diff --git a/llms/mistral/mistral.py b/llms/mistral/mistral.py
index f1022f0a..01d8cc36 100644
--- a/llms/mistral/mistral.py
+++ b/llms/mistral/mistral.py
@@ -70,38 +70,44 @@ def __call__(
         queries, keys, values = self.wq(x), self.wk(x), self.wv(x)
 
         # Prepare the queries, keys and values for the attention computation
-        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
-        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
-
-        if not args.optimized_sdpa:
-            # Baseline implementation requires the keys and values to be repeated
-            keys = mx.repeat(keys, self.repeats, axis=1)
-            values = mx.repeat(values, self.repeats, axis=1)
+        if args.optimized_sdpa:
+            queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
+            keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+            values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+        else:
+            queries = queries.reshape(
+                B,
+                L,
+                self. n_kv_heads,
+                self.n_heads // self.n_kv_heads,
+                -1,
+            ).moveaxis(1, 3)
+            keys = keys.reshape(B, L, self.n_kv_heads, 1, -1).moveaxis(1, 3)
+            values = values.reshape(B, L, self.n_kv_heads, 1, -1).moveaxis(1, 3)
 
         if cache is not None:
+            axis = 2 if args.optimized_sdpa else -2
             key_cache, value_cache = cache
-            queries = self.rope(queries, offset=key_cache.shape[2])
-            keys = self.rope(keys, offset=key_cache.shape[2])
-            keys = mx.concatenate([key_cache, keys], axis=2)
-            values = mx.concatenate([value_cache, values], axis=2)
+            queries = self.rope(queries, offset=key_cache.shape[axis])
+            keys = self.rope(keys, offset=key_cache.shape[axis])
+            keys = mx.concatenate([key_cache, keys], axis=axis)
+            values = mx.concatenate([value_cache, values], axis=axis)
         else:
             queries = self.rope(queries)
             keys = self.rope(keys)
 
         if args.optimized_sdpa:
-            # Optimized implementation
-            # TODO(atiorh): mx.fast_inference_sdpa --> mx.fast.scaled_dot_product_attention
-            output = mx.fast.scaled_dot_product_attention(queries, keys, values, scale=self.scale, mask=mask)
+            output = mx.fast.scaled_dot_product_attention(
+                queries, keys, values, scale=self.scale, mask=mask).transpose(0, 2, 1, 3)
         else:
             # Baseline implementation
-            scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
+            scores = (queries * self.scale) @ keys.swapaxes(-1, -2)
             if mask is not None:
                 scores += mask
             scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
-            output = (scores @ values)
+            output = (scores @ values).moveaxis(3, 1)
 
-        return self.wo(output.transpose(0, 2, 1, 3).reshape(B, L, -1)), (keys, values)
+        return self.wo(output.reshape(B, L, -1)), (keys, values)
 
 
 class FeedForward(nn.Module):