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", device_map="auto")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BurnyCoder/grokking-modular-multiplication-transformer", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68f19dc5864fab5623d5d978fd43843d03de749854aa83f8a57d3696f5378487
- Size of remote file:
- 369 MB
- SHA256:
- e148061c97dc59dd77353e8045b2aa01d842bbb4e17c4715ff32846283753e3a
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