examples/control-nested.okt

# okto_version: "1.2"
PROJECT "ControlNestedExample"
DESCRIPTION "Demonstrates nested CONTROL blocks with advanced decision-making"

ENV {
  accelerator: "gpu"
  min_memory: "8GB"
  precision: "fp16"
}

DATASET {
  train: "examples/datasets/demo_train.jsonl"
  validation: "examples/datasets/demo_train.jsonl"
  format: "jsonl"
  type: "chat"
}

MODEL {
  name: "nested-control-model"
  base: "oktoseek/base-mini"
  device: "cuda"
}

TRAIN {
  epochs: 10
  batch_size: 32
  learning_rate: 0.0001
  optimizer: "adamw"
  device: "cuda"
}

CONTROL {
  on_step_end {
    LOG loss
  }
  
  on_epoch_end {
    IF loss > 2.0 {
      SET LR = 0.00005
      LOG "High loss detected, reducing learning rate"
      
      WHEN gpu_usage > 90% {
        SET batch_size = 16
        LOG "Reducing batch size due to GPU pressure"
      }
      
      IF val_loss > 3.0 {
        STOP_TRAINING
      }
    }
    
    IF accuracy > 0.9 {
      SAVE "best_model"
      LOG "High accuracy reached"
    }
    
    EVERY 2 epochs {
      SAVE "checkpoint_epoch_{epoch}"
    }
  }
  
  validate_every: 200
  
  IF epoch == 1 {
    LOG "Warmup stage"
  }
  
  IF epoch > 5 AND accuracy < 0.6 {
    SET LR = 0.00001
    LOG "Model is stagnated, reducing learning rate"
  }
  
  IF epoch > 10 AND loss > 1.8 {
    STOP_TRAINING
  }
  
  WHEN gpu_memory < 12GB {
    SET batch_size = 16
  }
  
  EVERY 500 steps {
    SAVE checkpoint
  }
}

MONITOR {
  metrics: ["loss", "val_loss", "accuracy", "gpu_usage", "ram_usage"]
  notify_if {
    loss > 2.0
    gpu_usage > 90%
  }
  log_to: "logs/training.log"
}

EXPORT {
  format: ["okm"]
  path: "export/"
}