Instructions to use Ferrag/Wiki_LLM_82M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ferrag/Wiki_LLM_82M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ferrag/Wiki_LLM_82M")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Ferrag/Wiki_LLM_82M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4647325f9e6ab0738b046794b588905caea19a5f1f139d8f17cbc12cb8e0074
- Size of remote file:
- 334 MB
- SHA256:
- 0f937042c8644d7e4f9594f3643c03b7381da2be43f4659d051e64261e528fe4
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