These SAEs were trained using a blend of chat (
lmsys/lmsys-chat-1m) and pretraining data (
monology/pile-uncopyrighted), and also a small amount of
emergent misalignment data.
Each SAE is trained using
BatchTopK. For each layer, we train 4 SAEs, with
k=32,64,128,256.
Note: the first 8 tokens of each sample are excluded from training, and additionally activations with large outlier norms (>10x median norm of a batch) are filtered out.