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:
- c05fae90e6d20ee636ecae224d075b909f152a28604cf6996edf512c165cd422
- Size of remote file:
- 3.58 kB
- SHA256:
- d85227686229406fb9b040a829882aea0d7981193fd088dcbb04a36ef4fd5f7c
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