diff --git a/keras_hub/src/models/llama/llama_backbone.py b/keras_hub/src/models/llama/llama_backbone.py
index f4d3dd8730..a654bdf267 100644
--- a/keras_hub/src/models/llama/llama_backbone.py
+++ b/keras_hub/src/models/llama/llama_backbone.py
@@ -90,13 +90,14 @@ def __init__(
         layer_norm_epsilon=1e-6,
         dropout=0,
         dtype=None,
+        tie_word_embeddings=False,
         **kwargs,
     ):
         # === Layers ===
         self.token_embedding = ReversibleEmbedding(
             input_dim=vocabulary_size,
             output_dim=hidden_dim,
-            tie_weights=False,
+            tie_weights=tie_word_embeddings,
             embeddings_initializer=_llama_kernel_initializer(stddev=0.01),
             dtype=dtype,
             name="token_embedding",
@@ -155,6 +156,7 @@ def __init__(
         self.rope_scaling_factor = rope_scaling_factor
         self.layer_norm_epsilon = layer_norm_epsilon
         self.dropout = dropout
+        self.tie_word_embeddings = tie_word_embeddings
 
     def get_config(self):
         config = super().get_config()
diff --git a/keras_hub/src/utils/transformers/convert_llama3.py b/keras_hub/src/utils/transformers/convert_llama3.py
index 1207e5bc78..08e982e862 100644
--- a/keras_hub/src/utils/transformers/convert_llama3.py
+++ b/keras_hub/src/utils/transformers/convert_llama3.py
@@ -14,6 +14,7 @@ def convert_backbone_config(transformers_config):
         "hidden_dim": transformers_config["hidden_size"],
         "intermediate_dim": transformers_config["intermediate_size"],
         "num_key_value_heads": transformers_config["num_key_value_heads"],
+        "tie_word_embeddings": transformers_config["tie_word_embeddings"],
     }
 
 
@@ -22,12 +23,15 @@ def convert_weights(backbone, loader, transformers_config):
         keras_variable=backbone.get_layer("token_embedding").embeddings,
         hf_weight_key="model.embed_tokens.weight",
     )
-    loader.port_weight(
-        keras_variable=backbone.get_layer("token_embedding").reverse_embeddings,
-        hf_weight_key="lm_head.weight",
-        # rearrange_pattern="b a -> a b",
-        hook_fn=lambda hf_tensor, _: np.transpose(hf_tensor, axes=(1, 0)),
-    )
+    if not backbone.tie_word_embeddings:
+        loader.port_weight(
+            keras_variable=backbone.get_layer(
+                "token_embedding"
+            ).reverse_embeddings,
+            hf_weight_key="lm_head.weight",
+            # rearrange_pattern="b a -> a b",
+            hook_fn=lambda hf_tensor, _: np.transpose(hf_tensor, axes=(1, 0)),
+        )
 
     def transpose_and_reshape(x, shape):
         return np.reshape(np.transpose(x), shape)