refactor : 清理工厂和配置系统中的死代码与冗余抽象

- 删除 Registry 中未使用的 category/priority 字段，_entries 简化为直接存储类引用
- 修正 __init_subclass__ 避免叶子类（AutoRegressiveLM 等）创建空注册表
- 删除 5 个工厂的薄 create() 覆写，统一使用 BaseFactory.create(name, *args, **kwargs)
- 删除 3 处零调用的 available_types/available_strategies 别名死代码
- 删除零调用的 BaseModelConfig.to_file 死代码
- 将 BaseConfig.from_json/to_json 重命名为 from_file/to_file，消除与子类重复
- 移除两个 inference builder 中总是被覆写的 prompt_tokens=0

astrai/dataset/dataset.py

@@ -136,20 +136,6 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         dataset = DatasetFactory.create("custom", window_size, stride)
     """
 
-    @classmethod
-    def create(cls, train_type: str, window_size: int, stride: int) -> "BaseDataset":
-        """Create a dataset instance.
-
-        Args:
-            train_type: Type of training ("seq", "sft", "dpo", "grpo")
-            window_size: Window size for data sampling
-            stride: Stride between consecutive samples
-
-        Returns:
-            Dataset instance
-        """
-        return super().create(train_type, window_size, stride)
-
     @classmethod
     def load(
         cls,
@@ -179,11 +165,6 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
 
         return dataset
 
-    @classmethod
-    def available_types(cls) -> list:
-        """Return list of registered dataset type names."""
-        return cls.list_registered()
-
 
 @DatasetFactory.register("seq")
 class SEQDataset(BaseDataset):