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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f68a4476edc2b9565c9d3cbe17008c41107aa12e86d0524a24359081081d992
- Size of remote file:
- 369 MB
- SHA256:
- fb51c4e3c54f7883e757577b31a86f0d3058d49d8433cfceb3eee64602be407a
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