---
license: mit
title: Open Concept Steering
sdk: static
emoji: 👁
colorFrom: indigo
colorTo: indigo
short_description: Training SAEs
---
# Open Concept Steering

Open Concept Steering is an open-source library for discovering and manipulating interpretable features in large language models using Sparse Autoencoders (SAEs). Inspired by Anthropic's work on [Scaling Monosemanticity](https://transformer-circuits.pub/2024/scaling-monosemanticity/) and [Golden Gate Claude](https://www.anthropic.com/news/golden-gate-claude), this project aims to make concept steering accessible to the broader research community.

## Features

Coming soon!
- **Universal Model Support**: Train SAEs on any Hugging Face transformer model
- **Feature Discovery**: Find interpretable features representing specific concepts
- **Concept Steering**: Amplify or suppress discovered features to influence model behavior
- **Interactive Chat**: Chat with models while manipulating their internal features

## Pre-trained Models

In the spirit of fully open-source models, we have started training SAEs on [OLMo 2 7B](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct).

We provide pre-trained SAEs and discovered features for popular models on Hugging Face:

Each model repository will include:
- Trained SAE weights
- Catalog of discovered interpretable features
- Example steering configurations

## Datasets

The dataset from OLMo 2 7B's middle layer is [here](https://huggingface.co/spaces/hbfreed/olmo2-sae-steering-demo).
It is about 600 million residual stream vectors.

More to come!


## Quick Start

## Examples

Check out the [steered OLMo 7B model](https://huggingface.co/spaces/hbfreed/olmo2-sae-steering-demo)!

## License

This project is licensed under the MIT License.

## Citation

If you feel compelled to cite this library in your work, feel free to do so however you please.

## Acknowledgments

This project builds upon the work described in [Towards Monosemanticity: Decomposing Language Models With Dictionary Learning](https://transformer-circuits.pub/2023/monosemantic-features), [Update on how we train SAEs](https://transformer-circuits.pub/2024/april-update/index.html#training-saes), and [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://transformer-circuits.pub/2024/scaling-monosemanticity/) by Anthropic, and this project absolutely would not have been possible without it.