diff --git a/examples/models/llama2/builder.py b/examples/models/llama2/builder.py
index 00d71a5b014..b05dc19bfc0 100644
--- a/examples/models/llama2/builder.py
+++ b/examples/models/llama2/builder.py
@@ -62,6 +62,7 @@ def to_torch_dtype(self) -> torch.dtype:
 
 def load_llama_model(
     *,
+    modelname: str = "llama2",
     checkpoint: Optional[str] = None,
     checkpoint_dir: Optional[str] = None,
     params_path: str,
@@ -114,6 +115,7 @@ def load_llama_model(
 
     return LlamaEdgeManager(
         model=model,
+        modelname=modelname,
         weight_type=weight_type,
         dtype=dtype,
         use_kv_cache=use_kv_cache,
@@ -131,6 +133,7 @@ class LlamaEdgeManager:
     def __init__(
         self,
         model,
+        modelname,
         weight_type,
         dtype,
         use_kv_cache,
@@ -139,6 +142,7 @@ def __init__(
         verbose: bool = False,
     ):
         self.model = model
+        self.modelname = modelname
         self.weight_type = weight_type
         self.dtype = dtype
         self.example_inputs = example_inputs
diff --git a/examples/models/llama2/export_llama_lib.py b/examples/models/llama2/export_llama_lib.py
index 6a34c9bd889..9a8baf5aa1f 100644
--- a/examples/models/llama2/export_llama_lib.py
+++ b/examples/models/llama2/export_llama_lib.py
@@ -485,7 +485,6 @@ def _prepare_for_llama_export(modelname: str, args) -> LlamaEdgeManager:
     )
     params_path = canonical_path(args.params)
     output_dir_path = canonical_path(args.output_dir, dir=True)
-    modelname = "llama2"
     weight_type = WeightType.FAIRSEQ2 if args.fairseq2 else WeightType.LLAMA
 
     # dtype override
@@ -552,6 +551,7 @@ def _prepare_for_llama_export(modelname: str, args) -> LlamaEdgeManager:
 
     return (
         load_llama_model(
+            modelname=modelname,
             checkpoint=checkpoint_path,
             checkpoint_dir=checkpoint_dir,
             params_path=params_path,
@@ -599,6 +599,8 @@ def _export_llama(modelname, args) -> str:  # noqa: C901
         modelname, args
     ).export_to_edge(quantizers)
 
+    modelname = builder_exported_to_edge.modelname
+
     # to_backend
     partitioners = []
     if pt2e_quant_params is not None and pt2e_quant_params.quantize_linear is not None: