Instructions to use JunxiongWang/BiGS_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_512")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bece0b452378a479005762107534f15154d4b1cda493d6abfabe68cb727d8f6
- Size of remote file:
- 1.39 GB
- SHA256:
- d407cedc35f569df80d52ce0a47a6755d050de07545b22f8e62a722fbcb8dbba
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