init_model.py

import os
from transformers import BertTokenizer
from evo_model import EvoTransformerConfig, EvoTransformerForClassification

def initialize_and_save_model():
    # Step 1: Initialize configuration with architecture info
    config = EvoTransformerConfig(
        hidden_size=384,
        num_layers=6,
        num_labels=2,
        num_heads=6,
        ffn_dim=1024,
        use_memory=False
    )

    # Step 2: Initialize model
    model = EvoTransformerForClassification(config)

    # Step 3: Save model
    os.makedirs("trained_model", exist_ok=True)
    model.save_pretrained("trained_model")

    # Step 4: Save tokenizer (BERT-based)
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    tokenizer.save_pretrained("trained_model")

    print("✅ EvoTransformer and tokenizer initialized and saved to 'trained_model/'")

def load_model():
    # Load model from saved directory
    model = EvoTransformerForClassification.from_pretrained("trained_model")
    return model

# Allow direct run
if __name__ == "__main__":
    initialize_and_save_model()