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# CorrSteer Frontend
## Overview
CorrSteer demonstrates how text classification datasets can be used to steer large language models (LLMs), correlating with SAE (Sparse Autoencoder) features. This demo incorporates a modern tech stack for a seamless and efficient experience.
---
## How to Run the Demo
1. **Set Environment Variables:**
Create a `.env` file in the `demo` directory and include the following:
```env
VITE_API_BASE_URL=<your-api-url>
```
2. **Install Dependencies:**
```bash
pnpm i
```
3. **Start the Development Server:**
```bash
pnpm dev
```
The application will be available at `http://localhost:5173` by default.
4. **Build for Production (Optional):**
```bash
pnpm build
pnpm preview
```
---
## Key Features
- **Dataset & Model Selection:**
Select datasets and models using dropdown menus.
- **Streaming Outputs:**
Generate outputs from multiple models with live updates as data streams.
- **Interactive Tabs:**
Switch between different categories for customized prompts.
---
## Technology Stack
1. **Vite:**
- Development server and build tool.
2. **React:**
- UI library for building components and managing state.
3. **Tailwind CSS:**
- CSS framework for styling.
4. **ShadCN/UI:**
- Pre-built component library for UI elements.
---
## License
This project is licensed under the MIT License.
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