@ruvector/learning-wasm

Ultra-fast MicroLoRA adaptation for WebAssembly - rank-2 LoRA with <100us latency for per-operator learning.

Features

• 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

Installation

npm install @ruvector/learning-wasm

Usage

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);

Performance

• 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

License

MIT OR Apache-2.0

Links

• GitHub Repository
• Documentation