| # 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/" |
| } |
|
|
|
|