fix: try match target param properly end with

src/axolotl/loaders/model.py

@@ -177,9 +177,14 @@ class ModelLoader:
         # Quantize 3D MoE expert nn.Parameter tensors that BnB skips during loading.
         self.model._moe_experts_quantized = False
         if self.cfg.adapter in ("qlora", "lora") and self.cfg.load_in_4bit:
-            from axolotl.monkeypatch.moe_quant import quantize_moe_expert_params
+            from axolotl.monkeypatch.moe_quant import (
+                patch_peft_target_parameters_matching,
+                quantize_moe_expert_params,
+            )
 
             self.model._moe_experts_quantized = quantize_moe_expert_params(self.model)
+            if self.model._moe_experts_quantized:
+                patch_peft_target_parameters_matching()
 
         PLUGIN_MANAGER.post_model_build(self.cfg, self.model)
 

src/axolotl/monkeypatch/moe_quant.py

@@ -19,6 +19,48 @@ from axolotl.utils.logging import get_logger
 LOG = get_logger(__name__)
 
 
+def patch_peft_target_parameters_matching():
+    """Fix PEFT's _inject_parameters to use suffix matching for parametrized modules.
+
+    PEFT's parametrized-module branch uses exact name match for target_parameters,
+    but the standard branch uses endswith. This means suffix-style paths like
+    "mlp.experts.gate_up_proj" fail to match parametrized modules whose full path
+    is "model.layers.0.mlp.experts.gate_up_proj". This patch makes the parametrized
+    branch consistent with the standard branch.
+    """
+    from peft.tuners.tuners_utils import BaseTuner
+
+    original_inject = BaseTuner._inject_parameters
+
+    def _patched_inject_parameters(
+        self, peft_config, model, adapter_name, low_cpu_mem_usage
+    ):
+        # Patch target_parameters to use full paths for parametrized modules
+        original_targets = list(peft_config.target_parameters)
+        expanded = set(original_targets)
+
+        for module_name, module in model.named_modules():
+            if not hasattr(module, "parametrizations"):
+                continue
+            for target in original_targets:
+                mod_path, _, param_name = target.rpartition(".")
+                if (
+                    module_name == mod_path or module_name.endswith("." + mod_path)
+                ) and hasattr(module, param_name):
+                    expanded.add(f"{module_name}.{param_name}")
+
+        peft_config.target_parameters = sorted(expanded)
+        try:
+            return original_inject(
+                self, peft_config, model, adapter_name, low_cpu_mem_usage
+            )
+        finally:
+            peft_config.target_parameters = original_targets
+
+    BaseTuner._inject_parameters = _patched_inject_parameters
+    LOG.info("Patched PEFT _inject_parameters for parametrized module suffix matching")
+
+
 def find_unquantized_expert_params(model):
     """Find 3D+ nn.Parameter tensors that BnB quantization skipped.