🔧 LLM Checker - Technical Documentation
February 17, 2026 · View on GitHub
Detailed technical documentation for developers, contributors, and advanced users.
🏗️ Architecture Overview
System Design
┌─────────────────────────────────────────────────┐
│ CLI Interface │
│ (enhanced_cli.js) │
└─────────────────────┬───────────────────────────┘
│
┌─────────────────────▼───────────────────────────┐
│ Main LLM Checker │
│ (src/index.js) │
└─────────┬───────────────────────────┬───────────┘
│ │
┌─────────▼─────────┐ ┌─────────▼─────────┐
│ Hardware │ │ AI Model │
│ Detection │ │ Selector │
│ │ │ │
│ • CPU/RAM/GPU │ │ • Mathematical │
│ • Tier Analysis │ │ Algorithm │
│ • Performance │ │ • 5-Factor │
│ Calculation │ │ Scoring │
└─────────┬─────────┘ └─────────┬─────────┘
│ │
└─────────┬───────────────┬─┘
│ │
┌─────────▼─────────┐ ┌─▼─────────────┐
│ Model Database │ │ Ollama │
│ │ │ Integration │
│ • 15+ Models │ │ │
│ • Characteristics │ │ • Client │
│ • Scoring Data │ │ • Scraper │
└───────────────────┘ └───────────────┘
🧠 Intelligent Selector Algorithm
Core Algorithm Flow
function selectBestModel(hardware, availableModels, userPreference) {
// 1. Hardware Analysis
const analysis = analyzeHardware(hardware);
// 2. Model Scoring
const scores = availableModels.map(model => ({
model,
score: calculateModelScore(model, hardware, analysis, userPreference)
}));
// 3. Ranking & Selection
const ranked = scores.sort((a, b) => b.score - a.score);
// 4. Confidence Calculation
const confidence = calculateConfidence(ranked[0].score, analysis);
// 5. Reasoning Generation
const reasoning = generateReasoning(ranked[0].model, hardware, analysis);
return {
bestModel: ranked[0].model,
confidence,
reasoning,
allPredictions: ranked
};
}
5-Factor Scoring System
1. Memory Efficiency Score (35% weight)
calculateMemoryEfficiencyScore(model, analysis) {
const requiredMemory = model.memory_requirement;
const availableMemory = analysis.available_memory.total;
if (requiredMemory > availableMemory) {
return 0.1; // Severe penalty for overflow
}
const utilizationRatio = requiredMemory / availableMemory;
// Optimal utilization curve
if (utilizationRatio <= 0.3) return 0.6; // Under-utilizing
if (utilizationRatio <= 0.5) return 0.8; // Good utilization
if (utilizationRatio <= 0.7) return 1.0; // Optimal utilization
if (utilizationRatio <= 0.9) return 0.9; // High utilization
return 0.7; // Very tight fit
}
2. Performance Match Score (25% weight)
calculatePerformanceMatchScore(model, analysis) {
// CPU capability vs model requirements
const cpuMatch = Math.min(1.0,
analysis.cpu_tier.multiplier * model.cpu_intensive
);
// Memory tier bonus
const memoryMatch = analysis.memory_tier.multiplier / 2.0;
// GPU acceleration bonus
const gpuBoost = analysis.gpu_tier.multiplier > 1.0 ? 0.2 : 0;
return Math.min(1.0, cpuMatch + memoryMatch + gpuBoost);
}
3. Task Optimization Score (20% weight)
calculateTaskOptimizationScore(model, userPreference) {
if (model.specialization.includes(userPreference)) {
return 1.0; // Perfect match
}
// Check for compatible tasks
const compatibleTasks = {
'coding': ['programming', 'debugging'],
'general': ['chat', 'reasoning'],
'chat': ['general', 'reasoning']
};
const compatible = compatibleTasks[userPreference] || [];
const hasCompatible = compatible.some(task =>
model.specialization.includes(task)
);
return hasCompatible ? 0.7 : 0.5;
}
4. Quality/Popularity Score (15% weight)
calculatePopularityQualityScore(model) {
const qualityNormalized = model.quality_score / 10.0;
const popularityNormalized = model.popularity_score / 10.0;
// Quality is more important than popularity
return (qualityNormalized * 0.6) + (popularityNormalized * 0.4);
}
5. Resource Efficiency Score (5% weight)
calculateResourceEfficiencyScore(model, analysis) {
const efficiencyFactors = {
inference_speed: {
'very_fast': 1.0,
'fast': 0.9,
'medium': 0.8,
'slow': 0.6,
'very_slow': 0.4
}
};
const speedScore = efficiencyFactors.inference_speed[model.inference_speed];
const thermalScore = analysis.thermal_constraint;
return (speedScore * 0.7) + (thermalScore * 0.3);
}
💾 Model Database Schema
Model Entry Structure
{
// Basic Information
name: "Human-readable name",
size_gb: 3.8, // Disk space requirement
parameters: 7, // Model parameters in billions
memory_requirement: 8, // RAM needed in GB
// Performance Requirements
cpu_cores_min: 4, // Minimum CPU cores
cpu_intensive: 0.7, // CPU usage factor (0-1)
// Specialization & Quality
specialization: ['general', 'chat', 'reasoning'],
quality_score: 9.2, // Community quality rating (0-10)
popularity_score: 9.8, // Adoption/usage score (0-10)
// Technical Specifications
context_length: 4096, // Maximum context tokens
quantization: 'Q4_0', // Quantization method
inference_speed: 'medium' // Speed category
}
Hardware Tier Definitions
const hardwareTiers = {
memory: {
ultra_low: { min: 0, max: 4, multiplier: 0.3 },
low: { min: 4, max: 8, multiplier: 0.6 },
medium: { min: 8, max: 16, multiplier: 1.0 },
high: { min: 16, max: 32, multiplier: 1.4 },
very_high: { min: 32, max: 64, multiplier: 1.8 },
extreme: { min: 64, max: 128, multiplier: 2.2 }
},
cpu: {
ultra_low: { min: 0, max: 2, multiplier: 0.4 },
low: { min: 2, max: 4, multiplier: 0.7 },
medium: { min: 4, max: 8, multiplier: 1.0 },
high: { min: 8, max: 16, multiplier: 1.3 },
very_high: { min: 16, max: 32, multiplier: 1.6 },
extreme: { min: 32, max: 64, multiplier: 2.0 }
}
};
🔍 Hardware Detection System
Multi-Platform Detection
async getSystemSpecs() {
// Try systeminformation first (most detailed)
if (si) {
try {
const [cpu, mem, graphics, osInfo] = await Promise.all([
si.cpu(), si.mem(), si.graphics(), si.osInfo()
]);
return this.processDetailedSpecs(cpu, mem, graphics, osInfo);
} catch (error) {
console.warn('Detailed detection failed, using fallback');
}
}
// Fallback to Node.js built-ins
const os = require('os');
return {
cpu_cores: os.cpus().length,
cpu_freq_max: 3.0, // Estimated
total_ram_gb: os.totalmem() / (1024 ** 3),
gpu_model_normalized: os.platform() === 'darwin' ? 'apple_silicon' : 'cpu_only',
gpu_vram_gb: os.platform() === 'darwin' ? os.totalmem() / (1024 ** 3) * 0.75 : 0,
platform: os.platform()
};
}
Apple Silicon Optimization
handleAppleSilicon(totalRAM) {
// Apple Silicon uses unified memory architecture
const unifiedMemory = totalRAM;
// Estimate GPU-accessible memory (60% of total)
const gpuMemory = unifiedMemory * 0.6;
// System overhead (30% reserved for macOS)
const availableMemory = unifiedMemory * 0.7;
return {
total_ram_gb: unifiedMemory,
gpu_vram_gb: gpuMemory,
available_memory: availableMemory,
gpu_model_normalized: 'apple_silicon',
architecture_boost: 1.15 // 15% performance bonus
};
}
🚀 Performance Optimizations
Caching Strategy
class ModelSelector {
constructor() {
this.hardwareCache = null;
this.hardwareCacheExpiry = 5 * 60 * 1000; // 5 minutes
this.modelCache = new Map();
}
async getHardwareSpecs() {
if (this.hardwareCache &&
Date.now() - this.hardwareCache.timestamp < this.hardwareCacheExpiry) {
return this.hardwareCache.data;
}
const specs = await this.detectHardware();
this.hardwareCache = {
data: specs,
timestamp: Date.now()
};
return specs;
}
}
Lazy Loading
class IntelligentSelector {
constructor() {
this._modelDatabase = null;
this._hardwareTiers = null;
}
get modelDatabase() {
if (!this._modelDatabase) {
this._modelDatabase = this.initializeModelDatabase();
}
return this._modelDatabase;
}
}
🔌 API Integration Points
Adding New Models
// 1. Add to model database
const newModel = {
'new-model:7b': {
name: 'New Model 7B',
size_gb: 4.0,
parameters: 7,
memory_requirement: 8,
cpu_cores_min: 4,
cpu_intensive: 0.7,
specialization: ['general', 'chat'],
quality_score: 8.5,
popularity_score: 7.0,
context_length: 4096,
quantization: 'Q4_0',
inference_speed: 'medium'
}
};
// 2. The system automatically includes it in selection
Custom Hardware Profiles
// Override hardware detection for testing
const customHardware = {
cpu_cores: 16,
cpu_freq_max: 3.8,
total_ram_gb: 32,
gpu_model_normalized: 'rtx_4090',
gpu_vram_gb: 24
};
const result = await aiSelector.selectBestModel(
candidateModels,
customHardware,
'coding'
);
🧪 Testing Framework
Unit Tests Structure
// Test hardware analysis
describe('Hardware Analysis', () => {
test('should classify high-end system correctly', () => {
const hardware = { cpu_cores: 12, total_ram_gb: 24 };
const analysis = selector.analyzeHardware(hardware);
expect(analysis.overall_tier).toBe('high');
});
test('should handle Apple Silicon correctly', () => {
const hardware = {
gpu_model_normalized: 'apple_silicon',
total_ram_gb: 16
};
const analysis = selector.analyzeHardware(hardware);
expect(analysis.gpu_tier.multiplier).toBeGreaterThan(1.0);
});
});
// Test scoring algorithm
describe('Model Scoring', () => {
test('should penalize oversized models', () => {
const smallHardware = { total_ram_gb: 8 };
const largeModel = { memory_requirement: 16 };
const score = selector.calculateModelScore(largeModel, smallHardware);
expect(score).toBeLessThan(50);
});
});
Integration Tests
// Test full selection pipeline
describe('Model Selection Integration', () => {
test('should select appropriate model for given hardware', async () => {
const hardware = { cpu_cores: 8, total_ram_gb: 16 };
const models = ['llama2:7b', 'llama2:13b', 'phi3:mini'];
const result = await selector.selectBestModel(models, hardware);
expect(result.bestModel).toBe('llama2:7b');
expect(result.confidence).toBeGreaterThan(0.8);
expect(result.method).toBe('intelligent_mathematical');
});
});
📊 Monitoring & Analytics
Performance Metrics
class PerformanceMonitor {
static trackSelection(hardware, selectedModel, confidence) {
const metrics = {
timestamp: Date.now(),
hardware_tier: this.getHardwareTier(hardware),
model_size: this.getModelSize(selectedModel),
confidence: confidence,
selection_time: process.hrtime.bigint() - startTime
};
// Log or send to analytics service
console.log('Selection metrics:', metrics);
}
}
Error Tracking
class ErrorTracker {
static logSelectionError(error, context) {
const errorReport = {
error: error.message,
stack: error.stack,
context: {
hardware: context.hardware,
models: context.models,
timestamp: new Date().toISOString()
}
};
// Send to error tracking service
console.error('Selection error:', errorReport);
}
}
🔧 Configuration Options
Environment Variables
# Enable debug mode
DEBUG=1 llm-checker ai-check
# Disable hardware caching
NO_CACHE=1 llm-checker ai-check
# Custom model database path
MODEL_DB_PATH=/path/to/custom/models.json llm-checker ai-check
Configuration File
// ~/.llm-checker/config.json
{
"selector": {
"weights": {
"memory_efficiency": 0.35,
"performance_match": 0.25,
"task_optimization": 0.20,
"popularity_quality": 0.15,
"resource_efficiency": 0.05
},
"cache_ttl": 300000,
"fallback_enabled": true
},
"hardware": {
"force_detection": false,
"custom_multipliers": {
"apple_silicon": 1.15,
"rtx_40_series": 1.3
}
}
}
🚀 Deployment & Distribution
Build Process
# Development
npm run dev
# Testing
npm test
# Build for distribution
npm run build
# Package for npm
npm pack
CI/CD Pipeline
# .github/workflows/test.yml
name: Test
on: [push, pull_request]
jobs:
test:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
node-version: [16, 18, 20]
steps:
- uses: actions/checkout@v3
- uses: actions/setup-node@v3
with:
node-version: ${{ matrix.node-version }}
- run: npm ci
- run: npm test
🔍 Debugging Guide
Common Issues
-
Hardware Detection Fails
DEBUG=1 llm-checker ai-check # Check output for detection errors -
Low Confidence Scores
// Check if models are too large for hardware const analysis = selector.analyzeHardware(hardware); console.log('Available memory:', analysis.available_memory.total); -
Unexpected Model Selection
// Enable verbose scoring const result = selector.selectBestModel(models, hardware, 'general'); console.log('All scores:', result.allPredictions);
Debug Mode Output
DEBUG=1 llm-checker ai-check --models llama2:7b mistral:7b
# Outputs:
# [DEBUG] Hardware analysis: { cpu_cores: 12, total_ram_gb: 24 }
# [DEBUG] Model scores: [{ model: 'llama2:7b', score: 99 }, ...]
# [DEBUG] Selection reasoning: Optimal memory utilization...
This technical documentation covers the core implementation details. For usage examples, see Usage Guide.