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configuration_minimax_m2.py

@@ -12,19 +12,6 @@ from typing import List, Optional, Union
 from transformers.configuration_utils import PretrainedConfig
 
 
-class _QuantizationConfigDict(dict):
-    """Ensure quantization config always exposes a `quant_method`."""
-
-    def __init__(self, data: Optional[dict] = None):
-        if data is None:
-            data = {}
-        super().__init__(data)
-        self.setdefault("quant_method", "none")
-
-    def to_dict(self):
-        return dict(self)
-
-
 class MiniMaxM2Config(PretrainedConfig):
     model_type = "minimax"
 
@@ -80,10 +67,6 @@ class MiniMaxM2Config(PretrainedConfig):
         **kwargs,
     ) -> None:
         quantization_config = kwargs.pop("quantization_config", None)
-        if quantization_config is None:
-            quantization_config = _QuantizationConfigDict()
-        elif not isinstance(quantization_config, _QuantizationConfigDict):
-            quantization_config = _QuantizationConfigDict(quantization_config)
         transformers_version = kwargs.pop("transformers_version", None)
 
         super().__init__(
@@ -140,7 +123,8 @@ class MiniMaxM2Config(PretrainedConfig):
 
         # Convenient accessor used by rotary embedding helper
         self.partial_rotary_factor = float(self.rotary_dim) / float(self.head_dim)
-        self.quantization_config = quantization_config
+        if quantization_config is not None:
+            self.quantization_config = quantization_config
         self.transformers_version = transformers_version