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Default configs are better
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@ -139,7 +139,7 @@ class TrainingConfig:
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"""Configuration for training parameters."""
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"""Configuration for training parameters."""
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output_dir: str = "./outputs"
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output_dir: str = "./outputs"
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num_train_epochs: int = 3
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num_train_epochs: int = 1
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per_device_train_batch_size: int = 1
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per_device_train_batch_size: int = 1
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per_device_eval_batch_size: int = 1
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per_device_eval_batch_size: int = 1
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gradient_accumulation_steps: int = 8
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gradient_accumulation_steps: int = 8
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@ -158,7 +158,7 @@ class TrainingConfig:
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max_grad_norm: float = 1.0
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max_grad_norm: float = 1.0
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# Gradient checkpointing
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# Gradient checkpointing
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gradient_checkpointing: bool = True
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gradient_checkpointing: bool = False
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gradient_checkpointing_kwargs: Dict[str, Any] = field(default_factory=lambda: {"use_reentrant": False})
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gradient_checkpointing_kwargs: Dict[str, Any] = field(default_factory=lambda: {"use_reentrant": False})
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# Evaluation and checkpointing
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# Evaluation and checkpointing
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@ -178,9 +178,9 @@ class TrainingConfig:
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logging_first_step: bool = True
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logging_first_step: bool = True
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report_to: List[str] = field(default_factory=lambda: ["wandb"])
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report_to: List[str] = field(default_factory=lambda: ["wandb"])
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# Other training settings
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# Force seeds to a consistent value for reproducibility
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seed: int = 42
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seed: int = 42
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data_seed: Optional[int] = None
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data_seed: Optional[int] = 42
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# Performance
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# Performance
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dataloader_drop_last: bool = True
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dataloader_drop_last: bool = True
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