Instructions to use gubartz/sparse_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gubartz/sparse_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gubartz/sparse_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gubartz/sparse_model") model = AutoModelForMaskedLM.from_pretrained("gubartz/sparse_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88e3c55559957bb0ee701db4a096053f0b5440dd7a3ccc97f235a28c9d8e928e
- Size of remote file:
- 440 MB
- SHA256:
- 8c55e491124aef4ad80194c475c355f320bb080ceba45e36091db6bd34a305e2
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