diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py
index c15c0213b520..6584980f6dc2 100644
--- a/vllm/model_executor/models/llama.py
+++ b/vllm/model_executor/models/llama.py
@@ -162,20 +162,9 @@ def __init__(
             prefix=f"{prefix}.o_proj",
         )
 
-        is_neox_style = True
-        is_gguf = quant_config and quant_config.get_name() == "gguf"
-        if is_gguf and config.model_type == "llama":
-            is_neox_style = False
-
-        self.rotary_emb = get_rope(
-            self.head_dim,
-            rotary_dim=self.head_dim,
-            max_position=max_position_embeddings,
-            base=rope_theta,
-            rope_scaling=rope_scaling,
-            is_neox_style=is_neox_style,
-            partial_rotary_factor=self.partial_rotary_factor,
-        )
+        self._init_rotary_emb(config,
+                              rope_scaling=rope_scaling,
+                              quant_config=quant_config)
 
         if hasattr(config, "interleaved_sliding_window"):
             interleaved_sliding_window = config.interleaved_sliding_window
@@ -214,6 +203,24 @@ def forward(
         output, _ = self.o_proj(attn_output)
         return output
 
+    def _init_rotary_emb(self, config: LlamaConfig,
+                         rope_scaling: Optional[dict[str, Any]],
+                         quant_config: Optional[QuantizationConfig]) -> None:
+        is_neox_style = True
+        is_gguf = quant_config and quant_config.get_name() == "gguf"
+        if is_gguf and self.config.model_type == "llama":
+            is_neox_style = False
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=is_neox_style,
+            partial_rotary_factor=self.partial_rotary_factor,
+        )
+
 
 class LlamaDecoderLayer(nn.Module):
 
diff --git a/vllm/model_executor/models/nemotron_nas.py b/vllm/model_executor/models/nemotron_nas.py
index f4d5a77f2086..9808fe05558e 100644
--- a/vllm/model_executor/models/nemotron_nas.py
+++ b/vllm/model_executor/models/nemotron_nas.py
@@ -23,18 +23,20 @@
 # limitations under the License.
 """Inference-only deci model compatible with HuggingFace weights."""
 from collections.abc import Iterable
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 import torch
 from torch import nn
 from transformers import LlamaConfig
 
+from vllm.attention import AttentionType
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_pp_group
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import (
@@ -62,6 +64,48 @@ def _find_multiple(n: int, k: int) -> int:
     return n + k - (n % k)
 
 
+class DeciLMAttention(LlamaAttention):
+
+    def __init__(
+        self,
+        config: LlamaConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        bias_o_proj: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+    ) -> None:
+        super().__init__(config, hidden_size, num_heads, num_kv_heads,
+                         rope_theta, rope_scaling, max_position_embeddings,
+                         quant_config, bias, bias_o_proj, cache_config, prefix,
+                         attn_type)
+
+    def _init_rotary_emb(self, config, rope_scaling: Optional[dict[str, Any]],
+                         quant_config: Optional[QuantizationConfig]) -> None:
+        # Enables YARN for Mistral and LLaMA4 derivatives.
+        is_neox_style = True
+        if hasattr(config, "position_embedding_type"):
+            is_neox_style = config.position_embedding_type not in [
+                "mistral_yarn", "rope_llama4"
+            ]
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=self.max_position_embeddings,
+            base=self.rope_theta,
+            rope_scaling=rope_scaling,
+            is_neox_style=is_neox_style,
+            partial_rotary_factor=self.partial_rotary_factor)
+
+
 class DeciLMDecoderLayer(nn.Module):
 
     def __init__(
@@ -98,7 +142,7 @@ def __init__(
         if not self._is_no_op_attention:
             num_kv_heads = (config.num_attention_heads //
                             block_config.attention.n_heads_in_group)
-            self.self_attn = LlamaAttention(
+            self.self_attn = DeciLMAttention(
                 config=config,
                 hidden_size=self.hidden_size,
                 num_heads=config.num_attention_heads,