Instructions to use Nadav/PretrainedPHD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nadav/PretrainedPHD with Transformers:
# Load model directly from transformers import AutoModelForPreTraining model = AutoModelForPreTraining.from_pretrained("Nadav/PretrainedPHD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2069672d915de853779bb847e1136a39b907fdd6aee75d786bda957acc32e72
- Size of remote file:
- 449 MB
- SHA256:
- a8dae647210fff8b9f5db278d5b0306f28b88861635404f25868fce3e5d21973
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