Instructions to use ddevaul/char-level-logion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ddevaul/char-level-logion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ddevaul/char-level-logion")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ddevaul/char-level-logion") model = AutoModelForMaskedLM.from_pretrained("ddevaul/char-level-logion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68b91847674e52d9a87efe43e2ea3605cecaebee315ba333660522355b209c5e
- Size of remote file:
- 344 MB
- SHA256:
- 10051537e5e6ee07f357028d8a1171fe3e9508e4d1ee89318a2144f64bcbc7a6
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