Instructions to use SBMI/pert_gpt_63M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SBMI/pert_gpt_63M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SBMI/pert_gpt_63M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SBMI/pert_gpt_63M") model = AutoModelForMaskedLM.from_pretrained("SBMI/pert_gpt_63M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a0c70b2f6e4bb3f22dcc05950e9d1fc48218834922b1fd37e86be0a797dbb9f
- Size of remote file:
- 426 MB
- SHA256:
- cfc2a719564b0b5caf572a66662652dd99a9921bdf0b7cf0bff08c62a989b45a
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