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.
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🔧 What Are AI Model Parameters? — Conceptual Process — Part 5
100
print(f"{component:<20}: {count:>12,} ({percentage:>5.1f}%)")
print("=" * 50)
print(f"{'Total Parameters':<20}: {params['total_parameters']:>12,}")
# Compare to well-known models
self.compare_to_known_models(params['total_parameters'])
def compare_to_known_models(self, calculated_params):
"""Compare calculated parameters to known model sizes"""
known_models = {
'Small Language Model': 117_000_000,
# ~117M (like DistilBERT)
'Base Language Model': 340_000_000,
# ~340M (like BERT-Base)
'Large Language Model': 1_500_000_000,
# ~1.5B
'Very Large Model': 6_000_000_000,
# ~6B
'Massive Model': 175_000_000_000,
# ~175B (like GPT-3)
'Ultra-Large Model': 540_000_000_000
# ~540B
}
print(f"\nComparison to Known Models:")
print("-" * 40)
closest_model = min(known_models.items(),
key=lambda x: abs(x[1] - calculated_params))
print(f"Your model ({calculated_params:,} params) is closest to:")
print(f"{closest_model[0]}: {closest_model[1]:,} parameters")
# Example: Analyze a medium-sized transformer
analyzer = TransformerParameterAnalyzer(
vocab_size=50000,
hidden_size=1024,
num_layers=24,
num_heads=16
)
analyzer.show_parameter_breakdown()