diff --git a/convert.py b/convert.py
index 39c52524d..94728015f 100755
--- a/convert.py
+++ b/convert.py
@@ -1257,19 +1257,19 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
 
     # merge experts into one tensor
     if params.n_experts > 0:
-        for l in range(params.n_layer):
+        for i_l in range(params.n_layer):
             for w in range(1, 4):
                 experts = []
                 for e in range(params.n_experts):
-                    if f"layers.{l}.feed_forward.experts.{e}.w{w}.weight" in model:
-                        experts.append(model[f"layers.{l}.feed_forward.experts.{e}.w{w}.weight"])
-                        del tmp[f"layers.{l}.feed_forward.experts.{e}.w{w}.weight"]
-                    elif f"model.layers.{l}.block_sparse_moe.experts.{e}.w{w}.weight" in model:
-                        experts.append(model[f"model.layers.{l}.block_sparse_moe.experts.{e}.w{w}.weight"])
-                        del tmp[f"model.layers.{l}.block_sparse_moe.experts.{e}.w{w}.weight"]
+                    if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model:
+                        experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"])
+                        del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]
+                    elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model:
+                        experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"])
+                        del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]
                     else:
-                        raise ValueError(f"Expert tensor not found: layers.{l}.feed_forward.experts.{e}.w{w}.weight")
-                tmp[f"layers.{l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts)
+                        raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight")
+                tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts)
 
     # HF models permut or pack some of the tensors, so we need to undo that
     for i in itertools.count():