diff --git a/torchtitan/models/qwen3_5/state_dict_adapter.py b/torchtitan/models/qwen3_5/state_dict_adapter.py
index c8dad46128..937c2ca9fd 100644
--- a/torchtitan/models/qwen3_5/state_dict_adapter.py
+++ b/torchtitan/models/qwen3_5/state_dict_adapter.py
@@ -9,12 +9,11 @@
 
 Converts between HuggingFace Qwen3.5 checkpoint format and torchtitan format.
 
-MoE expert weights require two transformations:
-- **Transpose**: HF and TT use transposed layouts for grouped 3D expert weights.
-  E.g. HF down_proj [E, hidden, dim] <-> TT w2 [E, dim, hidden].
-- **Fuse/split gate_up_proj**: HF fuses gate_proj and up_proj into a single
-  gate_up_proj [E, dim, 2*hidden_dim]. TT stores them separately as
+MoE expert weights use the same grouped 3D layouts in HF and TT. HF fuses
+gate_proj and up_proj into a single gate_up_proj [E, 2*hidden_dim, dim], while
+TT stores them separately as
   w1 [E, hidden_dim, dim] and w3 [E, hidden_dim, dim].
+HF down_proj and TT w2 both use [E, dim, hidden_dim].
 
 Other notable conversions:
 - Conv3d patch embedding (HF) <-> Linear (TT) via weight reshape
@@ -143,7 +142,7 @@ def to_hf(self, state_dict: dict[str, Any]) -> dict[str, Any]:
                     hf_key = (
                         f"model.language_model.layers.{layer_num}.mlp.experts.down_proj"
                     )
-                    hf_state_dict[hf_key] = value.transpose(-2, -1)
+                    hf_state_dict[hf_key] = value
                     continue
 
                 if tt_abstract_key not in to_hf_map:
@@ -214,11 +213,11 @@ def to_hf(self, state_dict: dict[str, Any]) -> dict[str, Any]:
 
         # Fuse MoE w1 (gate) + w3 (up) → gate_up_proj
         for layer_num in moe_w1_by_layer:
-            w1 = moe_w1_by_layer[layer_num].transpose(-2, -1)
-            w3 = moe_w3_by_layer[layer_num].transpose(-2, -1)
+            w1 = moe_w1_by_layer[layer_num]
+            w3 = moe_w3_by_layer[layer_num]
             hf_state_dict[
                 f"model.language_model.layers.{layer_num}.mlp.experts.gate_up_proj"
-            ] = torch.cat([w1, w3], dim=-1)
+            ] = torch.cat([w1, w3], dim=-2)
 
         # Fuse vision wq/wk/wv → qkv
         for layer_num, parts in vision_qkv_by_layer.items():
@@ -269,28 +268,28 @@ def from_hf(self, hf_state_dict: dict[str, Any]) -> dict[str, Any]:
                 # pyrefly: ignore [missing-attribute]
                 idx = re.search(r"\d+", hf_key).group(0)
 
-                # MoE gate_up_proj → split into w1 + w3 and transpose
+                # MoE gate_up_proj → split into w1 + w3
                 if (
                     hf_abstract_key
                     == "model.language_model.layers.{}.mlp.experts.gate_up_proj"
                 ):
-                    w1_hf, w3_hf = value.chunk(2, dim=-1)
+                    w1_hf, w3_hf = value.chunk(2, dim=-2)
                     tt_state_dict[
                         f"layers.{idx}.moe.routed_experts.inner_experts.w1_EFD"
-                    ] = w1_hf.transpose(-2, -1)
+                    ] = w1_hf
                     tt_state_dict[
                         f"layers.{idx}.moe.routed_experts.inner_experts.w3_EFD"
-                    ] = w3_hf.transpose(-2, -1)
+                    ] = w3_hf
                     continue
 
-                # MoE down_proj → transpose
+                # MoE down_proj has the same layout as TT w2
                 if (
                     hf_abstract_key
                     == "model.language_model.layers.{}.mlp.experts.down_proj"
                 ):
                     tt_state_dict[
                         f"layers.{idx}.moe.routed_experts.inner_experts.w2_EDF"
-                    ] = value.transpose(-2, -1)
+                    ] = value
                     continue
 
                 # GatedDeltaNet fused in_proj_qkv → split into q/k/v