line
-
hf download hf://jfernandez/cebuanoLanguageModel/checkpoint-10000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/checkpoint-10000/optimizer.pt
668 MB
- Xet hash:
- cdb9f3159368aae0577281a3e01167e3a278c5555eba77b7776ec1ff7bc4e12a
- Size of remote file:
- 668 MB
- SHA256:
- d528b6983aea75e56ee61b1ab94fb125f4dadd9f8c62fb5ff8d68baf5d47b212
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