Instructions to use quincyqiang/chtesla3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quincyqiang/chtesla3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="quincyqiang/chtesla3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("quincyqiang/chtesla3") model = AutoModelForMaskedLM.from_pretrained("quincyqiang/chtesla3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 891791e3497e32b1026ab242796a5f1549f3a3846e48a7311429a9643c564b23
- Size of remote file:
- 408 MB
- SHA256:
- f76e686ddfcd44ca66351800420a842f9a12e5280f64da45de1dfe3d5a7885f1
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