diff --git a/tensorrt_llm/_torch/models/modeling_llama.py b/tensorrt_llm/_torch/models/modeling_llama.py
index 54193a32c08b..743e0b8ef502 100644
--- a/tensorrt_llm/_torch/models/modeling_llama.py
+++ b/tensorrt_llm/_torch/models/modeling_llama.py
@@ -449,6 +449,10 @@ def __init__(
         self.input_layernorm = RMSNorm(hidden_size=config.hidden_size,
                                        eps=config.rms_norm_eps,
                                        dtype=config.torch_dtype)
+        # When post_load_weights() chains layernorms across layers,
+        # this flag is set to True to skip the input layernorm in
+        # forward() since it is handled by the previous layer.
+        self.skip_input_layernorm = False
 
         self.post_attention_layernorm = RMSNorm(hidden_size=config.hidden_size,
                                                 eps=config.rms_norm_eps,
@@ -493,6 +497,8 @@ def forward(
 
         if residual is None:
             residual = hidden_states
+
+        if not self.skip_input_layernorm:
             hidden_states = self.input_layernorm(hidden_states)
 
         # Self Attention
@@ -668,6 +674,10 @@ def __init__(
             quantize_type="nvfp4"
             if not self.disable_nvfp4_layernorm_fusion and self.is_nvfp4
             and not (differ_pp_stage_with_previous_layer) else None)
+        # When post_load_weights() chains layernorms across layers,
+        # this flag is set to True to skip the input layernorm in
+        # forward() since it is handled by the previous layer.
+        self.skip_input_layernorm = False
 
         self.post_attention_layernorm = RMSNorm(
             hidden_size=config.hidden_size,
@@ -765,6 +775,8 @@ def forward(
     ) -> Union[torch.Tensor, Fp4QuantizedTensor]:
         if residual is None:
             residual = hidden_states
+
+        if not self.skip_input_layernorm:
             hidden_states = self.input_layernorm(hidden_states)
 
         hidden_states = self.self_attn(
@@ -936,6 +948,10 @@ def __init__(self, model_config: ModelConfig[LlamaConfig]):
         self.norm = RMSNorm(hidden_size=config.hidden_size,
                             eps=config.rms_norm_eps,
                             dtype=config.torch_dtype)
+        # When post_load_weights() chains the final norm into the
+        # last decoder layer, this flag is set to True to skip
+        # applying it again in forward().
+        self.skip_norm = False
 
     def forward(
         self,
@@ -969,6 +985,10 @@ def forward(
                 lora_params=lora_params,
             )
 
+        # If self.norm is not handled by the last layer, apply it here.
+        if not self.skip_norm:
+            hidden_states = self.norm(hidden_states)
+
         return hidden_states
 
 
@@ -1033,6 +1053,10 @@ def __init__(self, model_config: ModelConfig[LlamaConfig]):
         self.norm = RMSNorm(hidden_size=config.hidden_size,
                             eps=config.rms_norm_eps,
                             dtype=config.torch_dtype)
+        # When post_load_weights() chains the final norm into the
+        # last decoder layer, this flag is set to True to skip
+        # applying it again in forward().
+        self.skip_norm = False
 
     def forward(
         self,
@@ -1065,6 +1089,10 @@ def forward(
                 lora_params=lora_params,
             )
 
+        # If self.norm is not handled by the last layer, apply it here.
+        if not self.skip_norm:
+            hidden_states = self.norm(hidden_states)
+
         return hidden_states
 
 
@@ -1082,9 +1110,11 @@ def post_load_weights(self):
                 self.model.layers[:self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:
                 layer.next_layer_layernorm = self.model.norm
+                self.model.skip_norm = True
             else:
                 layer.next_layer_layernorm = self.model.layers[
                     idx + 1].input_layernorm
+                self.model.layers[idx + 1].skip_input_layernorm = True
                 layer.next_attn = self.model.layers[idx + 1].self_attn
 
 
@@ -1456,9 +1486,11 @@ def post_load_weights(self):
                 self.model.layers[:self.config.num_hidden_layers]):
             if idx == self.config.num_hidden_layers - 1:
                 layer.next_layer_layernorm = self.model.norm
+                self.model.skip_norm = True
             else:
                 layer.next_layer_layernorm = self.model.layers[
                     idx + 1].input_layernorm
+                self.model.layers[idx + 1].skip_input_layernorm = True
                 layer.next_attn = self.model.layers[idx + 1].self_attn
 
 
diff --git a/tests/unittest/_torch/modeling/test_modeling_llama.py b/tests/unittest/_torch/modeling/test_modeling_llama.py
index ca503642c670..334e60a61e97 100644
--- a/tests/unittest/_torch/modeling/test_modeling_llama.py
+++ b/tests/unittest/_torch/modeling/test_modeling_llama.py
@@ -407,6 +407,7 @@ def test_llama_verification_with_kv_cache_relocation(self) -> None:
 
             llama = LlamaForCausalLM(model_config).to(dtype).to(device)
             llama.load_weights(hf_llama.state_dict())
+
         num_blocks = 2
         tokens_per_block = 32
         head_dim = llama.config.hidden_size // llama.config.num_attention_heads
diff --git a/tests/unittest/_torch/modeling/test_modeling_llama_min_latency.py b/tests/unittest/_torch/modeling/test_modeling_llama_min_latency.py
index 599b1be02119..0ce83559923d 100644
--- a/tests/unittest/_torch/modeling/test_modeling_llama_min_latency.py
+++ b/tests/unittest/_torch/modeling/test_modeling_llama_min_latency.py
@@ -271,10 +271,7 @@ def test_llama_allclose_to_hf(self, scenario: AllCloseScenario) -> None:
                 "The transformers between 4.55.0 and 4.56.1 have accuracy "
                 "issues for Llama4. See: "
                 "https://github.com/huggingface/transformers/pull/40609")
-        elif transformers.__version__ >= "4.57.1":
-            self.skipTest(
-                "Bumping transformers version to 4.57.1 has accuracy issues for Llama4. See: "
-                "http://nvbugs/5732958")
+
         torch.random.manual_seed(0)
         config_dict = deepcopy(LLAMA_4_MAVERICK_TWO_LAYER_CONFIG)
         # 17B * sizeof(float16) plus some extra for activations
@@ -301,6 +298,7 @@ def test_llama_allclose_to_hf(self, scenario: AllCloseScenario) -> None:
             weight_mapper.init_model_and_config(llama, model_config)
             llama.load_weights(hf_llama.state_dict(),
                                weight_mapper=weight_mapper)
+            llama.post_load_weights()
 
         num_blocks = 1
         tokens_per_block = 128