diff --git a/src/axolotl/monkeypatch/attention/xformers.py b/src/axolotl/monkeypatch/attention/xformers.py
index 394169eef..5901963f0 100644
--- a/src/axolotl/monkeypatch/attention/xformers.py
+++ b/src/axolotl/monkeypatch/attention/xformers.py
@@ -7,7 +7,6 @@ from typing import Optional
 import torch
 import xformers
 import xformers.ops.fmha
-from flash_attn.bert_padding import pad_input
 from transformers.modeling_flash_attention_utils import (
     _upad_input,
 )
@@ -34,14 +33,29 @@ def xformers_attention_forward(
     max_length_k: Optional[int] = None,  # pylint: disable=unused-argument
     **kwargs,  # pylint: disable=unused-argument
 ):
+    # Get dimensions
+    # query: [batch, heads, seq_len, hidden_dim]
+    batch_size = query.size(0)
+    query_length = query.shape[2]
+    key_length = key.shape[2]
 
+    # Default causal mask
+    attn_bias = xformers.ops.LowerTriangularMask()
+
+    # Check if we have sliding window attention
+    has_sliding_window = sliding_window is not None and sliding_window < query_length
+
+    # Transpose dimensions for xformers (Q: [b, h, s, d] -> [b, s, h, d])
     query = query.transpose(1, 2)
     key = key.transpose(1, 2)
     value = value.transpose(1, 2)
-    query_length = query.shape[2]
-    batch_size = query.size(0)
 
-    attn_bias = xformers.ops.LowerTriangularMask()
+    # Get GQA parameters
+    num_attention_heads = module.config.num_attention_heads
+    num_key_value_heads = module.config.num_key_value_heads
+    head_dim = query.size(-1)
+    is_gqa = num_attention_heads != num_key_value_heads
+    n_groups = num_attention_heads // num_key_value_heads if is_gqa else 1
 
     # If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
     # then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
@@ -64,25 +78,13 @@ def xformers_attention_forward(
             key = key.reshape(-1, key.size(-2), key.size(-1))
             value = value.reshape(-1, value.size(-2), value.size(-1))
 
-        # pylint: disable=duplicate-code
-        if module.config.num_attention_heads != module.config.num_key_value_heads:
-            key = key.repeat_interleave(
-                module.config.num_attention_heads // module.config.num_key_value_heads,
-                dim=2,
-            )
-            value = value.repeat_interleave(
-                module.config.num_attention_heads // module.config.num_key_value_heads,
-                dim=2,
-            )
+        # Handle GQA
+        if is_gqa:
+            key = key.repeat_interleave(n_groups, dim=2)
+            value = value.repeat_interleave(n_groups, dim=2)
 
-        attn_output = xformers_attention(
-            query,
-            key,
-            value,
-            attn_bias=attn_bias,
-        )
     elif attention_mask is not None:
-        query, key, value, indices_q, cu_seq_lens, _ = _upad_input(
+        query, key, value, _, cu_seq_lens, _ = _upad_input(
             query, key, value, attention_mask, query_length
         )
         cu_seq_lens_q, cu_seq_lens_k = cu_seq_lens
@@ -94,31 +96,63 @@ def xformers_attention_forward(
             kv_seqlen=seq_lengths,
         )
 
-        attn_output_unpad = xformers_attention(
-            query,
-            key,
-            value,
-            attn_bias=attn_bias,
-        )
-        attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        # Handle GQA
+        if is_gqa:
+            key = key.repeat_interleave(n_groups, dim=2)
+            value = value.repeat_interleave(n_groups, dim=2)
     else:
-        # pylint: disable=duplicate-code
-        if module.config.num_attention_heads != module.config.num_key_value_heads:
-            key = key.repeat_interleave(
-                module.config.num_attention_heads // module.config.num_key_value_heads,
-                dim=2,
-            )
-            value = value.repeat_interleave(
-                module.config.num_attention_heads // module.config.num_key_value_heads,
-                dim=2,
+        # Handle Group Query Attention (GQA) using view/expand approach from reference
+        key = key.view(batch_size, key_length, num_key_value_heads, 1, head_dim)
+        value = value.view(batch_size, key_length, num_key_value_heads, 1, head_dim)
+        key = key.expand(
+            batch_size, key_length, num_key_value_heads, n_groups, head_dim
+        )
+        value = value.expand(
+            batch_size, key_length, num_key_value_heads, n_groups, head_dim
+        )
+
+        if module.training:
+            key = key.reshape(batch_size, key_length, num_attention_heads, head_dim)
+            value = value.reshape(batch_size, key_length, num_attention_heads, head_dim)
+
+            if has_sliding_window:
+                query = query.view(
+                    1, batch_size * query_length, num_attention_heads, head_dim
+                )
+                key = key.view(
+                    1, batch_size * key_length, num_attention_heads, head_dim
+                )
+                value = value.view(
+                    1, batch_size * key_length, num_attention_heads, head_dim
+                )
+        else:
+            query = query.view(
+                batch_size, query_length, num_key_value_heads, n_groups, head_dim
             )
 
-        attn_output = xformers_attention(
-            query,
-            key,
-            value,
-            attn_bias=attn_bias,
-        )
+            # If we need a sliding window attention
+            if has_sliding_window:
+                query = query.view(
+                    1,
+                    batch_size * query_length,
+                    num_key_value_heads,
+                    n_groups,
+                    head_dim,
+                )
+                key = key.view(
+                    1, batch_size * key_length, num_key_value_heads, n_groups, head_dim
+                )
+                value = value.view(
+                    1, batch_size * key_length, num_key_value_heads, n_groups, head_dim
+                )
+
+    # Run the xformers attention
+    attn_output = xformers_attention(
+        query,
+        key,
+        value,
+        attn_bias=attn_bias,
+    )
 
     attn_output = attn_output.view(
         batch_size, -1, attn_output.size(-2), attn_output.size(-1)