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# okto_version: "1.2"
# Teste 3: T5 com CONTROL - Decisões Automáticas
# Modelo: google/t5-small
# Objetivo: Testar bloco CONTROL com lógica condicional
PROJECT "test_t5_control"
DESCRIPTION "Teste T5 com bloco CONTROL - decisões automáticas durante treino"
ENV {
accelerator: "gpu"
min_memory: "4GB"
precision: "fp16"
backend: "oktoseek"
install_missing: true
}
DATASET {
train: "dataset/train.jsonl"
validation: "dataset/val.jsonl"
}
MODEL {
base: "t5-small"
device: "auto"
}
TRAIN {
epochs: 5
batch_size: 8
learning_rate: 0.0001
device: "auto"
}
CONTROL {
on_step_end {
LOG loss
}
on_epoch_end {
SAVE model
LOG "Epoch completed"
}
validate_every: 100
IF loss > 2.0 {
SET LR = 0.00005
LOG "High loss detected - reducing learning rate"
}
IF val_loss > 2.5 {
STOP_TRAINING
LOG "Validation loss too high - stopping training"
}
IF accuracy < 0.4 {
DECREASE LR BY 0.5
LOG "Low accuracy - decreasing learning rate by 50%"
}
WHEN gpu_memory < 8GB {
SET batch_size = 4
LOG "Low GPU memory - reducing batch size"
}
EVERY 500 steps {
SAVE checkpoint
LOG "Checkpoint saved"
}
}
EXPORT {
format: ["okm"]
path: "export/"
}
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