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 training_args.bin from jfernandez/cebuanoLanguageModel: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/training_args.bin
- Command line
-
hf download hf://jfernandez/cebuanoLanguageModel/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/jfernandez/cebuanoLanguageModel/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 7068101502264464dc13fdd6b8eb058c573c592028007a8a028ff02a5063acad
- Size of remote file:
- 3.39 kB
- SHA256:
- 2a9da38f20ddf6ab9aae7fb8129a73b1be6438ba4499e818f468e12abf4faafd
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