git clone [email protected]:AsaCooperStickland/transparent.git
cd transparent
pip install -e .

Training a language model with activation either relu,gelu or solu. You can add a penalty to the Fisher information matrix with --fisher-penalty-weight, or add an L1 norm penalty to the activations with --l1-norm-penalty.

python transparent/scripts/train_wikitext_transformer.py --act-type $ACTIVATION 

Performing a linear mode connectivity check on two models trained with the same settings, for a particular activation and l1/fisher penalty:

python transparent/scripts/basin_testing.py --act-type relu --l1-norm-penalty 0.0005 --fisher-penalty-weight 0.1

Evaluate custom trained models by decoding their weights into token space. Huggingface pretrained model support coming soon! So far only works for the second feed-forward network weights.

python transparent/scripts/decode_value_weights.py --act-type $ACTIVATION