diff --git a/python/sglang/srt/layers/linear.py b/python/sglang/srt/layers/linear.py
index 3b40a6067d9a..5b67519c4df7 100644
--- a/python/sglang/srt/layers/linear.py
+++ b/python/sglang/srt/layers/linear.py
@@ -257,7 +257,13 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
                     param.dtype == loaded_weight.dtype
                 ), "init para dtype and loaded weight dtype should be the same"
 
-        assert param.size() == loaded_weight.size()
+        assert param.size() == loaded_weight.size(), (
+            f"ReplicatedLinear weight size mismatch: "
+            f"param.size()={list(param.size())}, "
+            f"loaded_weight.size()={list(loaded_weight.size())}, "
+            f"param.dtype={param.dtype}, "
+            f"loaded_weight.dtype={loaded_weight.dtype}"
+        )
         param.data.copy_(loaded_weight)
 
     def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
diff --git a/python/sglang/srt/layers/quantization/modelopt_quant.py b/python/sglang/srt/layers/quantization/modelopt_quant.py
index c74415b9dbc0..9dec2fa63b01 100755
--- a/python/sglang/srt/layers/quantization/modelopt_quant.py
+++ b/python/sglang/srt/layers/quantization/modelopt_quant.py
@@ -290,9 +290,13 @@ def _get_quant_method(
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 
         if isinstance(layer, LinearBase):
-            if is_layer_skipped(
-                prefix, self.exclude_modules, self.packed_modules_mapping
-            ) or self.is_layer_excluded(prefix):
+            skipped = is_layer_skipped(
+                prefix,
+                self.exclude_modules,
+                self.packed_modules_mapping or {},
+            )
+            excluded = self.is_layer_excluded(prefix)
+            if skipped or excluded:
                 return UnquantizedLinearMethod()
             return Linear(self)
         elif self.kv_cache_quant_algo and isinstance(layer, RadixAttention):
@@ -1024,24 +1028,44 @@ def from_config(cls, config: Dict[str, Any]) -> ModelOptFp4Config:
         )
 
     def is_layer_excluded(self, prefix: str):
-        import regex as re
+        import re
 
         fused_patterns = ["q_a_proj", "q_b_proj", "kv_a_proj_with_mqa", "kv_b_proj"]
-        prefix_split = prefix.split(".")
-        for pattern in self.exclude_modules:
-            regex_str = pattern.replace(".", r"\.").replace("*", r".*")
-            pattern_split = pattern.split(".")
-            if re.fullmatch(regex_str, prefix):
-                return True
-            elif (
-                pattern_split[-1] in fused_patterns
-                and pattern_split[-1] in prefix_split[-1]
-            ):
-                # Check if the last part of the excluded pattern is contained in the last part of the prefix
-                # This handles fused modules like fused_qkv_a_proj_with_mqa that contain q_a_proj and kv_a_proj_with_mqa
-                # e.g., model.layers.{i}.self_attn.{fused_weight_name}
-                assert len(prefix_split) == 5 and len(pattern_split) == 5
-                return True
+        # Build candidate prefixes to handle naming mismatches between
+        # SGLang model prefixes and checkpoint ignore patterns.
+        # E.g., Kimi K2.5 VLM: SGLang prefix is "model.layers.X.self_attn.Y"
+        # but checkpoint ignore patterns use "language_model.layers.X.self_attn*".
+        prefixes_to_check = [prefix]
+        if prefix.startswith("language_model.model."):
+            # language_model.model.X -> language_model.X (drop inner "model.")
+            prefixes_to_check.append(
+                "language_model." + prefix.removeprefix("language_model.model.")
+            )
+            # language_model.model.X -> model.X (drop "language_model.")
+            prefixes_to_check.append(prefix.removeprefix("language_model."))
+        elif prefix.startswith("model."):
+            # model.X -> language_model.X (replace "model." with "language_model.")
+            prefixes_to_check.append("language_model." + prefix.removeprefix("model."))
+        elif prefix.startswith("language_model."):
+            prefixes_to_check.append(prefix.removeprefix("language_model."))
+
+        for check_prefix in prefixes_to_check:
+            check_prefix_split = check_prefix.split(".")
+            for pattern in self.exclude_modules:
+                regex_str = pattern.replace(".", r"\.").replace("*", r".*")
+                pattern_split = pattern.split(".")
+                if re.fullmatch(regex_str, check_prefix):
+                    return True
+                elif (
+                    pattern_split[-1] in fused_patterns
+                    and pattern_split[-1] in check_prefix_split[-1]
+                    and len(check_prefix_split) == 5
+                    and len(pattern_split) == 5
+                ):
+                    # Check if the last part of the excluded pattern is contained in the last part of the prefix
+                    # This handles fused modules like fused_qkv_a_proj_with_mqa that contain q_a_proj and kv_a_proj_with_mqa
+                    # e.g., model.layers.{i}.self_attn.{fused_weight_name}
+                    return True
         return False
 
     def get_quant_method(self, layer: torch.nn.Module, prefix: str):