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dllm/examples/editflow/bert/sft.py

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+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,
+    )