diff --git a/src/transformers/models/llama/configuration_llama.py b/src/transformers/models/llama/configuration_llama.py
index ea257b3a7b95a7..004d945c1274c9 100644
--- a/src/transformers/models/llama/configuration_llama.py
+++ b/src/transformers/models/llama/configuration_llama.py
@@ -78,7 +78,7 @@ class LlamaConfig(PretrainedConfig):
         tie_word_embeddings(`bool`, *optional*, defaults to `False`):
             Whether to tie weight embeddings
         rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
             strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
             is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
             `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
@@ -122,6 +122,7 @@ def __init__(
         pretraining_tp=1,
         tie_word_embeddings=False,
         rope_scaling=None,
+        rope_theta=10000,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -143,6 +144,7 @@ def __init__(
         self.use_cache = use_cache
         self.rope_scaling = rope_scaling
         self._rope_scaling_validation()
+        self.rope_theta = rope_theta
 
         super().__init__(
             pad_token_id=pad_token_id,
diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
index 5709fc6a778110..170a3e1ab41905 100644
--- a/src/transformers/models/llama/modeling_llama.py
+++ b/src/transformers/models/llama/modeling_llama.py
@@ -243,6 +243,7 @@ def __init__(self, config: LlamaConfig):
         self.num_key_value_groups = self.num_heads // self.num_key_value_heads
         self.pretraining_tp = config.pretraining_tp
         self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
 
         if (self.head_dim * self.num_heads) != self.hidden_size:
             raise ValueError(
@@ -257,21 +258,25 @@ def __init__(self, config: LlamaConfig):
 
     def _init_rope(self):
         if self.config.rope_scaling is None:
-            self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+            self.rotary_emb = LlamaRotaryEmbedding(
+                self.head_dim, max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta
+            )
         else:
             scaling_type = self.config.rope_scaling["type"]
             scaling_factor = self.config.rope_scaling["factor"]
             if scaling_type == "linear":
                 self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
-                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings,
+                    base=self.rope_theta, scaling_factor=scaling_factor
                 )
             elif scaling_type == "dynamic":
                 self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
-                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings,
+                    base=self.rope_theta, scaling_factor=scaling_factor
                 )
             else:
                 raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
         return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()