Ultra-fast MicroLoRA adaptation for WASM - rank-2 LoRA with <100us latency for per-operator learning
npm install @ruvector/learning-wasmUltra-fast MicroLoRA adaptation for WebAssembly - rank-2 LoRA with <100us latency for per-operator learning.
- MicroLoRA: Lightweight Low-Rank Adaptation for neural networks
- Sub-100us Latency: Optimized for real-time adaptation
- Rank-2 LoRA: Minimal memory footprint with effective learning
- WASM Optimized: Built with Rust for maximum performance in browsers and Node.js
``bash`
npm install @ruvector/learning-wasm
`javascript
import init, { MicroLoraAdapter } from '@ruvector/learning-wasm';
await init();
// Create a MicroLoRA adapter
const adapter = new MicroLoraAdapter(inputDim, outputDim, rank);
// Apply adaptation
const result = adapter.forward(input);
// Update weights based on feedback
adapter.update(gradient, learningRate);
``
- Adaptation latency: <100 microseconds
- Memory overhead: Minimal (rank-2 matrices only)
- Browser compatible: Works in all modern browsers
- Node.js compatible: Full support for server-side usage
MIT OR Apache-2.0