diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 1e9effc26..31ebc1ab0 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -1516,12 +1516,19 @@ class DbrxModel(Model):
         block_count = self.hparams.get("n_layers")
         tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
         for name, data_torch in self.get_tensors():
+            n_expert = self.hparams["ffn_config"]["moe_num_experts"]
+            n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
+            n_embd = self.hparams["d_model"]
+
             # Specific behavior for experts tensors: reshape to 3D and add suffix .weight
-            exp_tensor_names = ["ffn.experts.mlp.v1", "ffn.experts.mlp.w1", "ffn.experts.mlp.w2"]
+            exp_tensor_names = {"ffn.experts.mlp.v1": (n_embd, n_ff,   n_expert),  # LLM_TENSOR_FFN_GATE_EXPS
+                                "ffn.experts.mlp.w1": (n_embd, n_ff,   n_expert),  # LLM_TENSOR_FFN_DOWN_EXPS
+                                "ffn.experts.mlp.w2": (n_ff,   n_embd, n_expert)}  # LLM_TENSOR_FFN_UP_EXPS
             experts = False
-            for exp_tensor_name in exp_tensor_names:
+            for exp_tensor_name in exp_tensor_names.keys():
                 if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
                     experts = True
+                    expert_reshape = exp_tensor_names[exp_tensor_name]
                     break
 
             old_dtype = data_torch.dtype
@@ -1548,7 +1555,7 @@ class DbrxModel(Model):
 
             # Reshape experts tensors from 2D to 3D as expected by GeLU
             if experts and n_dims == 2:
-                data = data.reshape((self.hparams["ffn_config"]["moe_num_experts"], self.hparams["ffn_config"]["ffn_hidden_size"], self.hparams["d_model"]))
+                data = data.reshape(expert_reshape)
                 n_dims = len(data.shape)
 
             # if f32 desired, convert any float16 to float32