diff --git a/nemo_automodel/_transformers/kernel_patches.py b/nemo_automodel/_transformers/kernel_patches.py
index 3aadffc56e..6438ab3466 100644
--- a/nemo_automodel/_transformers/kernel_patches.py
+++ b/nemo_automodel/_transformers/kernel_patches.py
@@ -136,6 +136,51 @@ def _patch_liger_kernel(model):
         raise RuntimeError("Failed to patch model")
 
 
+def _patch_legacy_flash_attn_flag():
+    """Bridge the legacy ``_supports_flash_attn_2`` class flag to v5.5's
+    ``_supports_flash_attn``.
+
+    transformers v5.5 renamed the FA2-support attribute from
+    ``_supports_flash_attn_2`` to ``_supports_flash_attn`` and switched the
+    dispatch check at ``_flash_attn_can_dispatch`` to the new name only.
+    Remote-code models pinned against <=v5.3 (e.g. microsoft/Phi-4-multimodal-instruct
+    sets ``_supports_flash_attn_2 = True`` in its modeling file) are not aware
+    of the rename, so their FA2 support is invisible to v5.5 and
+    ``attn_implementation="flash_attention_2"`` raises ``ValueError``.
+
+    Install a property on ``PreTrainedModel._supports_flash_attn`` that falls
+    back to the legacy flag when a subclass has not set the new one. Subclasses
+    that set ``_supports_flash_attn = True`` directly still shadow the property
+    via normal MRO lookup, so native models are unaffected.
+    """
+    import transformers.modeling_utils as mu
+
+    base = mu.PreTrainedModel
+    if getattr(base, "_nemo_fa2_flag_bridged", False):
+        return
+
+    # Capture the base-class default (``False`` on v5.5) so the fallback
+    # preserves original behavior when no flag is set anywhere.
+    _base_default = base.__dict__.get("_supports_flash_attn", False)
+
+    def _supports_flash_attn_fget(self):
+        cls = type(self)
+        for klass in cls.__mro__:
+            # Stop at the base — the property lives here; anything below is
+            # just the captured default.
+            if klass is base:
+                break
+            d = klass.__dict__
+            if "_supports_flash_attn" in d:
+                return d["_supports_flash_attn"]
+            if d.get("_supports_flash_attn_2") is True:
+                return True
+        return _base_default
+
+    base._supports_flash_attn = property(_supports_flash_attn_fget)
+    base._nemo_fa2_flag_bridged = True  # type: ignore[attr-defined]
+
+
 def _get_next_fallback_attn(attn_implementation: str) -> str:
     """
     Get the next attention implementation in the priority list, in reverse order.
diff --git a/nemo_automodel/_transformers/utils.py b/nemo_automodel/_transformers/utils.py
index f0525d4ebc..06e303c1c6 100644
--- a/nemo_automodel/_transformers/utils.py
+++ b/nemo_automodel/_transformers/utils.py
@@ -236,6 +236,10 @@ def _patched_post_init(self):
     _patch_phi4mm_processor()
     _patch_peft_prepare_inputs()
 
+    from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+    _patch_legacy_flash_attn_flag()
+
 
 def _patch_phi4mm_processor():
     """Patch AutoProcessor.from_pretrained to fall back to the remote
diff --git a/nemo_automodel/components/models/qwen3_5_moe/cp_linear_attn.py b/nemo_automodel/components/models/qwen3_5_moe/cp_linear_attn.py
index 07b414e69f..63a86bb0fc 100644
--- a/nemo_automodel/components/models/qwen3_5_moe/cp_linear_attn.py
+++ b/nemo_automodel/components/models/qwen3_5_moe/cp_linear_attn.py
@@ -97,8 +97,11 @@ def _forward_no_cp(
     ):
         """HF GatedDeltaNet forward with FSDP-safe fp32 gate computation.
 
-        Copied from transformers==5.3.0 Qwen3_5GatedDeltaNet.forward
-        with gate computation replaced by self._compute_gate(a).
+        Mirrors transformers==5.5 Qwen3_5GatedDeltaNet.forward (uses the
+        per-layer cache API: ``has_previous_state(layer_idx)``,
+        ``cache_params.layers[layer_idx].{conv,recurrent}_states``, and the
+        ``update_{conv,recurrent}_state`` methods) with the gate computation
+        replaced by ``self._compute_gate(a)``.
         """
         from transformers.models.qwen3_5.modeling_qwen3_5 import apply_mask_to_padding_states
 
@@ -106,12 +109,12 @@ def _forward_no_cp(
         batch_size, seq_len, _ = hidden_states.shape
 
         use_precomputed_states = (
-            cache_params is not None and cache_params.has_previous_state and seq_len == 1 and cache_position is not None
+            cache_params is not None and cache_params.has_previous_state(self.layer_idx) and seq_len == 1
         )
 
-        if cache_params is not None:
-            conv_state = cache_params.conv_states[self.layer_idx]
-            recurrent_state = cache_params.recurrent_states[self.layer_idx]
+        if use_precomputed_states:
+            conv_state = cache_params.layers[self.layer_idx].conv_states
+            recurrent_state = cache_params.layers[self.layer_idx].recurrent_states
 
         mixed_qkv = self.in_proj_qkv(hidden_states)
         mixed_qkv = mixed_qkv.transpose(1, 2)
@@ -133,7 +136,7 @@ def _forward_no_cp(
         else:
             if cache_params is not None:
                 conv_state = F.pad(mixed_qkv, (self.conv_kernel_size - mixed_qkv.shape[-1], 0))
-                cache_params.conv_states[self.layer_idx] = conv_state
+                cache_params.update_conv_state(conv_state, self.layer_idx)
             if self.causal_conv1d_fn is not None:
                 mixed_qkv = self.causal_conv1d_fn(
                     x=mixed_qkv,
@@ -183,7 +186,7 @@ def _forward_no_cp(
             )
 
         if cache_params is not None:
-            cache_params.recurrent_states[self.layer_idx] = last_recurrent_state
+            cache_params.update_recurrent_state(last_recurrent_state, self.layer_idx)
 
         core_attn_out = core_attn_out.reshape(-1, self.head_v_dim)
         z = z.reshape(-1, self.head_v_dim)
diff --git a/tests/unit_tests/_transformers/test_auto_model.py b/tests/unit_tests/_transformers/test_auto_model.py
index 583b5202c7..c8d8296e2c 100644
--- a/tests/unit_tests/_transformers/test_auto_model.py
+++ b/tests/unit_tests/_transformers/test_auto_model.py
@@ -182,6 +182,119 @@ def test_get_next_fallback_attn_edge_cases(self):
         assert _get_next_fallback_attn("0") == "eager"
 
 
+class TestPatchLegacyFlashAttnFlag:
+    """Bridge the legacy ``_supports_flash_attn_2`` flag to v5.5's ``_supports_flash_attn``.
+
+    transformers v5.5 renamed the FA2-support attribute and switched the dispatch
+    check to the new name only. Remote-code models pinned against <=v5.3 still set
+    the legacy flag; the patch installs a fallback property so they dispatch to FA2.
+    """
+
+    def test_installs_property_on_base(self):
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+        assert isinstance(mu.PreTrainedModel.__dict__["_supports_flash_attn"], property)
+
+    def test_is_idempotent(self):
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+        prop1 = mu.PreTrainedModel.__dict__["_supports_flash_attn"]
+        _patch_legacy_flash_attn_flag()
+        prop2 = mu.PreTrainedModel.__dict__["_supports_flash_attn"]
+        assert prop1 is prop2
+
+    def test_legacy_flag_bridged_to_true(self):
+        """Subclass with only ``_supports_flash_attn_2 = True`` resolves to True."""
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+
+        class _Legacy(mu.PreTrainedModel):
+            _supports_flash_attn_2 = True
+
+        assert _Legacy.__new__(_Legacy)._supports_flash_attn is True
+
+    def test_explicit_new_flag_true_wins(self):
+        """Subclass that sets ``_supports_flash_attn = True`` directly shadows the property."""
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+
+        class _Native(mu.PreTrainedModel):
+            _supports_flash_attn = True
+
+        assert _Native.__new__(_Native)._supports_flash_attn is True
+
+    def test_explicit_new_flag_false_wins_over_legacy_true(self):
+        """Explicit ``_supports_flash_attn = False`` shadows a legacy True."""
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+
+        class _Native(mu.PreTrainedModel):
+            _supports_flash_attn = False
+            _supports_flash_attn_2 = True
+
+        assert _Native.__new__(_Native)._supports_flash_attn is False
+
+    def test_neither_flag_falls_back_to_base_default(self):
+        """Subclass with neither flag falls back to the captured base default (False)."""
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+
+        class _Bare(mu.PreTrainedModel):
+            pass
+
+        assert _Bare.__new__(_Bare)._supports_flash_attn is False
+
+    def test_legacy_flag_false_does_not_bridge(self):
+        """Only ``_supports_flash_attn_2 is True`` bridges; False passes through."""
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+
+        class _LegacyFalse(mu.PreTrainedModel):
+            _supports_flash_attn_2 = False
+
+        assert _LegacyFalse.__new__(_LegacyFalse)._supports_flash_attn is False
+
+    def test_nearest_subclass_wins_in_mro(self):
+        """In multi-level inheritance, the nearest ``_supports_flash_attn`` in MRO wins."""
+        import transformers.modeling_utils as mu
+
+        from nemo_automodel._transformers.kernel_patches import _patch_legacy_flash_attn_flag
+
+        _patch_legacy_flash_attn_flag()
+
+        class _Ancestor(mu.PreTrainedModel):
+            _supports_flash_attn_2 = True
+
+        class _Mid(_Ancestor):
+            _supports_flash_attn = False
+
+        class _Leaf(_Mid):
+            pass
+
+        assert _Leaf.__new__(_Leaf)._supports_flash_attn is False
+
+
 class DummyModel(torch.nn.Module):
     """A tiny nn.Module that behaves enough like a HF/BERT style model."""
 
diff --git a/tests/unit_tests/models/qwen3_5_moe/test_cp_linear_attn.py b/tests/unit_tests/models/qwen3_5_moe/test_cp_linear_attn.py
index 748a65b988..8e1e3fdf8c 100644
--- a/tests/unit_tests/models/qwen3_5_moe/test_cp_linear_attn.py
+++ b/tests/unit_tests/models/qwen3_5_moe/test_cp_linear_attn.py
@@ -302,6 +302,68 @@ def test_cp_mesh_gt_1_calls_forward_with_cp(self, module, device):
             mock_cp_fwd.assert_called_once()
 
 
+class TestForwardNoCpV55CacheAPI:
+    """_forward_no_cp must use the transformers v5.5 per-layer cache API.
+
+    v5.5 renamed ``has_previous_state`` to a method taking ``layer_idx``, moved
+    states under ``cache.layers[layer_idx]``, and exposes ``update_conv_state`` /
+    ``update_recurrent_state`` methods instead of direct dict assignment. A plain
+    ``DynamicCache`` (no pre-existing state, as in training) has no top-level
+    ``conv_states`` attribute — the pre-v5.5 pattern raised ``AttributeError``.
+    """
+
+    def test_training_cache_no_previous_state_runs(self, module, text_config, device):
+        """Training-style forward with a fresh DynamicCache (no previous state) must not raise."""
+        from transformers import DynamicCache
+
+        B, S, D = 1, 8, module.hidden_size
+        hidden = torch.randn(B, S, D, device=device)
+        out = module._forward_no_cp(hidden, cache_params=DynamicCache(config=text_config))
+        assert out.shape == (B, S, D)
+
+    def test_no_cache_path_still_works(self, module, device):
+        """When cache_params is None, _forward_no_cp runs the pure compute path."""
+        B, S, D = 1, 8, module.hidden_size
+        hidden = torch.randn(B, S, D, device=device)
+        out = module._forward_no_cp(hidden, cache_params=None)
+        assert out.shape == (B, S, D)
+
+    def test_updates_conv_state_via_method(self, module, text_config, device):
+        """Prefill writes the conv state via ``update_conv_state(state, layer_idx)``."""
+        from transformers import DynamicCache
+
+        B, S, D = 1, 8, module.hidden_size
+        hidden = torch.randn(B, S, D, device=device)
+        cache = DynamicCache(config=text_config)
+        with (
+            patch.object(cache, "update_conv_state", wraps=cache.update_conv_state) as mock_update_conv,
+            patch.object(cache, "update_recurrent_state", wraps=cache.update_recurrent_state) as mock_update_rec,
+        ):
+            module._forward_no_cp(hidden, cache_params=cache)
+        mock_update_conv.assert_called_once()
+        # Written at the layer_idx owned by the module.
+        args, _ = mock_update_conv.call_args
+        assert args[1] == module.layer_idx
+        mock_update_rec.assert_called_once()
+
+    def test_has_previous_state_called_as_method_with_layer_idx(self, module, text_config, device):
+        """v5.5 ``has_previous_state`` is a method that takes ``layer_idx``."""
+        from transformers import DynamicCache
+
+        B, S, D = 1, 8, module.hidden_size
+        hidden = torch.randn(B, S, D, device=device)
+        cache = DynamicCache(config=text_config)
+        with patch.object(cache, "has_previous_state", wraps=cache.has_previous_state) as mock_hps:
+            module._forward_no_cp(hidden, cache_params=cache)
+        mock_hps.assert_called()
+        # At least one call must pass the module's layer_idx.
+        layer_idx_seen = any(
+            (call.args and call.args[0] == module.layer_idx) or call.kwargs.get("layer_idx") == module.layer_idx
+            for call in mock_hps.call_args_list
+        )
+        assert layer_idx_seen
+
+
 # ============================================================================
 # _conv1d_with_cp
 # ============================================================================