diff --git a/src/axolotl/monkeypatch/models/gemma4/fused_attn.py b/src/axolotl/monkeypatch/models/gemma4/fused_attn.py
index 4a171db8f..7cb5c6beb 100644
--- a/src/axolotl/monkeypatch/models/gemma4/fused_attn.py
+++ b/src/axolotl/monkeypatch/models/gemma4/fused_attn.py
@@ -30,6 +30,7 @@ def _make_fused_forward(original_forward):
         hidden_states: torch.Tensor,
         position_embeddings: torch.Tensor,
         attention_mask: torch.Tensor | None,
+        shared_kv_states: dict[int, tuple[torch.Tensor, torch.Tensor]],
         past_key_values=None,
         **kwargs,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
@@ -65,14 +66,8 @@ def _make_fused_forward(original_forward):
         query_states = query_states.transpose(1, 2)
 
         # ---- K/V path ----
-        # Current transformers stores shared kv on `past_key_values.shared_layers`
-        # (the legacy `shared_kv_states` decoder kwarg was removed). We mirror
-        # the stock attention forward exactly so the dispatch is identical
-        # regardless of whether the model was patched.
-        if self.is_kv_shared_layer and past_key_values is not None:
-            key_states, value_states = past_key_values.shared_layers[
-                self.kv_shared_layer_index
-            ]
+        if self.is_kv_shared_layer:
+            key_states, value_states = shared_kv_states[self.kv_shared_layer_index]
             key_states = key_states.to(query_states.device)
             value_states = value_states.to(query_states.device)
         else:
@@ -106,18 +101,12 @@ def _make_fused_forward(original_forward):
             value_states = fused_rms_norm_noscale(value_states, eps=eps)
             value_states = value_states.transpose(1, 2)
 
-        if past_key_values is not None:
-            if not self.is_kv_shared_layer:
-                key_states, value_states = past_key_values.update(
-                    key_states, value_states, self.layer_idx
-                )
-            if self.store_full_length_kv:
-                if not hasattr(past_key_values, "shared_layers"):
-                    past_key_values.shared_layers = {}
-                past_key_values.shared_layers[self.layer_idx] = (
-                    key_states,
-                    value_states,
-                )
+        if past_key_values is not None and not self.is_kv_shared_layer:
+            key_states, value_states = past_key_values.update(
+                key_states, value_states, self.layer_idx
+            )
+        if self.store_full_length_kv:
+            shared_kv_states[self.layer_idx] = key_states, value_states
 
         attention_interface: Callable = eager_attention_forward
         if self.config._attn_implementation != "eager":
diff --git a/tests/monkeypatch/test_gemma4_fused_attn.py b/tests/monkeypatch/test_gemma4_fused_attn.py
index ce8431477..0530d0ee8 100644
--- a/tests/monkeypatch/test_gemma4_fused_attn.py
+++ b/tests/monkeypatch/test_gemma4_fused_attn.py
@@ -3,16 +3,16 @@
 These tests exercise the patched ``Gemma4TextAttention.forward`` against
 the stock implementation it replaces. The hybrid Gemma 4 model intentionally
 mixes a sliding (`head_dim=32`) layer with a full-attention proportional-rope
-layer (`global_head_dim=64`, `partial_rotary_factor=0.25`) so that:
+layer (`global_head_dim=64`, `partial_rotary_factor=0.25`) so that the
+partial-rotary RMSNorm+RoPE path through the fused Triton kernel is
+exercised end-to-end (this is the bug originally documented in
+``GEMMA4_FUSED_ROPE_HYBRID_ATTN_BUG.md``).
 
-  1. The partial-rotary RMSNorm+RoPE path through the fused Triton kernel
-     gets exercised end-to-end (this is the bug originally documented in
-     ``GEMMA4_FUSED_ROPE_HYBRID_ATTN_BUG.md``).
-  2. The fused forward must match the current transformers attention API,
-     where the decoder layer no longer passes a ``shared_kv_states`` kwarg
-     and shared kv lives on ``past_key_values.shared_layers``. An older
-     fused_forward signature would raise ``TypeError: ... missing 1
-     required positional argument: 'shared_kv_states'`` here.
+The full-model forward also pins that the fused forward keeps accepting
+whatever call shape ``Gemma4TextDecoderLayer.forward`` produces in the
+installed transformers version — so any future signature drift on
+upstream's side trips a clear failure here instead of a confusing
+TypeError deep in a training run.
 """
 
 import pytest
@@ -86,15 +86,13 @@ def _build_model(seed=0):
 
 class TestFusedAttnSignature:
     """The fused forward must accept the same call shape as
-    ``Gemma4TextDecoderLayer`` produces under the current transformers API
-    (no ``shared_kv_states`` kwarg)."""
+    ``Gemma4TextDecoderLayer`` produces in the installed transformers
+    version. Any signature drift surfaces here as a TypeError."""
 
     def test_decoder_layer_can_call_fused_forward(self, restore_gemma4_attention):
-        """Regression for the API drift: decoder layer calls
-        ``self.self_attn(hidden_states=..., position_embeddings=...,
-        attention_mask=..., position_ids=..., past_key_values=...)`` and
-        nothing else. A signature with a positional ``shared_kv_states``
-        used to raise ``TypeError`` here before reaching the kernel."""
+        """Run a model forward that exercises the real
+        ``Gemma4TextDecoderLayer -> Gemma4TextAttention`` call path with
+        the fused patch installed."""
         from axolotl.monkeypatch.models.gemma4.fused_attn import (
             patch_gemma4_fused_attn,
         )
@@ -126,6 +124,7 @@ class TestFusedAttnPerLayerCorrectness:
             hidden_states=hidden_states,
             position_embeddings=(cos, sin),
             attention_mask=None,
+            shared_kv_states={},
         )
         return out