3.8B PARAMETERS,
7B PERFORMANCE

Pulling manifest... 📱 Downloading the EFFICIENCY CHAMPION (2.3GB) 🚀 [████████████████████] 100% ✅ SUCCESS! The small model revolution begins now!

import pandas as pd import numpy as np from typing import Dict, List import matplotlib.pyplot as plt class EfficiencyAnalyzer: """The David vs Goliath of data analysis""" def __init__(self, data: pd.DataFrame): self.data = data self.efficiency_metrics = {} def calculate_model_efficiency(self, model_params: int, performance_score: float) -> float: """ Calculate efficiency = performance per billion parameters Phi-3 Mini: 94.2 / 3.8 = 24.8 (CHAMPION!) Llama 2 7B: 86.1 / 7.0 = 12.3 (Half the efficiency) """ return performance_score / (model_params / 1e9) def benchmark_revolution(self) -> Dict[str, float]: """Prove small models can beat giants""" models = { 'Phi-3 Mini': {'params': 3.8e9, 'score': 94.2}, 'Llama 2 7B': {'params': 7.0e9, 'score': 86.1}, 'Mistral 7B': {'params': 7.0e9, 'score': 88.4} } efficiency_results = {} for name, specs in models.items(): efficiency_results[name] = self.calculate_model_efficiency( specs['params'], specs['score'] ) return efficiency_results def visualize_david_vs_goliath(self): """Show the efficiency revolution in charts""" results = self.benchmark_revolution() fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6)) # Efficiency comparison models = list(results.keys()) efficiency = list(results.values()) colors = ['#0078D4', '#FF6B35', '#FFA500'] # Azure blue for Phi-3 bars = ax1.bar(models, efficiency, color=colors) ax1.set_title('EFFICIENCY REVOLUTION: Performance per Parameter') ax1.set_ylabel('Efficiency Score') # Highlight the champion bars[0].set_edgecolor('gold') bars[0].set_linewidth(3) # Add efficiency annotations for i, v in enumerate(efficiency): ax1.annotate(f'{v:.1f}x', (i, v), ha='center', va='bottom', fontweight='bold', fontsize=12) # Size vs Performance scatter params = [3.8, 7.0, 7.0] performance = [94.2, 86.1, 88.4] scatter = ax2.scatter(params, performance, s=200, c=colors, alpha=0.7) ax2.set_xlabel('Model Size (Billion Parameters)') ax2.set_ylabel('Performance Score') ax2.set_title('David vs Goliath: Size vs Performance') # Annotate the revolution ax2.annotate('EFFICIENCY\nCHAMPION!', xy=(3.8, 94.2), xytext=(2.5, 96), arrowprops=dict(arrowstyle='->', color='gold', lw=2), fontsize=10, fontweight='bold', ha='center') plt.tight_layout() return plt def mobile_deployment_score(self, ram_gb: float, model_size_gb: float) -> str: """Rate mobile deployment feasibility""" if ram_gb <= 4 and model_size_gb <= 3: return "🚀 MOBILE CHAMPION - Runs on phones!" elif ram_gb <= 6 and model_size_gb <= 5: return "📱 Mobile Ready - Good for tablets" else: return "💻 Desktop Only - Too heavy for mobile" # Example usage - The Revolution in Action! if __name__ == "__main__": # Create sample data efficiency_data = pd.DataFrame({ 'model': ['Phi-3 Mini', 'Llama 2 7B', 'Mistral 7B'], 'parameters': [3.8e9, 7.0e9, 7.0e9], 'performance': [94.2, 86.1, 88.4], 'ram_required': [4, 8, 8], 'model_size': [2.3, 4.7, 4.8] }) analyzer = EfficiencyAnalyzer(efficiency_data) # Run the revolution analysis efficiency_results = analyzer.benchmark_revolution() print("🏆 EFFICIENCY REVOLUTION RESULTS:") for model, score in efficiency_results.items(): print(f" {model}: {score:.1f} points per billion parameters") # Mobile deployment analysis print("\n📱 MOBILE DEPLOYMENT ANALYSIS:") for _, row in efficiency_data.iterrows(): mobile_score = analyzer.mobile_deployment_score( row['ram_required'], row['model_size'] ) print(f" {row['model']}: {mobile_score}") # The shocking truth print("\n💥 THE SHOCKING TRUTH:") print(" Phi-3 Mini is 2X MORE EFFICIENT than 7B models!") print(" Smaller ≠ Weaker. Microsoft proved SIZE DOESN'T MATTER!") print(" The future belongs to EFFICIENT models, not GIANT ones!") # Generate the revolution visualization plot = analyzer.visualize_david_vs_goliath() print("\n📊 David vs Goliath chart generated!") print("🎯 Phi-3 Mini: The efficiency revolution is here!")