diff --git a/src/anemoi/models/layers/attention.py b/src/anemoi/models/layers/attention.py
index 0784e06..c803109 100644
--- a/src/anemoi/models/layers/attention.py
+++ b/src/anemoi/models/layers/attention.py
@@ -8,9 +8,11 @@
 #
 
 import logging
+import math
 from typing import Optional
 
 import einops
+import torch
 from torch import Tensor
 from torch import nn
 from torch.distributed.distributed_c10d import ProcessGroup
@@ -76,10 +78,11 @@ def __init__(
         self.dropout_p = dropout_p
         self.is_causal = is_causal
         self.softcap = softcap
-        self.alibi_slopes = alibi_slopes
+        self.use_alibi_slopes = True  # use_alibi_slopes
 
-        if alibi_slopes is not None:
-            assert alibi_slopes.shape[0] == num_heads
+        if self.use_alibi_slopes is not None:
+            self.alibi_slopes = get_alibi_slopes(num_heads)
+            assert self.alibi_slopes.shape[0] == num_heads
 
         self.lin_qkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias)
 
@@ -126,6 +129,9 @@ def forward(
             query, key, value = (
                 einops.rearrange(t, "batch heads grid vars -> batch grid heads vars") for t in (query, key, value)
             )
+
+            alibi_slopes = self.alibi_slopes.repeat(query.shape[0]).to(query.device) if self.use_alibi_slopes else None
+
             out = self.attention(
                 query,
                 key,
@@ -134,7 +140,7 @@ def forward(
                 window_size=self.window_size,
                 dropout_p=dropout_p,
                 softcap=self.softcap,
-                alibi_slopes=self.alibi_slopes,
+                alibi_slopes=alibi_slopes,
             )
             out = einops.rearrange(out, "batch grid heads vars -> batch heads grid vars")
         else:
@@ -152,3 +158,14 @@ def forward(
         out = self.projection(out)
 
         return out
+
+
+def get_alibi_slopes(num_heads: int) -> Tensor:
+    n = 2 ** math.floor(math.log2(num_heads))
+    slope_0 = 2.0 ** (-8.0 / n)
+    alibi_slopes = torch.pow(slope_0, torch.arange(1, 1 + n))
+    if n < num_heads:
+        slope_hat_0 = 2.0 ** (-4.0 / n)
+        alibi_slopes_hat = torch.pow(slope_hat_0, torch.arange(1, 1 + 2 * (num_heads - n), 2))
+        alibi_slopes = torch.cat([alibi_slopes, alibi_slopes_hat])
+    return alibi_slopes