d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
78 lines
1.6 KiB
Markdown
78 lines
1.6 KiB
Markdown
# SONA WASM Example
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Interactive browser demo of the Self-Optimizing Neural Architecture (SONA).
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## Quick Start
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1. Build the WASM module (if not already built):
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```bash
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cd ..
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wasm-pack build --target web --features wasm
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cp -r pkg wasm-example/
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```
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2. Serve the example:
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```bash
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cd wasm-example
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python3 -m http.server 8080
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```
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3. Open in browser:
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```
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http://localhost:8080
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```
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## Features
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- **Real-time Learning**: Record trajectories and see instant updates
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- **LoRA Visualization**: Watch transformation in real-time
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- **Statistics Dashboard**: Monitor patterns, quality, and performance
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- **Interactive Controls**: Adjust configuration and run experiments
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## Files
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- `index.html` - Demo page with UI
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- `index.js` - JavaScript logic using WASM bindings
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- `package.json` - NPM configuration
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- `pkg/` - Generated WASM package
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- `sona.js` - JavaScript bindings
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- `sona_bg.wasm` - WebAssembly binary
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- `sona.d.ts` - TypeScript definitions
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## Usage Example
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```javascript
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import init, { WasmSonaEngine } from './pkg/sona.js';
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async function main() {
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await init();
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const engine = new WasmSonaEngine(256);
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const trajectoryId = engine.start_trajectory(new Float32Array(256).fill(0.1));
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engine.record_step(trajectoryId, 42, 0.8, 1000);
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engine.end_trajectory(trajectoryId, 0.85);
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const output = engine.apply_lora(new Float32Array(256).fill(1.0));
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console.log('Transformed output:', output);
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}
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main();
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```
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## Performance
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- WASM file size: ~1.5MB (release build)
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- Initialization: < 100ms
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- Per-trajectory overhead: < 1ms
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- LoRA application: < 0.1ms (256-dim)
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## Browser Support
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- Chrome/Edge 91+
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- Firefox 89+
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- Safari 14.1+
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## License
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MIT OR Apache-2.0
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