sparse-autoencoder-training

Name: sparse-autoencoder-training
Availability: InStock
Author: Nitish

Train and analyze Sparse Autoencoders on neural network activations with Python and TransformerLens.

Updated Jun 26, 2026

Search Tags:#superposition #feature discovery #sparse autoencoder #safety analysis #neural network interpretability

Ready to run

npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill sparse-autoencoder-training-nitish-gitbit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sparse-autoencoder-training
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/optional-skills/mlops/saelens
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill sparse-autoencoder-training-nitish-gitbit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sae-lens>=6.0.0, transformer-lens>=2.0.0, torch>=2.0.0, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides tools and guidance for training and analyzing Sparse Autoencoders (SAEs) to decompose neural network activations into interpretable features, enhancing understanding and interpretability of complex models.

Core Features & Use Cases

Training Sparse Autoencoders: Offers a framework for training SAEs on various neural network architectures.
Feature Analysis: Enables analysis of SAE features to understand model activations and identify interpretable concepts.
Use Case: For instance, it can be used to study superposition in language models and identify safety-relevant features like deception or harmful content.

Quick Start

Install the Skill with 'pip install sae-lens' and follow the provided tutorials to train and analyze SAEs.

Frequently Asked Questions about sparse-autoencoder-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I train sparse autoencoders for neural network interpretability?▼

Train sparse autoencoders for neural network interpretability using this framework to decompose model activations into interpretable features. It provides scripts and guidance leveraging SAE Lens and TransformerLens to analyze activation geometry and feature discovery.

What is superposition in language models and how do I analyze it?▼

Superposition in language models is a phenomenon where networks represent more features than dimensions. You can analyze it by training sparse autoencoders on model activations to identify and isolate these interpretable concepts using the provided analysis tools.

How can I identify safety-relevant features like deception in a language model?▼

Identify safety-relevant features like deception by applying sparse autoencoder analysis to model activations. This Skill helps you decompose activations into interpretable features, enabling the detection of harmful content and safety analysis within neural networks.

Do I need TransformerLens and PyTorch to run sparse autoencoder training?▼

Yes, you need TransformerLens and PyTorch to run sparse autoencoder training. The Skill requires Python with specific dependencies including sae-lens, transformer-lens, and torch to properly execute the training and feature analysis scripts.

Can I use sparse autoencoders to understand feature geometry across different neural network architectures?▼

Yes, you can use sparse autoencoders to understand feature geometry across different neural network architectures. The framework supports training SAEs on various models to analyze activation structures and discover interpretable features.

What is the best way to start discovering interpretable features in model activations?▼

The best way to start discovering interpretable features is by installing sae-lens and following the provided tutorials. This Skill offers a framework to train SAEs and analyze the resulting features to understand model behavior and activation geometry.