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from evo_model import EvoTransformerConfig, EvoTransformerForClassification
import os
def initialize_and_save_model(save_path="trained_model"):
config = EvoTransformerConfig()
model = EvoTransformerForClassification(config)
os.makedirs(save_path, exist_ok=True)
model.save_pretrained(save_path)
def retrain_model():
# Dummy retrain function – replace with actual training logic later
initialize_and_save_model("trained_model")
return "✅ Retrained and saved new model."
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