diff --git a/model/model.py b/model/model.py
index d5aed3f..5e4b1ec 100644
--- a/model/model.py
+++ b/model/model.py
@@ -155,8 +155,8 @@ def __init__(self, heads, head_size,hidden_size,RoPE=True):
         self.head_size = head_size
         self.RoPE = RoPE
         self.hidden_size = hidden_size
-        self.linear_1 = nn.Linear(hidden_size,hidden_size * 2,bias=True)
-        self.linear_2 = nn.Linear(hidden_size * 2,heads * 2,bias=True)
+        self.linear_1 = nn.Linear(hidden_size,head_size * 2,bias=True)
+        self.linear_2 = nn.Linear(head_size * 2,heads * 2,bias=True)
 
     def forward(self, inputs, mask=None):
         inputs = self.linear_1(inputs)
@@ -164,8 +164,8 @@ def forward(self, inputs, mask=None):
         # RoPE编码
         if self.RoPE:
             pos = SinusoidalPositionEmbedding(self.head_size, 'zero')(inputs)
-            cos_pos = pos[...,1::2].repeat(1,1,self.hidden_size // (self.head_size // 2))
-            sin_pos = pos[...,::2].repeat(1,1,self.hidden_size // (self.head_size // 2))
+            cos_pos = pos[...,1::2].repeat(1,1,2))
+            sin_pos = pos[...,::2].repeat(1,1,2))
             qw2 = torch.stack([-qw[..., 1::2], qw[..., ::2]], 3)
             qw2 = torch.reshape(qw2, qw.shape)
             qw = qw * cos_pos + qw2 * sin_pos
@@ -314,4 +314,4 @@ def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False,
             return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
         except ImportError:
             pass
-    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
\ No newline at end of file
+    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)