| import numpy as np | |
| from pathlib import Path | |
| from ...logger import logger | |
| from ...config import load_config | |
| from ..energy.free_energy import FreeEnergyEngine | |
| _cfg = load_config() | |
| class EvolutionaryLoRA: | |
| def __init__(self, brain): | |
| self.brain = brain | |
| self.free_energy_engine = FreeEnergyEngine() | |
| self.out_path = Path(_cfg["storage_root"]) / "storage" / "lora_delta_evo.json" | |
| def run_generation(self) -> None: | |
| # Simulated local teacher-forcing evaluation | |
| fake_logprob = -np.random.rand() | |
| self.free_energy_engine.ingest_observation(fake_logprob) | |
| if self.free_energy_engine.free_energy < 0.5: | |
| self.out_path.parent.mkdir(parents=True, exist_ok=True) | |
| self.out_path.touch() | |
| logger.info(f"LoRA improvement kept (free-energy={self.free_energy_engine.free_energy:.3f})") | |
| else: | |
| logger.info(f"LoRA discarded (free-energy={self.free_energy_engine.free_energy:.3f})") | |