fix lint

README.md

@@ -448,7 +448,7 @@ docker run -it --gpus=all \
 
 ```bash
 docker-compose up -d
-docker-compose exec -it llamafactory bash
+docker-compose exec llamafactory bash
 ```
 
 <details><summary>Details about volume</summary>

README_zh.md

@@ -448,7 +448,7 @@ docker run -it --gpus=all \
 
 ```bash
 docker-compose up -d
-docker-compose exec -it llamafactory bash
+docker-compose exec llamafactory bash
 ```
 
 <details><summary>数据卷详情</summary>

src/llamafactory/hparams/finetuning_args.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from typing import Literal, Optional
+from typing import List, Literal, Optional
 
 
 @dataclass
@@ -319,20 +319,19 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
                 return [item.strip() for item in arg.split(",")]
             return arg
 
-        self.freeze_trainable_modules = split_arg(self.freeze_trainable_modules)
-        self.freeze_extra_modules = split_arg(self.freeze_extra_modules)
-        self.lora_alpha = self.lora_alpha or self.lora_rank * 2
-        self.lora_target = split_arg(self.lora_target)
-        self.additional_target = split_arg(self.additional_target)
-        self.galore_target = split_arg(self.galore_target)
+        self.freeze_trainable_modules: List[str] = split_arg(self.freeze_trainable_modules)
+        self.freeze_extra_modules: Optional[List[str]] = split_arg(self.freeze_extra_modules)
+        self.lora_alpha: int = self.lora_alpha or self.lora_rank * 2
+        self.lora_target: List[str] = split_arg(self.lora_target)
+        self.additional_target: Optional[List[str]] = split_arg(self.additional_target)
+        self.galore_target: List[str] = split_arg(self.galore_target)
         self.freeze_vision_tower = self.freeze_vision_tower or self.train_mm_proj_only
+        self.use_ref_model = self.pref_loss not in ["orpo", "simpo"]
 
         assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
         assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
         assert self.reward_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
 
-        self.use_ref_model = self.pref_loss not in ["orpo", "simpo"]
-
         if self.stage == "ppo" and self.reward_model is None:
             raise ValueError("`reward_model` is necessary for PPO training.")
 

src/llamafactory/hparams/model_args.py

@@ -1,9 +1,13 @@
 from dataclasses import asdict, dataclass, field
-from typing import Any, Dict, Literal, Optional
+from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Union
 
 from typing_extensions import Self
 
 
+if TYPE_CHECKING:
+    import torch
+
+
 @dataclass
 class ModelArguments:
     r"""
@@ -194,9 +198,9 @@ class ModelArguments:
     )
 
     def __post_init__(self):
-        self.compute_dtype = None
-        self.device_map = None
-        self.model_max_length = None
+        self.compute_dtype: Optional["torch.dtype"] = None
+        self.device_map: Optional[Union[str, Dict[str, Any]]] = None
+        self.model_max_length: Optional[int] = None
 
         if self.split_special_tokens and self.use_fast_tokenizer:
             raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")