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-Edit this `README.md` markdown file to author your organization card.
+# 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
+
+- **Universal Model Support**: Train SAEs on any HuggingFace 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
+
+We provide pre-trained SAEs and discovered features for popular models on HuggingFace:
+
+Each model repository includes:
+- Trained SAE weights
+- Catalog of discovered interpretable features
+- Example steering configurations
+- Performance benchmarks
+
+
+## Quick Start
+
+```python
+from open_concept_steering import ModelLoader, SAETrainer, FeatureScanner, ChatInterface
+
+# Load a model and train SAE
+model = ModelLoader.load("llama2")
+sae = SAETrainer.train(model)
+
+# Find features for a concept
+scanner = FeatureScanner(sae)
+feature = scanner.find_feature("golden gate bridge")
+
+# Chat with the steered model
+chat = ChatInterface(model)
+chat.run(amplified_features=[feature])
+```
+
+## Examples
+
+See the `examples/` directory for detailed notebooks demonstrating:
+- Training SAEs on different models
+- Finding and analyzing features
+- Steering model behavior
+- Interactive chat sessions
+
+## 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 ["Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet"](https://transformer-circuits.pub/2024/scaling-monosemanticity/) by Anthropic.