Explains model parameters, tokens, and scaling laws in plain language. Learn how size, data quality, and training choices affect cost and accuracy—and how to pick the right model for a task.
🔧 What Are AI Model Parameters? — Conceptual Process — Part 4 curacy - prev_accuracy
efficiency = (accuracy_diff / param_diff) * 1_000_000_000
# per billion params
else:
efficiency = 0
params_str = f"{params/1_000_000:.0f}M" if params >= 1_000_000 else f"{params/1_000:.0f}K"
print(f"{params_str:<12} {accuracy:<10.3f} {time_hours:<15} {efficiency:.2e}")
def calculate_diminishing_returns(self):
"""Show how returns diminish as parameters increase"""
improvements = []
for i in range(1, len(self.performance_data)):
current = self.performance_data[i]
previous = self.performance_data[i-1]
param_ratio = current['params'] / previous['params']
accuracy_improvement = current['accuracy'] - previous['accuracy']
improvements.append({
'param_increase': f"{param_ratio:.1f}x",
'accuracy_gain': f"{accuracy_improvement:.3f}",
'efficiency': accuracy_improvement / (param_ratio - 1)
})
print("\nDiminishing Returns Analysis:")
print("-" * 50)
for improvement in improvements:
print(f"Parameter increase: {improvement['param_increase']}")
print(f"Accuracy gain: {improvement['accuracy_gain']}")
print(f"Efficiency ratio: {improvement['efficiency']:.4f}")
print("-" * 30)
analyzer = ParameterPerformanceAnalyzer()
analyzer.analyze_scaling_laws()
analyzer.calculate_diminishing_returns()
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