diff --git a/src/transformers/integrations/mxfp4.py b/src/transformers/integrations/mxfp4.py
index b37f72e7c388..67cb89ba4812 100644
--- a/src/transformers/integrations/mxfp4.py
+++ b/src/transformers/integrations/mxfp4.py
@@ -314,12 +314,12 @@ def should_convert_module(current_key_name, patterns):
 def dequantize(module, param_name, param_value, target_device, dq_param_name, **kwargs):
     from ..integrations.tensor_parallel import shard_and_distribute_module
 
-    model = kwargs.get("model", None)
-    empty_param = kwargs.get("empty_param", None)
-    casting_dtype = kwargs.get("casting_dtype", None)
-    to_contiguous = kwargs.get("to_contiguous", None)
-    rank = kwargs.get("rank", None)
-    device_mesh = kwargs.get("device_mesh", None)
+    model = kwargs.get("model")
+    empty_param = kwargs.get("empty_param")
+    casting_dtype = kwargs.get("casting_dtype")
+    to_contiguous = kwargs.get("to_contiguous")
+    rank = kwargs.get("rank")
+    device_mesh = kwargs.get("device_mesh")
 
     for proj in ["gate_up_proj", "down_proj"]:
         if proj in param_name:
@@ -357,12 +357,12 @@ def load_and_swizzle_mxfp4(module, param_name, param_value, target_device, **kwa
     )
     from ..integrations.tensor_parallel import shard_and_distribute_module
 
-    model = kwargs.get("model", None)
-    empty_param = kwargs.get("empty_param", None)
-    casting_dtype = kwargs.get("casting_dtype", None)
-    to_contiguous = kwargs.get("to_contiguous", None)
-    rank = kwargs.get("rank", None)
-    device_mesh = kwargs.get("device_mesh", None)
+    model = kwargs.get("model")
+    empty_param = kwargs.get("empty_param")
+    casting_dtype = kwargs.get("casting_dtype")
+    to_contiguous = kwargs.get("to_contiguous")
+    rank = kwargs.get("rank")
+    device_mesh = kwargs.get("device_mesh")
 
     for proj in ["gate_up_proj", "down_proj"]:
         if proj in param_name:
diff --git a/src/transformers/quantizers/quantizer_mxfp4.py b/src/transformers/quantizers/quantizer_mxfp4.py
index 5281d4d76388..4fb4168a8bd9 100644
--- a/src/transformers/quantizers/quantizer_mxfp4.py
+++ b/src/transformers/quantizers/quantizer_mxfp4.py
@@ -101,7 +101,7 @@ def validate_environment(self, *args, **kwargs):
             global triton_kernels_hub
             triton_kernels_hub = get_kernel("kernels-community/triton_kernels")
 
-        device_map = kwargs.get("device_map", None)
+        device_map = kwargs.get("device_map")
         if device_map is None:
             logger.warning_once(
                 "You have loaded an FP4 model on CPU and have a CUDA device available, make sure to set "
@@ -210,11 +210,11 @@ def create_quantized_param(
         # we take this path if already quantized but not in a compatible way
         # The params going here are either gate_up_proj_blocks, or down_proj_blocks, or gate_up_proj_scales, or down_proj_scales
         else:
-            empty_param = kwargs.get("empty_param", None)
-            casting_dtype = kwargs.get("casting_dtype", None)
-            to_contiguous = kwargs.get("to_contiguous", None)
-            rank = kwargs.get("rank", None)
-            device_mesh = kwargs.get("device_mesh", None)
+            empty_param = kwargs.get("empty_param")
+            casting_dtype = kwargs.get("casting_dtype")
+            to_contiguous = kwargs.get("to_contiguous")
+            rank = kwargs.get("rank")
+            device_mesh = kwargs.get("device_mesh")
             if ("blocks" in param_name or "scales" in param_name) and self.quantization_config.dequantize:
                 # blocks and scales have the same length that's this works for both
                 module, _ = get_module_from_name(model, param_name[: -len("_blocks")])