[config] update args (#7231)

Former-commit-id: f71a901840811bf560df671ec63a146ff99140c6

src/llamafactory/hparams/finetuning_args.py

@@ -415,15 +415,15 @@ class FinetuningArguments(
     )
     freeze_vision_tower: bool = field(
         default=True,
-        metadata={"help": "Whether ot not to freeze vision tower in MLLM training."},
+        metadata={"help": "Whether ot not to freeze the vision tower in MLLM training."},
     )
     freeze_multi_modal_projector: bool = field(
         default=True,
         metadata={"help": "Whether or not to freeze the multi modal projector in MLLM training."},
     )
-    train_mm_proj_only: bool = field(
+    freeze_language_model: bool = field(
         default=False,
-        metadata={"help": "Whether or not to train the multimodal projector for MLLM only."},
+        metadata={"help": "Whether or not to freeze the language model in MLLM training."},
     )
     compute_accuracy: bool = field(
         default=False,
@@ -455,8 +455,6 @@ class FinetuningArguments(
         self.additional_target: Optional[List[str]] = split_arg(self.additional_target)
         self.galore_target: List[str] = split_arg(self.galore_target)
         self.apollo_target: List[str] = split_arg(self.apollo_target)
-        self.freeze_vision_tower = self.freeze_vision_tower or self.train_mm_proj_only
-        self.freeze_multi_modal_projector = self.freeze_multi_modal_projector and not self.train_mm_proj_only
         self.use_ref_model = self.stage == "dpo" and self.pref_loss not in ["orpo", "simpo"]
 
         assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
@@ -484,9 +482,6 @@ class FinetuningArguments(
         if self.pissa_init and (self.stage in ["ppo", "kto"] or self.use_ref_model):
             raise ValueError("Cannot use PiSSA for current training stage.")
 
-        if self.train_mm_proj_only and self.finetuning_type != "full":
-            raise ValueError("`train_mm_proj_only` is only valid for full training.")
-
         if self.finetuning_type != "lora":
             if self.loraplus_lr_ratio is not None:
                 raise ValueError("`loraplus_lr_ratio` is only valid for LoRA training.")

src/llamafactory/hparams/model_args.py

@@ -23,6 +23,8 @@ import torch
 from transformers.training_args import _convert_str_dict
 from typing_extensions import Self
 
+from ..extras.constants import AttentionFunction, EngineName, RopeScaling
+
 
 @dataclass
 class BaseModelArguments:
@@ -77,12 +79,12 @@ class BaseModelArguments:
         default=True,
         metadata={"help": "Whether or not to use memory-efficient model loading."},
     )
-    rope_scaling: Optional[Literal["linear", "dynamic", "yarn", "llama3"]] = field(
+    rope_scaling: Optional[RopeScaling] = field(
         default=None,
         metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
     )
-    flash_attn: Literal["auto", "disabled", "sdpa", "fa2"] = field(
-        default="auto",
+    flash_attn: AttentionFunction = field(
+        default=AttentionFunction.AUTO,
         metadata={"help": "Enable FlashAttention for faster training and inference."},
     )
     shift_attn: bool = field(
@@ -129,8 +131,8 @@ class BaseModelArguments:
         default=False,
         metadata={"help": "Whether or not to randomly initialize the model weights."},
     )
-    infer_backend: Literal["huggingface", "vllm"] = field(
-        default="huggingface",
+    infer_backend: EngineName = field(
+        default=EngineName.HF,
         metadata={"help": "Backend engine used at inference."},
     )
     offload_folder: str = field(