diff --git a/configuration_stablelm_epoch.py b/configuration_stablelm_epoch.py
index ea24f38..7d8e249 100755
--- a/configuration_stablelm_epoch.py
+++ b/configuration_stablelm_epoch.py
@@ -65,6 +65,8 @@ class StableLMEpochConfig(PretrainedConfig):
             Whether or not the model should use bias for qkv layers.
         tie_word_embeddings(`bool`, *optional*, defaults to `False`):
             Whether to tie weight embeddings
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
     """
     model_type = "stablelm_epoch"
     keys_to_ignore_at_inference = ["past_key_values"]
@@ -88,6 +90,7 @@ class StableLMEpochConfig(PretrainedConfig):
         bos_token_id=0,
         eos_token_id=2,
         tie_word_embeddings=False,
+        attention_dropout: float = 0.0,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -105,6 +108,7 @@ class StableLMEpochConfig(PretrainedConfig):
         self.use_cache = use_cache
         self.use_qkv_bias = use_qkv_bias
         self.tie_word_embeddings = tie_word_embeddings
+        self.attention_dropout = attention_dropout
         super().__init__(
             bos_token_id=bos_token_id,
             eos_token_id=eos_token_id,
diff --git a/modeling_stablelm_epoch.py b/modeling_stablelm_epoch.py
index 930b98f..e7fde9f 100755
--- a/modeling_stablelm_epoch.py
+++ b/modeling_stablelm_epoch.py
@@ -191,6 +191,7 @@ class Attention(nn.Module):
         self.num_key_value_groups = self.num_heads // self.num_key_value_heads
         self.max_position_embeddings = config.max_position_embeddings
         self.is_causal = True
+        self.attention_dropout = config.attention_dropout
 
         if (self.head_dim * self.num_heads) != self.hidden_size:
             raise ValueError(
@@ -275,6 +276,7 @@ class Attention(nn.Module):
 
         # Upcast attention to fp32
         attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
         attn_output = torch.matmul(attn_weights, value_states)
 
         if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):