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rank-2 LoRA with <100us latency for per-operator learning.\n\n## Features\n\n- **MicroLoRA**: Lightweight Low-Rank Adaptation for neural networks\n- **Sub-100us Latency**: Optimized for real-time adaptation\n- **Rank-2 LoRA**: Minimal memory footprint with effective learning\n- **WASM Optimized**: Built with Rust for maximum performance in browsers and Node.js\n\n## Installation\n\n```bash\nnpm install @ruvector/learning-wasm\n```\n\n## Usage\n\n```javascript\nimport init, { MicroLoraAdapter } from '@ruvector/learning-wasm';\n\nawait init();\n\n// Create a MicroLoRA adapter\nconst adapter = new MicroLoraAdapter(inputDim, outputDim, rank);\n\n// Apply adaptation\nconst result = adapter.forward(input);\n\n// Update weights based on feedback\nadapter.update(gradient, learningRate);\n```\n\n## Performance\n\n- Adaptation latency: <100 microseconds\n- Memory overhead: Minimal (rank-2 matrices only)\n- Browser compatible: Works in all modern browsers\n- Node.js compatible: Full support for server-side usage\n\n## License\n\nMIT OR Apache-2.0\n\n## Links\n\n- [GitHub Repository](https://github.com/ruvnet/ruvector)\n- [Documentation](https://ruv.io)\n","readmeFilename":"README.md"}