Instructions to use decoderesearch/gemma-4-saes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SAELens
How to use decoderesearch/gemma-4-saes 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
Training Procedure and Activation Details of SAEs
#1
by iain-ualberta - opened
Hi, my team is considering using your SAEs for a project, but there is no description of the training procedure or activation functions used in your SAEs. It would help to know these for added details in a publication, trustworthiness, and to know how we might interpret the latent variables. Could you add these details to the README.md?
You can see all the exact training settings used for each SAE by looking at the runner_cfg.json in each SAE dir, for instance here's one for the layer 28 gemma-4-e2b SAE: https://huggingface.co/decoderesearch/gemma-4-saes/blob/main/gemma-4-e2b/btk-mat-layer-28-k-100/runner_cfg.json. These were all trained with SAELens.