Squashed 'vendor/ruvector/' content from commit b64c2172
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ruv
167
crates/ruvllm-wasm/examples/micro_lora_example.ts
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crates/ruvllm-wasm/examples/micro_lora_example.ts
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/**
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* MicroLoRA Example - Browser-based LoRA Adaptation
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*
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* This example demonstrates how to use MicroLoRA for real-time
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* adaptation of language model outputs in the browser.
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*/
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import init, {
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MicroLoraWasm,
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MicroLoraConfigWasm,
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AdaptFeedbackWasm,
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MicroLoraStatsWasm
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} from '../pkg/ruvllm_wasm';
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async function main() {
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// Initialize WASM module
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await init();
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console.log('✅ WASM module initialized');
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// Create a rank-2 adapter for 768-dim hidden states
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const config = new MicroLoraConfigWasm();
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config.rank = 2;
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config.alpha = 4.0;
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config.inFeatures = 768;
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config.outFeatures = 768;
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console.log(`📊 Config: rank=${config.rank}, alpha=${config.alpha}`);
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console.log(`📊 Memory footprint: ${config.memoryBytes()} bytes (${(config.memoryBytes() / 1024).toFixed(2)} KB)`);
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// Create the adapter
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const lora = new MicroLoraWasm(config);
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console.log('✅ MicroLoRA adapter created');
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// Simulate some hidden state input
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const hiddenState = new Float32Array(768);
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for (let i = 0; i < 768; i++) {
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hiddenState[i] = Math.random() * 0.1 - 0.05; // Small random values
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}
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// Apply LoRA transformation
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console.log('\n🔄 Applying LoRA transformation...');
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const output = lora.apply(hiddenState);
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console.log(`✅ Output shape: ${output.length}`);
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console.log(`📈 Output magnitude: ${Math.sqrt(output.reduce((sum, x) => sum + x * x, 0) / output.length).toFixed(6)}`);
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// Simulate user feedback loop
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console.log('\n📚 Training loop:');
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const numIterations = 10;
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for (let i = 0; i < numIterations; i++) {
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// Simulate varying quality feedback
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const quality = 0.5 + 0.3 * Math.sin(i * 0.5); // Oscillates between 0.2 and 0.8
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const feedback = new AdaptFeedbackWasm(quality);
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feedback.learningRate = 0.01;
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lora.adapt(hiddenState, feedback);
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if ((i + 1) % 3 === 0) {
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// Apply updates every 3 iterations
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lora.applyUpdates(0.01);
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const stats = lora.stats();
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console.log(` Iteration ${i + 1}: quality=${quality.toFixed(3)}, avg_quality=${stats.avgQuality.toFixed(3)}, pending=${lora.pendingUpdates()}`);
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}
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}
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// Get final statistics
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console.log('\n📊 Final Statistics:');
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const stats = lora.stats();
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console.log(` Samples seen: ${stats.samplesSeen}`);
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console.log(` Average quality: ${stats.avgQuality.toFixed(3)}`);
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console.log(` Memory usage: ${stats.memoryBytes} bytes`);
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console.log(` Parameter count: ${stats.paramCount}`);
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// Test serialization
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console.log('\n💾 Serialization test:');
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const json = lora.toJson();
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console.log(` JSON size: ${json.length} bytes`);
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const restored = MicroLoraWasm.fromJson(json);
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const restoredStats = restored.stats();
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console.log(` ✅ Restored samples: ${restoredStats.samplesSeen}`);
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console.log(` ✅ Restored avg quality: ${restoredStats.avgQuality.toFixed(3)}`);
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// Apply after restoration
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const output2 = restored.apply(hiddenState);
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const diff = Math.sqrt(
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output.reduce((sum, val, i) => sum + Math.pow(val - output2[i], 2), 0) / output.length
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);
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console.log(` ✅ Output difference after serialization: ${diff.toFixed(8)} (should be ~0)`);
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// Test reset
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console.log('\n🔄 Reset test:');
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lora.reset();
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const resetStats = lora.stats();
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console.log(` Samples after reset: ${resetStats.samplesSeen}`);
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console.log(` Quality after reset: ${resetStats.avgQuality}`);
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// Browser storage integration
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console.log('\n💾 Browser storage integration:');
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try {
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localStorage.setItem('lora-state', json);
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console.log(' ✅ Saved to localStorage');
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const loaded = localStorage.getItem('lora-state');
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if (loaded) {
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const fromStorage = MicroLoraWasm.fromJson(loaded);
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console.log(' ✅ Loaded from localStorage');
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const fromStorageStats = fromStorage.stats();
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console.log(` ✅ Loaded samples: ${fromStorageStats.samplesSeen}`);
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}
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} catch (e) {
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console.log(' ⚠️ localStorage not available (running in Node?)');
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}
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console.log('\n✨ MicroLoRA example complete!');
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}
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// Real-world usage example: Online learning from user feedback
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async function onlineLearningExample() {
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await init();
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const config = new MicroLoraConfigWasm();
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config.rank = 2;
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config.inFeatures = 512;
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config.outFeatures = 512;
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const lora = new MicroLoraWasm(config);
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// Simulate a chat interface with user feedback
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console.log('\n🗨️ Online Learning Example:');
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console.log('Simulating a chat interface with user feedback...\n');
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const conversations = [
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{ input: 'helpful response', quality: 0.9 },
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{ input: 'somewhat helpful', quality: 0.6 },
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{ input: 'excellent answer', quality: 0.95 },
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{ input: 'mediocre response', quality: 0.5 },
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{ input: 'very helpful', quality: 0.85 },
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];
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for (const [idx, conv] of conversations.entries()) {
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// Generate some input based on the conversation
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const input = new Float32Array(512);
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for (let i = 0; i < 512; i++) {
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input[i] = Math.random() * 0.1;
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}
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// User provides feedback
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const feedback = new AdaptFeedbackWasm(conv.quality);
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lora.adapt(input, feedback);
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// Update every 2 conversations
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if ((idx + 1) % 2 === 0) {
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lora.applyUpdates(0.02);
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}
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console.log(` Response ${idx + 1}: "${conv.input}" (quality: ${conv.quality})`);
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}
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const finalStats = lora.stats();
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console.log(`\n 📈 Average user satisfaction: ${(finalStats.avgQuality * 100).toFixed(1)}%`);
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console.log(` 📊 Total adaptations: ${finalStats.samplesSeen}`);
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}
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// Run examples
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main().then(() => onlineLearningExample()).catch(console.error);
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