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sparse-autoencoder-training

Train Sparse Autoencoders with SAELens to decompose transformer activations into interpretable features.

Updated May 20, 2026
Search Tags:#superposition#transformers#interpretability#feature-discovery#sae#sae-lens#sparse-autoencoder
One-click install
npx skills add https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent --skill sparse-autoencoder-training-sriramkunamsetty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sparse-autoencoder-training
Source: https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent/tree/main/hermes-agent/optional-skills/mlops/saelens
Command: npx skills add https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent --skill sparse-autoencoder-training-sriramkunamsetty

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

Core Features & Use Cases

  • Step-by-step workflows for loading pre-trained SAEs, configuring training, and analyzing activations from transformer models.
  • Guidance on comparing SAE architectures (standard, gated, topk) and tuning sparsity and reconstruction trade-offs to reveal interpretable features.
  • Real-world usage includes discovering semantically meaningful feature directions, steering experiments, and ablation studies across prompts.

Quick Start

Configure and run the SAELens training workflow to train an SAE on a transformer activation stream and begin interpretable feature discovery.

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 a sparse autoencoder on transformer activations?▼

To train a sparse autoencoder on transformer activations, use SAELens to configure the training workflow on a transformer activation stream. This decomposes neural activations into interpretable features for mechanistic interpretability analysis.

What is superposition in neural networks and how do sparse autoencoders analyze it?▼

Superposition is when models represent more features than dimensions available. Sparse autoencoders analyze superposition by decomposing transformer activations into monosemantic, interpretable feature directions across model layers.

Do I need CUDA-enabled hardware to train sparse autoencoders?▼

Yes, you need CUDA-enabled hardware to train or load pre-trained sparse autoencoders. The workflow requires compatible CUDA-enabled hardware running Python 3.10 or higher to process transformer activations.

How do I compare SAE architectures like standard, gated, and topk?▼

Compare standard, gated, and topk SAE architectures by evaluating their sparsity and reconstruction trade-offs. This analysis reveals how each architecture discovers semantically meaningful and interpretable feature directions.

Can I use pre-trained SAEs for feature discovery instead of training?▼

Yes, you can load pre-trained SAEs for feature discovery. The workflow provides step-by-step guidance for loading pre-trained SAEs to analyze transformer activations and study monosemantic representations in language models.

What can I do with interpretable features after decomposing transformer activations?▼

After decomposing transformer activations into interpretable features, you can perform steering experiments and ablation studies across prompts. These features enable mechanistic interpretability analysis of monosemantic representations in language models.

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