refactor export, fix #1190

Former-commit-id: 30e60e37023a7c4a2db033ffec0542efa3d5cdfb

src/llmtuner/hparams/__init__.py

@@ -1,5 +1,4 @@
 from .data_args import DataArguments
 from .finetuning_args import FinetuningArguments
-from .general_args import GeneralArguments
 from .generating_args import GeneratingArguments
 from .model_args import ModelArguments

src/llmtuner/hparams/finetuning_args.py

@@ -8,6 +8,10 @@ class FinetuningArguments:
     r"""
     Arguments pertaining to which techniques we are going to fine-tuning with.
     """
+    stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
+        default="sft",
+        metadata={"help": "Which stage will be performed in training."}
+    )
     finetuning_type: Optional[Literal["lora", "freeze", "full", "none"]] = field(
         default="lora",
         metadata={"help": "Which fine-tuning method to use."}

src/llmtuner/hparams/model_args.py

@@ -46,6 +46,10 @@ class ModelArguments:
         default=None,
         metadata={"help": "Adopt scaled rotary positional embeddings."}
     )
+    checkpoint_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
+    )
     flash_attn: Optional[bool] = field(
         default=False,
         metadata={"help": "Enable FlashAttention-2 for faster training."}
@@ -54,14 +58,14 @@ class ModelArguments:
         default=False,
         metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."}
     )
-    checkpoint_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
-    )
     reward_model: Optional[str] = field(
         default=None,
         metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
     )
+    upcast_layernorm: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to upcast the layernorm weights in fp32."}
+    )
     plot_loss: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
@@ -70,9 +74,9 @@ class ModelArguments:
         default=None,
         metadata={"help": "Auth token to log in with Hugging Face Hub."}
     )
-    upcast_layernorm: Optional[bool] = field(
-        default=False,
-        metadata={"help": "Whether to upcast the layernorm weights in fp32."}
+    export_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory to save the exported model."}
     )
 
     def __post_init__(self):