Instructions to use google/gemma-scope-9b-pt-res with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SAELens
How to use google/gemma-scope-9b-pt-res with SAELens:
# pip install sae-lens from sae_lens import SAE sae, cfg_dict, sparsity = SAE.from_pretrained( release = "RELEASE_ID", # e.g., "gpt2-small-res-jb". See other options in https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml sae_id = "SAE_ID", # e.g., "blocks.8.hook_resid_pre". Won't always be a hook point ) - Notebooks
- Google Colab
- Kaggle
add experimental embedding SAEs
#4
by Aric - opened
SAEs on the raw embedding vectors (not including the sqrt(d_model) scaling that happens inside the model fwd pass).
Not trained on BOS, PAD, EOS and any token that didn't occur in the training data.
Aric changed pull request status to open
ArthurConmyGDM changed pull request status to merged