45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from dataclasses import dataclass
|
|
|
|
import transformers
|
|
|
|
import dllm
|
|
from examples.editflow import sft as editflow_sft
|
|
|
|
|
|
@dataclass
|
|
class ModelArguments(editflow_sft.ModelArguments):
|
|
model_name_or_path: str = "answerdotai/ModernBERT-large"
|
|
lm_head_key: str = "decoder"
|
|
|
|
|
|
@dataclass
|
|
class DataArguments(editflow_sft.DataArguments):
|
|
dataset_args: str = "tatsu-lab/alpaca"
|
|
max_length: int = 512
|
|
|
|
|
|
@dataclass
|
|
class TrainingArguments(editflow_sft.TrainingArguments):
|
|
output_dir: str = "models/EditFlow/ModernBERT-large/alpaca"
|
|
num_train_epochs: float = 20
|
|
learning_rate: float = 3e-4
|
|
per_device_train_batch_size: int = 64
|
|
per_device_eval_batch_size: int = 64
|
|
eval_steps: float = 0.1
|
|
save_steps: float = 0.1
|
|
x0_sampler: str = "masks[length:64]"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# ----- Argument parsing -------------------------------------------------------
|
|
parser = transformers.HfArgumentParser(
|
|
(ModelArguments, DataArguments, TrainingArguments)
|
|
)
|
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
|
editflow_sft.train(
|
|
model_args=model_args,
|
|
data_args=data_args,
|
|
training_args=training_args,
|
|
ef_config_cls=dllm.pipelines.editflow.EditFlowModernBertConfig,
|
|
)
|