Text Classification
Transformers
PyTorch
English
mechanistic-interpretability
grokking
modular-arithmetic
transformer
TransformerLens
toy-model
Instructions to use BurnyCoder/grokking-modular-multiplication-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BurnyCoder/grokking-modular-multiplication-transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BurnyCoder/grokking-modular-multiplication-transformer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BurnyCoder/grokking-modular-multiplication-transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 0838cd4a8466038b184e6686ed1fd28e0042a50214413ae06f3cc0febc027d61
- Size of remote file:
- 253 kB
- SHA256:
- 960737458f48565a253a4c2fdba0b28f94f56796746254ffbc903e8dcd81317d
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