diff --git a/tests/kernels/moe/test_moe.py b/tests/kernels/moe/test_moe.py
index e3864a71d..aaa048ab8 100644
--- a/tests/kernels/moe/test_moe.py
+++ b/tests/kernels/moe/test_moe.py
@@ -680,13 +680,21 @@ def test_mixtral_moe(
 
         # Load the weights
         vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
-        for i in range(config.num_local_experts):
-            weights = (
-                hf_moe.experts[i].w1.weight.data,
-                hf_moe.experts[i].w3.weight.data,
-            )
-            vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
-            vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
+        if isinstance(hf_moe.experts, torch.nn.ModuleList):
+            # Transformers v4
+            for i in range(config.num_local_experts):
+                weights = (
+                    hf_moe.experts[i].w1.weight.data,
+                    hf_moe.experts[i].w3.weight.data,
+                )
+                vllm_moe.experts.w13_weight[i][:] = torch.cat(weights, dim=0)
+                vllm_moe.experts.w2_weight[i][:] = hf_moe.experts[i].w2.weight.data
+        else:
+            # Transformers v5
+            vllm_moe.experts.w13_weight.data[:] = hf_moe.experts.gate_up_proj.data
+            vllm_moe.experts.w2_weight.data[:] = hf_moe.experts.down_proj.data
+            # TODO: remove this line after https://github.com/huggingface/transformers/pull/43622
+            hf_moe.experts.config._experts_implementation = "eager"
 
         # Generate input batch of dimensions [batch_size, seq_len, hidden_dim]
         hf_inputs = torch.randn((1, 64, config.hidden_size)).to(dtype).to("cuda")
@@ -718,7 +726,10 @@ def test_mixtral_moe(
         get_forward_context().all_moe_layers = None
 
         # Run forward passes for both MoE blocks
-        hf_states, _ = hf_moe.forward(hf_inputs)
+        hf_states = hf_moe.forward(hf_inputs)
+        if isinstance(hf_states, tuple):
+            # Transformers v4
+            hf_states = hf_states[0]
         vllm_states = vllm_moe.forward(vllm_inputs)
 
     mixtral_moe_tol = {