diff --git a/src/axolotl/loaders/patch_manager.py b/src/axolotl/loaders/patch_manager.py
index 01d9997d7d..0de186ae18 100644
--- a/src/axolotl/loaders/patch_manager.py
+++ b/src/axolotl/loaders/patch_manager.py
@@ -313,6 +313,13 @@ def _apply_adapter_patches(self):
 
             patch_peft_prep_code()
 
+        if self.cfg.adapter:
+            from axolotl.monkeypatch.peft_modules_to_save import (
+                patch_peft_modules_to_save_kwargs,
+            )
+
+            patch_peft_modules_to_save_kwargs()
+
     def _apply_flex_attention_patches(self):
         """Apply patches for flexible attention."""
         if self.cfg.flex_attention:
diff --git a/src/axolotl/monkeypatch/peft_modules_to_save.py b/src/axolotl/monkeypatch/peft_modules_to_save.py
new file mode 100644
index 0000000000..e0727c524c
--- /dev/null
+++ b/src/axolotl/monkeypatch/peft_modules_to_save.py
@@ -0,0 +1,57 @@
+"""Patch PEFT's AuxiliaryTrainingWrapper / ModulesToSaveWrapper so kwargs-only
+forward calls work (e.g. Gemma 4 vision_tower / embed_vision in lora_modules_to_save)."""
+
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+_PATCHED_ATTR = "_axolotl_modules_to_save_kwargs_patched"
+
+
+def _patched_forward(self, *args, **kwargs):
+    # _check_forward_args only validates len(x) vs len(adapter_names); skip when no positional x.
+    if args:
+        self._check_forward_args(*args, **kwargs)
+
+    adapter_names = kwargs.pop("adapter_names", None)
+
+    if self.disable_adapters or any(
+        adapter not in self._adapters for adapter in self.active_adapters
+    ):
+        return self._forward_wrapped_passthrough(*args, **kwargs)
+
+    if adapter_names is None:
+        return self._forward_wrapped(*args, **kwargs)
+    # Mixed-batch path needs positional input for sub-batch indexing; leave unchanged.
+    return self._mixed_batch_forward(*args, adapter_names=adapter_names, **kwargs)
+
+
+def _patched_forward_wrapped(self, *args, **kwargs):
+    if not self.active_adapters:
+        return self._forward_wrapped_passthrough(*args, **kwargs)
+    return self.modules_to_save[self.active_adapters[0]](*args, **kwargs)
+
+
+def _patched_forward_wrapped_passthrough(self, *args, **kwargs):
+    return self.original_module(*args, **kwargs)
+
+
+def patch_peft_modules_to_save_kwargs() -> None:
+    """Apply the kwargs-compatible forward patch to PEFT. Idempotent."""
+    from peft.utils.other import AuxiliaryTrainingWrapper, ModulesToSaveWrapper
+
+    if getattr(AuxiliaryTrainingWrapper, _PATCHED_ATTR, False):
+        return
+
+    AuxiliaryTrainingWrapper.forward = _patched_forward
+    ModulesToSaveWrapper._forward_wrapped = _patched_forward_wrapped
+    ModulesToSaveWrapper._forward_wrapped_passthrough = (
+        _patched_forward_wrapped_passthrough
+    )
+
+    setattr(AuxiliaryTrainingWrapper, _PATCHED_ATTR, True)
+    LOG.debug(
+        "Patched peft.AuxiliaryTrainingWrapper / ModulesToSaveWrapper to accept "
+        "kwargs-only forward calls (enables full-FT of modules called with "
+        "keyword args, e.g. Gemma 4 vision_tower/embed_vision)"
+    )
diff --git a/tests/monkeypatch/test_peft_modules_to_save.py b/tests/monkeypatch/test_peft_modules_to_save.py
new file mode 100644
index 0000000000..d4f38b7569
--- /dev/null
+++ b/tests/monkeypatch/test_peft_modules_to_save.py
@@ -0,0 +1,148 @@
+"""Tests for axolotl.monkeypatch.peft_modules_to_save."""
+
+import pytest
+import torch
+import torch.nn as nn
+
+pytest.importorskip("peft")
+
+
+@pytest.fixture(scope="module")
+def patched():
+    from axolotl.monkeypatch.peft_modules_to_save import (
+        patch_peft_modules_to_save_kwargs,
+    )
+
+    patch_peft_modules_to_save_kwargs()
+    yield
+
+
+@pytest.fixture
+def wrap_module(patched):
+    """Wrap ``module`` like PEFT wraps a ``modules_to_save`` entry."""
+    from peft.utils.other import ModulesToSaveWrapper
+
+    def _wrap(module: nn.Module) -> ModulesToSaveWrapper:
+        w = ModulesToSaveWrapper(module, "default")
+        w.set_adapter("default")
+        return w
+
+    return _wrap
+
+
+def test_kwargs_only_forward(wrap_module):
+    """Gemma 4 vision_tower shape: self.vision_tower(pixel_values=...)."""
+
+    class KwargsOnly(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = nn.Linear(8, 8)
+
+        def forward(self, *, pixel_values):
+            return self.lin(pixel_values)
+
+    w = wrap_module(KwargsOnly())
+    out = w(pixel_values=torch.randn(2, 8))
+    assert out.shape == (2, 8)
+
+
+def test_positional_only_forward(wrap_module):
+    """embed_tokens shape: self.embed_tokens(input_ids)."""
+
+    class PosOnly(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.emb = nn.Embedding(100, 8)
+
+        def forward(self, x):
+            return self.emb(x)
+
+    w = wrap_module(PosOnly())
+    out = w(torch.tensor([1, 2, 3]))
+    assert out.shape == (3, 8)
+
+
+def test_mixed_args_kwargs_forward(wrap_module):
+    """Module taking both positional and keyword args."""
+
+    class Mixed(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = nn.Linear(8, 8)
+
+        def forward(self, x, scale=1.0):
+            return self.lin(x) * scale
+
+    w = wrap_module(Mixed())
+    out = w(torch.randn(2, 8), scale=2.0)
+    assert out.shape == (2, 8)
+
+
+def test_kwargs_only_disabled_adapter(wrap_module):
+    """Passthrough branch (enable_adapters(False)) must also accept kwargs-only."""
+
+    class KwargsOnly(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = nn.Linear(8, 8)
+
+        def forward(self, *, pixel_values):
+            return self.lin(pixel_values)
+
+    w = wrap_module(KwargsOnly())
+    w.enable_adapters(False)
+
+    out = w(pixel_values=torch.randn(2, 8))
+    assert out.shape == (2, 8)
+
+
+def test_trainable_tokens_wrapper_not_broken(patched):
+    """Base-class forward replacement must not break TrainableTokensWrapper
+    (positional embed_tokens path must still work)."""
+    try:
+        from peft.utils.other import TrainableTokensWrapper
+    except ImportError:
+        pytest.skip("TrainableTokensWrapper not available in installed PEFT")
+
+    try:
+        w = TrainableTokensWrapper(
+            nn.Embedding(100, 8), "default", token_indices=[0, 1, 2]
+        )
+    except TypeError as exc:
+        pytest.skip(f"TrainableTokensWrapper init signature changed: {exc}")
+    w.set_adapter("default")
+
+    out = w(torch.tensor([0, 1, 2, 5]))
+    assert out.shape == (4, 8)
+
+
+def test_mixed_batch_kwargs_only_raises(wrap_module):
+    """Document current behavior: adapter_names without positional input is
+    unsupported (mixed-batch path is deliberately not kwargs-patched)."""
+
+    class KwargsOnly(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.lin = nn.Linear(8, 8)
+
+        def forward(self, *, pixel_values):
+            return self.lin(pixel_values)
+
+    w = wrap_module(KwargsOnly())
+    with pytest.raises(TypeError):
+        w(pixel_values=torch.randn(2, 8), adapter_names=["default", "default"])
+
+
+def test_patch_is_idempotent():
+    """Applying the patch twice is a no-op."""
+    from peft.utils.other import AuxiliaryTrainingWrapper
+
+    from axolotl.monkeypatch.peft_modules_to_save import (
+        patch_peft_modules_to_save_kwargs,
+    )
+
+    patch_peft_modules_to_save_kwargs()
+    first = AuxiliaryTrainingWrapper.forward
+    patch_peft_modules_to_save_kwargs()
+    second = AuxiliaryTrainingWrapper.forward
+    assert first is second, "patch must be idempotent"