AI Model Parameters Guide

=12, num_heads=12): self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_layers = num_layers self.num_heads = num_heads self.head_size = hidden_size // num_heads def calculate_transformer_parameters(self): """Calculate parameters in each component of a transformer""" # Token embeddings token_embedding_params = self.vocab_size * self.hidden_size # Position embeddings (assuming max sequence length of 2048) position_embedding_params = 2048 * self.hidden_size # Each transformer layer has several components single_layer_params = self.calculate_single_layer_params() total_layer_params = single_layer_params * self.num_layers # Output layer (language modeling head) output_layer_params = self.hidden_size * self.vocab_size return { 'token_embeddings': token_embedding_params, 'position_embeddings': position_embedding_params, 'transformer_layers': total_layer_params, 'output_layer': output_layer_params, 'total_parameters': (token_embedding_params + position_embedding_params + total_layer_params + output_layer_params) } def calculate_single_layer_params(self): """Calculate parameters in a single transformer layer""" # Multi-head attention # Query, Key, Value projections: 3 * hidden_size * hidden_size attention_qkv_params = 3 * self.hidden_size * self.hidden_size # Output projection: hidden_size * hidden_size attention_output_params = self.hidden_size * self.hidden_size # Layer normalization parameters (scale and bias) attention_layernorm_params = 2 * self.hidden_size # Feed-forward network (typically 4x hidden size) ffn_hidden_size = 4 * self.hidden_size ffn_params = (self.hidden_size * ffn_hidden_size + # First linear layer ffn_hidden_size + # First bias ffn_hidden_size * self.hidden_size + # Second linear layer self.hidden_size) # Second bias # Second layer normalization ffn_layernorm_params = 2 * self.hidden_size return (attention_qkv_params + attention_output_params + attention_layernorm_params + ffn_params + ffn_layernorm_params) def show_parameter_breakdown(self): """Display detailed parameter breakdown""" params = self.calculate_transformer_parameters() print(f"Transformer Model Parameter Breakdown:") print(f"Architecture: {self.num_layers} layers, {self.hidden_size} hidden size") print("=" * 50) for component, count in params.items(): if component != 'total_parameters': percentage = (count / params['total_parameters']) *