sparse-autoencoder-training

To train a sparse autoencoder for interpretable features, you decompose neural network activations into sparse, human-readable representations. This process requires Python environments with sae-lens, transformer-lens, and torch to execute the training and analysis steps.

A sparse autoencoder is used in interpretable AI to decompose complex neural network activations into distinct, interpretable features. This allows researchers to understand model behavior, perform feature discovery, and conduct safety analysis on deep learning models.

Yes, you can use sae-lens and transformer-lens to analyze GPT-2 model behavior. The Skill provides step-by-step instructions to train sparse autoencoders on GPT-2 activations and interpret the concepts the model has learned during predictions.

Yes, you need PyTorch (torch>=2.0.0) to train sparse autoencoders on model activations. The required environment also includes sae-lens>=6.0.0 and transformer-lens>=2.0.0 to properly run the training and feature analysis workflows.

The best way to discover active features during a prediction is to train a sparse autoencoder on the model's activations and analyze the resulting learned representations. This technique reveals exactly which interpretable features trigger for specific inputs.

You should use sparse autoencoders for deep learning safety analysis when you need to understand internal model behavior and feature discovery. By decomposing activations into interpretable features, you can identify potentially unsafe concepts learned by the model.