diff --git a/src/nanotron/models/llama.py b/src/nanotron/models/llama.py
index 4da2b81d..f3639155 100644
--- a/src/nanotron/models/llama.py
+++ b/src/nanotron/models/llama.py
@@ -394,14 +394,24 @@ def __init__(
             tp_recompute_allgather=parallel_config.tp_recompute_allgather,
         )
         # TODO(kunhao): We want to have only one version per device and not one version per layer.
-        self.rotary_embedding = RotaryEmbedding(
-            dim=self.d_qk,
-            end=config.max_position_embeddings,
-            theta=config.rope_theta,
-        )
+        if config.rope_interleaved:
+            self.rotary_embedding = RotaryEmbedding(
+                dim=self.d_qk,
+                end=config.max_position_embeddings,
+                theta=config.rope_theta,
+            )
+        else:
+            self.rotary_embedding = LlamaRotaryEmbedding(
+                dim=self.d_qk,
+                end=config.max_position_embeddings,
+                theta=config.rope_theta,
+            )
+        self.rope_interleaved = config.rope_interleaved
 
         # NOTE: Only supported for training (TODO(fmom): position_ids not supported yet)
-        self.flash_rotary_embedding = FlashRotaryEmbedding(dim=self.d_qk, base=config.rope_theta, interleaved=True)
+        self.flash_rotary_embedding = FlashRotaryEmbedding(
+            dim=self.d_qk, base=config.rope_theta, interleaved=config.rope_interleaved
+        )
 
         self.o_proj = TensorParallelRowLinear(
             config.num_attention_heads * self.d_qk,
@@ -480,8 +490,14 @@ def forward(
             # Compute rotary embeddings
             # Note: keep track of old rotary embedding end to check if we need to enlarge k_cache and v_cache
             old_rotary_embed_end = self.rotary_embedding.end
-            query_states = self.rotary_embedding(query_states, position_ids=position_ids)
-            key_states = self.rotary_embedding(key_states, position_ids=position_ids)
+            # interleaved version.
+            if self.rope_interleaved:
+                query_states = self.rotary_embedding(query_states, position_ids=position_ids)
+                key_states = self.rotary_embedding(key_states, position_ids=position_ids)
+            # non interleaved version.
+            else:
+                cos, sin = self.rotary_embedding(value_states, position_ids)
+                query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
             if "key" not in store:
                 # First inference iteration (Prefill)
@@ -620,7 +636,7 @@ def forward(
                     cache_seqlens=position_offsets.contiguous(),
                     softmax_scale=softmax_scale,
                     causal=True,
-                    rotary_interleaved=False,  # GPT-NeoX style
+                    rotary_interleaved=False,  # the value is not used unless rotary_cos/sin is provided. https://github.com/Dao-AILab/flash-attention
                 )
 
             store.update(