Training Sparse Autoencoders on Language Models
☆1,519Aug 30, 2026Updated this week
Alternatives and similar repositories for SAELens
Users that are interested in SAELens are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- A library for mechanistic interpretability of GPT-style language models☆3,850Updated this week
- Sparsify transformers with SAEs and transcoders☆739Updated this week
- ☆428Aug 21, 2025Updated last year
- Create feature-centric and prompt-centric visualizations for sparse autoencoders (like those from Anthropic's published research).☆274Feb 27, 2026Updated 6 months ago
- The nnsight package enables interpreting and manipulating the internals of deep learned models.☆1,087Updated this week
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- ☆184May 1, 2026Updated 4 months ago
- Delphi was the home of a temple to Phoebus Apollo, which famously had the inscription, 'Know Thyself.' This library lets language models …☆275Updated this week
- Mechanistic Interpretability Visualizations using React☆367Apr 30, 2026Updated 4 months ago
- ☆111May 23, 2026Updated 3 months ago
- ☆211Nov 17, 2024Updated last year
- Sparse Autoencoder for Mechanistic Interpretability☆306Jul 20, 2024Updated 2 years ago
- A framework for training, analyzing, and visualizing sparse autoencoders and related interpretability methods☆227Updated this week
- ☆602Jul 19, 2024Updated 2 years ago
- ☆223Oct 14, 2025Updated 10 months ago
- AI Agents on DigitalOcean Gradient AI Platform • AdBuild production-ready AI agents using customizable tools or access multiple LLMs through a single endpoint. Create custom knowledge bases or connect external data.
- Using sparse coding to find distributed representations used by neural networks.☆313Nov 10, 2023Updated 2 years ago
- open source interpretability platform 🧠☆1,124Updated this week
- ☆2,899Updated this week
- ☆1,258Updated this week
- ViT Prisma is a mechanistic interpretability library for Vision and Video Transformers (ViTs).☆387Jul 23, 2025Updated last year
- ☆141Oct 28, 2023Updated 2 years ago
- Sparse Autoencoder Training Library☆58May 1, 2025Updated last year
- ☆74Jan 17, 2025Updated last year
- ☆295Oct 1, 2024Updated last year
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click. Zero configuration with optimized deployments.
- ☆62Nov 19, 2024Updated last year
- Stanford NLP Python library for understanding and improving PyTorch models via interventions☆899Mar 6, 2026Updated 5 months ago
- Implementation of the BatchTopK activation function for training sparse autoencoders (SAEs)☆67Jul 24, 2025Updated last year
- ☆32Apr 4, 2024Updated 2 years ago
- Improving Steering Vectors by Targeting Sparse Autoencoder Features☆30Nov 20, 2024Updated last year
- Unified access to Large Language Model modules using NNsight☆119Updated this week
- Code and results accompanying the paper "Refusal in Language Models Is Mediated by a Single Direction".☆434Jun 13, 2025Updated last year
- Steering Llama 2 with Contrastive Activation Addition☆250May 23, 2024Updated 2 years ago
- Tools for understanding how transformer predictions are built layer-by-layer☆611Aug 7, 2025Updated last year
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- ☆98Mar 28, 2025Updated last year
- A curated list of LLM Interpretability related material - Tutorial, Library, Survey, Paper, Blog, etc..☆307Jan 22, 2026Updated 7 months ago
- ☆79Mar 6, 2025Updated last year
- ☆267Nov 22, 2024Updated last year
- This repository collects all relevant resources about interpretability in LLMs☆404Nov 1, 2024Updated last year
- ☆24Feb 13, 2026Updated 6 months ago
- ☆26Aug 23, 2025Updated last year