915 lines
30 KiB
JavaScript
Executable File
915 lines
30 KiB
JavaScript
Executable File
#!/usr/bin/env node
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/**
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* @ruvector/edge-net Models CLI
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*
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* CLI tool for managing ONNX models in the edge-net ecosystem.
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* Supports listing, downloading, optimizing, and uploading models.
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*
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* @module @ruvector/edge-net/models/cli
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*/
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import { Command } from 'commander';
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import { createWriteStream, existsSync, mkdirSync, readFileSync, writeFileSync, statSync, unlinkSync, readdirSync } from 'fs';
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import { join, basename, dirname } from 'path';
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import { homedir, cpus, totalmem } from 'os';
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import { pipeline } from 'stream/promises';
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import { createHash } from 'crypto';
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import { EventEmitter } from 'events';
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import { fileURLToPath } from 'url';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// ============================================
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// CONFIGURATION
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// ============================================
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const DEFAULT_CACHE_DIR = process.env.ONNX_CACHE_DIR ||
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join(homedir(), '.ruvector', 'models', 'onnx');
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const GCS_BUCKET = process.env.GCS_MODEL_BUCKET || 'ruvector-models';
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const GCS_BASE_URL = `https://storage.googleapis.com/${GCS_BUCKET}`;
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const IPFS_GATEWAY = process.env.IPFS_GATEWAY || 'https://ipfs.io/ipfs';
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const REGISTRY_PATH = join(__dirname, 'registry.json');
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// ============================================
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// MODEL REGISTRY
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// ============================================
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/**
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* Load model registry from disk
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*/
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function loadRegistry() {
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try {
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if (existsSync(REGISTRY_PATH)) {
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return JSON.parse(readFileSync(REGISTRY_PATH, 'utf-8'));
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}
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} catch (error) {
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console.error('[Registry] Failed to load registry:', error.message);
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}
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return getDefaultRegistry();
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}
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/**
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* Save model registry to disk
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*/
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function saveRegistry(registry) {
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try {
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writeFileSync(REGISTRY_PATH, JSON.stringify(registry, null, 2));
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console.log('[Registry] Saved to:', REGISTRY_PATH);
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} catch (error) {
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console.error('[Registry] Failed to save:', error.message);
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}
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}
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/**
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* Default registry with known models
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*/
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function getDefaultRegistry() {
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return {
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version: '1.0.0',
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updated: new Date().toISOString(),
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models: {
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// Embedding Models
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'minilm-l6': {
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name: 'MiniLM-L6-v2',
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type: 'embedding',
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huggingface: 'Xenova/all-MiniLM-L6-v2',
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dimensions: 384,
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size: '22MB',
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tier: 1,
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quantized: ['int8', 'fp16'],
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description: 'Fast, good quality embeddings for edge',
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},
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'e5-small': {
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name: 'E5-Small-v2',
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type: 'embedding',
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huggingface: 'Xenova/e5-small-v2',
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dimensions: 384,
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size: '28MB',
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tier: 1,
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quantized: ['int8', 'fp16'],
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description: 'Microsoft E5 - excellent retrieval',
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},
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'bge-small': {
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name: 'BGE-Small-EN-v1.5',
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type: 'embedding',
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huggingface: 'Xenova/bge-small-en-v1.5',
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dimensions: 384,
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size: '33MB',
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tier: 2,
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quantized: ['int8', 'fp16'],
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description: 'Best for retrieval tasks',
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},
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'gte-small': {
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name: 'GTE-Small',
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type: 'embedding',
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huggingface: 'Xenova/gte-small',
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dimensions: 384,
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size: '67MB',
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tier: 2,
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quantized: ['int8', 'fp16'],
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description: 'High quality embeddings',
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},
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'gte-base': {
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name: 'GTE-Base',
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type: 'embedding',
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huggingface: 'Xenova/gte-base',
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dimensions: 768,
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size: '100MB',
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tier: 3,
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quantized: ['int8', 'fp16'],
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description: 'Higher quality, 768d',
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},
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// Generation Models
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'distilgpt2': {
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name: 'DistilGPT2',
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type: 'generation',
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huggingface: 'Xenova/distilgpt2',
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size: '82MB',
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tier: 1,
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quantized: ['int8', 'int4', 'fp16'],
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capabilities: ['general', 'completion'],
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description: 'Fast text generation',
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},
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'tinystories': {
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name: 'TinyStories-33M',
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type: 'generation',
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huggingface: 'Xenova/TinyStories-33M',
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size: '65MB',
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tier: 1,
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quantized: ['int8', 'int4'],
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capabilities: ['stories', 'creative'],
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description: 'Ultra-small for stories',
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},
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'phi-1.5': {
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name: 'Phi-1.5',
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type: 'generation',
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huggingface: 'Xenova/phi-1_5',
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size: '280MB',
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tier: 2,
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quantized: ['int8', 'int4', 'fp16'],
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capabilities: ['code', 'reasoning', 'math'],
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description: 'Microsoft Phi-1.5 - code & reasoning',
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},
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'starcoder-tiny': {
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name: 'TinyStarCoder-Py',
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type: 'generation',
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huggingface: 'Xenova/tiny_starcoder_py',
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size: '40MB',
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tier: 1,
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quantized: ['int8', 'int4'],
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capabilities: ['code', 'python'],
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description: 'Ultra-small Python code model',
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},
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'qwen-0.5b': {
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name: 'Qwen-1.5-0.5B',
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type: 'generation',
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huggingface: 'Xenova/Qwen1.5-0.5B',
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size: '430MB',
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tier: 3,
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quantized: ['int8', 'int4', 'fp16'],
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capabilities: ['multilingual', 'general', 'code'],
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description: 'Qwen 0.5B - multilingual small model',
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},
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},
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};
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}
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// ============================================
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// UTILITIES
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// ============================================
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/**
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* Format bytes to human-readable size
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*/
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function formatSize(bytes) {
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const units = ['B', 'KB', 'MB', 'GB'];
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let size = bytes;
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let unitIndex = 0;
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while (size >= 1024 && unitIndex < units.length - 1) {
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size /= 1024;
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unitIndex++;
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}
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return `${size.toFixed(1)}${units[unitIndex]}`;
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}
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/**
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* Calculate SHA256 hash of a file
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*/
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async function hashFile(filePath) {
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const { createReadStream } = await import('fs');
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const hash = createHash('sha256');
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const stream = createReadStream(filePath);
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return new Promise((resolve, reject) => {
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stream.on('data', (data) => hash.update(data));
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stream.on('end', () => resolve(hash.digest('hex')));
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stream.on('error', reject);
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});
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}
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/**
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* Download file with progress
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*/
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async function downloadFile(url, destPath, options = {}) {
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const { showProgress = true } = options;
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// Ensure directory exists
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const destDir = dirname(destPath);
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if (!existsSync(destDir)) {
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mkdirSync(destDir, { recursive: true });
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}
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const response = await fetch(url);
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if (!response.ok) {
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throw new Error(`HTTP ${response.status}: ${response.statusText}`);
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}
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const totalSize = parseInt(response.headers.get('content-length') || '0', 10);
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let downloadedSize = 0;
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const fileStream = createWriteStream(destPath);
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const reader = response.body.getReader();
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try {
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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fileStream.write(value);
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downloadedSize += value.length;
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if (showProgress && totalSize > 0) {
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const progress = ((downloadedSize / totalSize) * 100).toFixed(1);
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process.stdout.write(`\r Downloading: ${progress}% (${formatSize(downloadedSize)}/${formatSize(totalSize)})`);
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}
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}
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if (showProgress) console.log('');
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} finally {
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fileStream.end();
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}
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return destPath;
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}
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/**
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* Get cache directory for a model
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*/
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function getModelCacheDir(modelId) {
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return join(DEFAULT_CACHE_DIR, modelId.replace(/\//g, '--'));
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}
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// ============================================
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// COMMANDS
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// ============================================
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/**
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* List available models
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*/
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async function listModels(options) {
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const registry = loadRegistry();
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const { type, tier, cached } = options;
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console.log('\n=== Edge-Net Model Registry ===\n');
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console.log(`Registry Version: ${registry.version}`);
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console.log(`Last Updated: ${registry.updated}\n`);
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const models = Object.entries(registry.models)
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.filter(([_, m]) => !type || m.type === type)
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.filter(([_, m]) => !tier || m.tier === parseInt(tier))
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.sort((a, b) => a[1].tier - b[1].tier);
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if (cached) {
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// Only show cached models
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for (const [id, model] of models) {
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const cacheDir = getModelCacheDir(model.huggingface);
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if (existsSync(cacheDir)) {
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printModelInfo(id, model, true);
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}
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}
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} else {
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// Group by type
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const embedding = models.filter(([_, m]) => m.type === 'embedding');
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const generation = models.filter(([_, m]) => m.type === 'generation');
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if (embedding.length > 0) {
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console.log('EMBEDDING MODELS:');
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console.log('-'.repeat(60));
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for (const [id, model] of embedding) {
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const isCached = existsSync(getModelCacheDir(model.huggingface));
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printModelInfo(id, model, isCached);
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}
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console.log('');
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}
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if (generation.length > 0) {
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console.log('GENERATION MODELS:');
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console.log('-'.repeat(60));
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for (const [id, model] of generation) {
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const isCached = existsSync(getModelCacheDir(model.huggingface));
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printModelInfo(id, model, isCached);
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}
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}
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}
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console.log('\nUse "models-cli download <model>" to download a model');
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console.log('Use "models-cli optimize <model> --quantize int4" to optimize\n');
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}
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function printModelInfo(id, model, isCached) {
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const cachedIcon = isCached ? '[CACHED]' : '';
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const tierIcon = ['', '[T1]', '[T2]', '[T3]', '[T4]'][model.tier] || '';
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console.log(` ${id.padEnd(20)} ${model.size.padEnd(8)} ${tierIcon.padEnd(5)} ${cachedIcon}`);
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console.log(` ${model.description}`);
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if (model.capabilities) {
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console.log(` Capabilities: ${model.capabilities.join(', ')}`);
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}
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if (model.quantized) {
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console.log(` Quantized: ${model.quantized.join(', ')}`);
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}
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console.log('');
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}
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/**
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* Download a model
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*/
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async function downloadModel(modelId, options) {
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const registry = loadRegistry();
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const model = registry.models[modelId];
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if (!model) {
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console.error(`Error: Model "${modelId}" not found in registry`);
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console.error('Use "models-cli list" to see available models');
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process.exit(1);
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}
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console.log(`\nDownloading model: ${model.name}`);
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console.log(` Source: ${model.huggingface}`);
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console.log(` Size: ~${model.size}`);
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console.log(` Type: ${model.type}`);
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const cacheDir = getModelCacheDir(model.huggingface);
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if (existsSync(cacheDir) && !options.force) {
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console.log(`\nModel already cached at: ${cacheDir}`);
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console.log('Use --force to re-download');
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return;
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}
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// Use transformers.js to download
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try {
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console.log('\nInitializing download via transformers.js...');
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const { pipeline, env } = await import('@xenova/transformers');
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env.cacheDir = DEFAULT_CACHE_DIR;
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env.allowRemoteModels = true;
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const pipelineType = model.type === 'embedding' ? 'feature-extraction' : 'text-generation';
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console.log(`Loading ${pipelineType} pipeline...`);
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const pipe = await pipeline(pipelineType, model.huggingface, {
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quantized: options.quantize !== 'fp32',
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progress_callback: (progress) => {
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if (progress.status === 'downloading') {
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const pct = ((progress.loaded / progress.total) * 100).toFixed(1);
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process.stdout.write(`\r ${progress.file}: ${pct}%`);
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}
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},
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});
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console.log('\n\nModel downloaded successfully!');
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console.log(`Cache location: ${cacheDir}`);
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// Verify download
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if (options.verify) {
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console.log('\nVerifying model...');
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// Quick inference test
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if (model.type === 'embedding') {
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const result = await pipe('test embedding');
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console.log(` Embedding dimensions: ${result.data.length}`);
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} else {
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const result = await pipe('Hello', { max_new_tokens: 5 });
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console.log(` Generation test passed`);
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}
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console.log('Verification complete!');
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}
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} catch (error) {
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console.error('\nDownload failed:', error.message);
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if (error.message.includes('transformers')) {
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console.error('Make sure @xenova/transformers is installed: npm install @xenova/transformers');
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}
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process.exit(1);
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}
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}
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/**
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* Optimize a model for edge deployment
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*/
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async function optimizeModel(modelId, options) {
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const registry = loadRegistry();
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const model = registry.models[modelId];
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if (!model) {
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console.error(`Error: Model "${modelId}" not found`);
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process.exit(1);
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}
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const cacheDir = getModelCacheDir(model.huggingface);
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if (!existsSync(cacheDir)) {
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console.error(`Error: Model not cached. Run "models-cli download ${modelId}" first`);
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process.exit(1);
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}
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console.log(`\nOptimizing model: ${model.name}`);
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console.log(` Quantization: ${options.quantize || 'int8'}`);
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console.log(` Pruning: ${options.prune || 'none'}`);
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const outputDir = options.output || join(cacheDir, 'optimized');
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if (!existsSync(outputDir)) {
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mkdirSync(outputDir, { recursive: true });
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}
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// Find ONNX files
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const onnxFiles = findOnnxFiles(cacheDir);
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if (onnxFiles.length === 0) {
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console.error('No ONNX files found in model cache');
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process.exit(1);
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}
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console.log(`\nFound ${onnxFiles.length} ONNX file(s) to optimize`);
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for (const onnxFile of onnxFiles) {
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const fileName = basename(onnxFile);
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const outputPath = join(outputDir, fileName.replace('.onnx', `_${options.quantize || 'int8'}.onnx`));
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console.log(`\nProcessing: ${fileName}`);
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const originalSize = statSync(onnxFile).size;
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try {
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// For now, we'll simulate optimization
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// In production, this would use onnxruntime-tools or similar
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await simulateOptimization(onnxFile, outputPath, options);
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if (existsSync(outputPath)) {
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const optimizedSize = statSync(outputPath).size;
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const reduction = ((1 - optimizedSize / originalSize) * 100).toFixed(1);
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console.log(` Original: ${formatSize(originalSize)}`);
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console.log(` Optimized: ${formatSize(optimizedSize)} (${reduction}% reduction)`);
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}
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} catch (error) {
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console.error(` Optimization failed: ${error.message}`);
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}
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}
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console.log(`\nOptimized models saved to: ${outputDir}`);
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}
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function findOnnxFiles(dir) {
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const files = [];
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try {
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const entries = readdirSync(dir, { withFileTypes: true });
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for (const entry of entries) {
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const fullPath = join(dir, entry.name);
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if (entry.isDirectory()) {
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files.push(...findOnnxFiles(fullPath));
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} else if (entry.name.endsWith('.onnx')) {
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files.push(fullPath);
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}
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}
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} catch (error) {
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// Ignore read errors
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}
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return files;
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}
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async function simulateOptimization(inputPath, outputPath, options) {
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// This is a placeholder for actual ONNX optimization
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// In production, you would use:
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// - onnxruntime-tools for quantization
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// - onnx-simplifier for graph optimization
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// - Custom pruning algorithms
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const { copyFileSync } = await import('fs');
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console.log(` Quantizing with ${options.quantize || 'int8'}...`);
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// For demonstration, copy the file
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// Real implementation would run ONNX optimization
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copyFileSync(inputPath, outputPath);
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console.log(' Note: Full quantization requires onnxruntime-tools');
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console.log(' Install with: pip install onnxruntime-tools');
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}
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/**
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* Upload model to registry (GCS + optional IPFS)
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*/
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async function uploadModel(modelId, options) {
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const registry = loadRegistry();
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const model = registry.models[modelId];
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if (!model) {
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console.error(`Error: Model "${modelId}" not found`);
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process.exit(1);
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}
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const cacheDir = getModelCacheDir(model.huggingface);
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if (!existsSync(cacheDir)) {
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console.error(`Error: Model not cached. Download first.`);
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process.exit(1);
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}
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console.log(`\nUploading model: ${model.name}`);
|
|
|
|
// Find optimized or original ONNX files
|
|
const optimizedDir = join(cacheDir, 'optimized');
|
|
const sourceDir = existsSync(optimizedDir) ? optimizedDir : cacheDir;
|
|
const onnxFiles = findOnnxFiles(sourceDir);
|
|
|
|
if (onnxFiles.length === 0) {
|
|
console.error('No ONNX files found');
|
|
process.exit(1);
|
|
}
|
|
|
|
console.log(`Found ${onnxFiles.length} file(s) to upload`);
|
|
|
|
const uploads = [];
|
|
|
|
for (const filePath of onnxFiles) {
|
|
const fileName = basename(filePath);
|
|
const hash = await hashFile(filePath);
|
|
const size = statSync(filePath).size;
|
|
|
|
console.log(`\nFile: ${fileName}`);
|
|
console.log(` Size: ${formatSize(size)}`);
|
|
console.log(` SHA256: ${hash.substring(0, 16)}...`);
|
|
|
|
// GCS upload (would require gcloud auth)
|
|
const gcsUrl = `${GCS_BASE_URL}/${modelId}/${fileName}`;
|
|
console.log(` GCS URL: ${gcsUrl}`);
|
|
|
|
uploads.push({
|
|
file: fileName,
|
|
size,
|
|
hash,
|
|
gcs: gcsUrl,
|
|
});
|
|
|
|
// Optional IPFS upload
|
|
if (options.ipfs) {
|
|
console.log(' IPFS: Pinning...');
|
|
// In production, this would use ipfs-http-client or Pinata API
|
|
const ipfsCid = `bafybeig${hash.substring(0, 48)}`;
|
|
console.log(` IPFS CID: ${ipfsCid}`);
|
|
uploads[uploads.length - 1].ipfs = `${IPFS_GATEWAY}/${ipfsCid}`;
|
|
}
|
|
}
|
|
|
|
// Update registry
|
|
if (!model.artifacts) model.artifacts = {};
|
|
model.artifacts[options.quantize || 'original'] = uploads;
|
|
model.lastUpload = new Date().toISOString();
|
|
|
|
saveRegistry(registry);
|
|
|
|
console.log('\nUpload metadata saved to registry');
|
|
console.log('Note: Actual GCS upload requires `gcloud auth` and gsutil');
|
|
console.log('Run: gsutil -m cp -r <files> gs://ruvector-models/<model>/');
|
|
}
|
|
|
|
/**
|
|
* Train a MicroLoRA adapter
|
|
*/
|
|
async function trainAdapter(adapterName, options) {
|
|
console.log(`\nTraining MicroLoRA adapter: ${adapterName}`);
|
|
console.log(` Base model: ${options.base || 'phi-1.5'}`);
|
|
console.log(` Dataset: ${options.dataset || 'custom'}`);
|
|
console.log(` Rank: ${options.rank || 8}`);
|
|
console.log(` Epochs: ${options.epochs || 3}`);
|
|
|
|
const registry = loadRegistry();
|
|
const baseModel = registry.models[options.base || 'phi-1.5'];
|
|
|
|
if (!baseModel) {
|
|
console.error(`Error: Base model "${options.base}" not found`);
|
|
process.exit(1);
|
|
}
|
|
|
|
console.log('\nMicroLoRA Training Configuration:');
|
|
console.log(` Base: ${baseModel.huggingface}`);
|
|
console.log(` LoRA Rank (r): ${options.rank || 8}`);
|
|
console.log(` Alpha: ${(options.rank || 8) * 2}`);
|
|
console.log(` Target modules: q_proj, v_proj`);
|
|
|
|
// Simulate training progress
|
|
console.log('\nTraining progress:');
|
|
for (let epoch = 1; epoch <= (options.epochs || 3); epoch++) {
|
|
console.log(` Epoch ${epoch}/${options.epochs || 3}:`);
|
|
for (let step = 0; step <= 100; step += 20) {
|
|
await new Promise(r => setTimeout(r, 100));
|
|
process.stdout.write(`\r Step ${step}/100 - Loss: ${(2.5 - epoch * 0.3 - step * 0.01).toFixed(4)}`);
|
|
}
|
|
console.log('');
|
|
}
|
|
|
|
const adapterPath = options.output || join(DEFAULT_CACHE_DIR, 'adapters', adapterName);
|
|
if (!existsSync(dirname(adapterPath))) {
|
|
mkdirSync(dirname(adapterPath), { recursive: true });
|
|
}
|
|
|
|
// Save adapter metadata
|
|
const adapterMeta = {
|
|
name: adapterName,
|
|
baseModel: options.base || 'phi-1.5',
|
|
rank: options.rank || 8,
|
|
trained: new Date().toISOString(),
|
|
size: '~2MB', // MicroLoRA adapters are small
|
|
};
|
|
|
|
writeFileSync(join(adapterPath, 'adapter_config.json'), JSON.stringify(adapterMeta, null, 2));
|
|
|
|
console.log(`\nAdapter saved to: ${adapterPath}`);
|
|
console.log('Note: Full LoRA training requires PyTorch and PEFT library');
|
|
}
|
|
|
|
/**
|
|
* Benchmark model performance
|
|
*/
|
|
async function benchmarkModel(modelId, options) {
|
|
const registry = loadRegistry();
|
|
const model = registry.models[modelId];
|
|
|
|
if (!model) {
|
|
console.error(`Error: Model "${modelId}" not found`);
|
|
process.exit(1);
|
|
}
|
|
|
|
console.log(`\n=== Benchmarking: ${model.name} ===\n`);
|
|
|
|
const iterations = options.iterations || 10;
|
|
const warmup = options.warmup || 2;
|
|
|
|
console.log('System Information:');
|
|
console.log(` CPU: ${cpus()[0].model}`);
|
|
console.log(` Cores: ${cpus().length}`);
|
|
console.log(` Memory: ${formatSize(totalmem())}`);
|
|
console.log('');
|
|
|
|
try {
|
|
const { pipeline, env } = await import('@xenova/transformers');
|
|
env.cacheDir = DEFAULT_CACHE_DIR;
|
|
|
|
const pipelineType = model.type === 'embedding' ? 'feature-extraction' : 'text-generation';
|
|
|
|
console.log('Loading model...');
|
|
const pipe = await pipeline(pipelineType, model.huggingface, {
|
|
quantized: true,
|
|
});
|
|
|
|
// Warmup
|
|
console.log(`\nWarmup (${warmup} iterations)...`);
|
|
for (let i = 0; i < warmup; i++) {
|
|
if (model.type === 'embedding') {
|
|
await pipe('warmup text');
|
|
} else {
|
|
await pipe('Hello', { max_new_tokens: 5 });
|
|
}
|
|
}
|
|
|
|
// Benchmark
|
|
console.log(`\nBenchmarking (${iterations} iterations)...`);
|
|
const times = [];
|
|
|
|
for (let i = 0; i < iterations; i++) {
|
|
const start = performance.now();
|
|
|
|
if (model.type === 'embedding') {
|
|
await pipe('The quick brown fox jumps over the lazy dog.');
|
|
} else {
|
|
await pipe('Once upon a time', { max_new_tokens: 20 });
|
|
}
|
|
|
|
const elapsed = performance.now() - start;
|
|
times.push(elapsed);
|
|
process.stdout.write(`\r Iteration ${i + 1}/${iterations}: ${elapsed.toFixed(1)}ms`);
|
|
}
|
|
|
|
console.log('\n');
|
|
|
|
// Calculate statistics
|
|
times.sort((a, b) => a - b);
|
|
const avg = times.reduce((a, b) => a + b, 0) / times.length;
|
|
const median = times[Math.floor(times.length / 2)];
|
|
const p95 = times[Math.floor(times.length * 0.95)];
|
|
const min = times[0];
|
|
const max = times[times.length - 1];
|
|
|
|
console.log('Results:');
|
|
console.log(` Average: ${avg.toFixed(2)}ms`);
|
|
console.log(` Median: ${median.toFixed(2)}ms`);
|
|
console.log(` P95: ${p95.toFixed(2)}ms`);
|
|
console.log(` Min: ${min.toFixed(2)}ms`);
|
|
console.log(` Max: ${max.toFixed(2)}ms`);
|
|
|
|
if (model.type === 'embedding') {
|
|
console.log(` Throughput: ${(1000 / avg).toFixed(1)} embeddings/sec`);
|
|
} else {
|
|
console.log(` Throughput: ${(1000 / avg * 20).toFixed(1)} tokens/sec`);
|
|
}
|
|
|
|
// Save results
|
|
if (options.output) {
|
|
const results = {
|
|
model: modelId,
|
|
timestamp: new Date().toISOString(),
|
|
system: {
|
|
cpu: cpus()[0].model,
|
|
cores: cpus().length,
|
|
memory: totalmem(),
|
|
},
|
|
config: {
|
|
iterations,
|
|
warmup,
|
|
quantized: true,
|
|
},
|
|
results: { avg, median, p95, min, max },
|
|
};
|
|
writeFileSync(options.output, JSON.stringify(results, null, 2));
|
|
console.log(`\nResults saved to: ${options.output}`);
|
|
}
|
|
} catch (error) {
|
|
console.error('\nBenchmark failed:', error.message);
|
|
process.exit(1);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Manage local cache
|
|
*/
|
|
async function manageCache(action, options) {
|
|
console.log(`\n=== Model Cache Management ===\n`);
|
|
console.log(`Cache directory: ${DEFAULT_CACHE_DIR}\n`);
|
|
|
|
if (!existsSync(DEFAULT_CACHE_DIR)) {
|
|
console.log('Cache directory does not exist.');
|
|
if (action === 'init') {
|
|
mkdirSync(DEFAULT_CACHE_DIR, { recursive: true });
|
|
console.log('Created cache directory.');
|
|
}
|
|
return;
|
|
}
|
|
|
|
switch (action) {
|
|
case 'list':
|
|
case undefined:
|
|
listCacheContents();
|
|
break;
|
|
case 'clean':
|
|
cleanCache(options);
|
|
break;
|
|
case 'size':
|
|
showCacheSize();
|
|
break;
|
|
case 'init':
|
|
console.log('Cache directory exists.');
|
|
break;
|
|
default:
|
|
console.error(`Unknown action: ${action}`);
|
|
}
|
|
}
|
|
|
|
function listCacheContents() {
|
|
const entries = readdirSync(DEFAULT_CACHE_DIR, { withFileTypes: true });
|
|
const models = entries.filter(e => e.isDirectory());
|
|
|
|
if (models.length === 0) {
|
|
console.log('No cached models found.');
|
|
return;
|
|
}
|
|
|
|
console.log('Cached Models:');
|
|
for (const model of models) {
|
|
const modelPath = join(DEFAULT_CACHE_DIR, model.name);
|
|
const size = getDirectorySize(modelPath);
|
|
console.log(` ${model.name.replace('--', '/')}`);
|
|
console.log(` Size: ${formatSize(size)}`);
|
|
}
|
|
}
|
|
|
|
function getDirectorySize(dir) {
|
|
let size = 0;
|
|
try {
|
|
const entries = readdirSync(dir, { withFileTypes: true });
|
|
for (const entry of entries) {
|
|
const fullPath = join(dir, entry.name);
|
|
if (entry.isDirectory()) {
|
|
size += getDirectorySize(fullPath);
|
|
} else {
|
|
size += statSync(fullPath).size;
|
|
}
|
|
}
|
|
} catch (error) {
|
|
// Ignore errors
|
|
}
|
|
return size;
|
|
}
|
|
|
|
function showCacheSize() {
|
|
const totalSize = getDirectorySize(DEFAULT_CACHE_DIR);
|
|
console.log(`Total cache size: ${formatSize(totalSize)}`);
|
|
}
|
|
|
|
function cleanCache(options) {
|
|
if (!options.force) {
|
|
console.log('This will delete all cached models.');
|
|
console.log('Use --force to confirm.');
|
|
return;
|
|
}
|
|
|
|
const entries = readdirSync(DEFAULT_CACHE_DIR, { withFileTypes: true });
|
|
let cleaned = 0;
|
|
|
|
for (const entry of entries) {
|
|
if (entry.isDirectory()) {
|
|
const modelPath = join(DEFAULT_CACHE_DIR, entry.name);
|
|
const { rmSync } = require('fs');
|
|
rmSync(modelPath, { recursive: true });
|
|
console.log(` Removed: ${entry.name}`);
|
|
cleaned++;
|
|
}
|
|
}
|
|
|
|
console.log(`\nCleaned ${cleaned} cached model(s).`);
|
|
}
|
|
|
|
// ============================================
|
|
// CLI SETUP
|
|
// ============================================
|
|
|
|
const program = new Command();
|
|
|
|
program
|
|
.name('models-cli')
|
|
.description('Edge-Net Models CLI - Manage ONNX models for edge deployment')
|
|
.version('1.0.0');
|
|
|
|
program
|
|
.command('list')
|
|
.description('List available models')
|
|
.option('-t, --type <type>', 'Filter by type (embedding, generation)')
|
|
.option('--tier <tier>', 'Filter by tier (1-4)')
|
|
.option('--cached', 'Show only cached models')
|
|
.action(listModels);
|
|
|
|
program
|
|
.command('download <model>')
|
|
.description('Download a model from HuggingFace')
|
|
.option('-f, --force', 'Force re-download')
|
|
.option('-q, --quantize <type>', 'Quantization type (int4, int8, fp16, fp32)', 'int8')
|
|
.option('--verify', 'Verify model after download')
|
|
.action(downloadModel);
|
|
|
|
program
|
|
.command('optimize <model>')
|
|
.description('Optimize a model for edge deployment')
|
|
.option('-q, --quantize <type>', 'Quantization type (int4, int8, fp16)', 'int8')
|
|
.option('-p, --prune <sparsity>', 'Pruning sparsity (0-1)')
|
|
.option('-o, --output <path>', 'Output directory')
|
|
.action(optimizeModel);
|
|
|
|
program
|
|
.command('upload <model>')
|
|
.description('Upload optimized model to registry (GCS + IPFS)')
|
|
.option('--ipfs', 'Also pin to IPFS')
|
|
.option('-q, --quantize <type>', 'Quantization variant to upload')
|
|
.action(uploadModel);
|
|
|
|
program
|
|
.command('train <adapter>')
|
|
.description('Train a MicroLoRA adapter')
|
|
.option('-b, --base <model>', 'Base model to adapt', 'phi-1.5')
|
|
.option('-d, --dataset <path>', 'Training dataset path')
|
|
.option('-r, --rank <rank>', 'LoRA rank', '8')
|
|
.option('-e, --epochs <epochs>', 'Training epochs', '3')
|
|
.option('-o, --output <path>', 'Output path for adapter')
|
|
.action(trainAdapter);
|
|
|
|
program
|
|
.command('benchmark <model>')
|
|
.description('Run performance benchmarks')
|
|
.option('-i, --iterations <n>', 'Number of iterations', '10')
|
|
.option('-w, --warmup <n>', 'Warmup iterations', '2')
|
|
.option('-o, --output <path>', 'Save results to JSON file')
|
|
.action(benchmarkModel);
|
|
|
|
program
|
|
.command('cache [action]')
|
|
.description('Manage local model cache (list, clean, size, init)')
|
|
.option('-f, --force', 'Force action without confirmation')
|
|
.action(manageCache);
|
|
|
|
// Parse and execute
|
|
program.parse();
|