diff --git a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
index 7d252a0e..01b4ef10 100644
--- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
+++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py
@@ -7,8 +7,7 @@ from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_utils import PreTrainedModel
 
 from text_generation_server.utils import paged_attention, flash_attn
-from text_generation_server.utils.flash_attn import attention
-from text_generation_server.layers import (
+from text_generation_server.utils.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
@@ -139,10 +138,7 @@ class FlashRWAttention(torch.nn.Module):
         self.rope_theta = config.rope_theta
 
         self.rotary_emb = PositionRotaryEmbedding.static(
-            config=config,
-            dim=self.head_size,
-            base=self.rope_theta,
-            device=weights.device,
+            config=config, dim=self.head_size, base=self.rope_theta, device=weights.device
         )
         self.softmax_scale = self.head_size ** (-0.5)
 
@@ -480,6 +476,44 @@ class FlashRWLayer(nn.Module):
 
             return mlp_output, residual
 
+class FlashRWLayerNorm(nn.Module):
+    def __init__(self, config, prefix, weights):
+        super().__init__()
+        self.num_ln = config.num_ln_in_parallel_attn
+
+        if self.num_ln == 1:
+            self.input_ln = FastLayerNorm.load(
+                prefix=f"{prefix}.input_layernorm",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+        elif self.num_ln == 2:
+            self.ln_attn = FastLayerNorm.load(
+                prefix=f"{prefix}.ln_attn",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+            self.ln_mlp = FastLayerNorm.load(
+                prefix=f"{prefix}.ln_mlp",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+        else:
+            raise ValueError("Number of layer norms can either be 1 or 2.")
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+    ):
+        if self.num_ln == 1:
+            ln_hidden_states, residual = self.input_ln(hidden_states, residual)
+            return ln_hidden_states, ln_hidden_states, residual
+        elif self.num_ln == 2:
+            ln_attn, residual = self.ln_attn(hidden_states, residual)
+            ln_mlp, _ = self.ln_mlp(residual)
+            return ln_attn, ln_mlp, residual
+
 
 class FlashRWLayerNorm(nn.Module):
     def __init__(self, config, prefix, weights):
@@ -524,7 +558,7 @@ class FlashRWLargeLayer(nn.Module):
     def __init__(self, layer_id, config, weights):
         super().__init__()
         prefix = f"transformer.h.{layer_id}"
-
+        
         self.ln_layer = FlashRWLayerNorm(config, prefix, weights)
 
         self.self_attention = FlashRWLargeAttention(