diff --git a/README.md b/README.md
index 55cbe4f..0c0530a 100644
--- a/README.md
+++ b/README.md
@@ -249,6 +249,7 @@ xe-forge-skill profile kernel.py --spec spec.yaml
 ## Writing the Model Class
 
 Every kernel file must contain a `Model` class that wraps the Triton kernel launch. The optimizer uses this class to execute, benchmark, and verify correctness.
+The `Model` class can include custom initialization and an optional `get_example_inputs` method to provide a complex combination of inputs if they cannot be generated randomly by their shapes and a common dtype.
 
 ### Structure
 
@@ -298,6 +299,13 @@ class Model(torch.nn.Module):
             num_warps=32,
         )
         return OUT
+
+      # Optional
+      # If this method is provided, the Xe-Forge will get input tensors by this method.
+      # If not, Xe-Forge will generate random inputs based on shapes and dtype.
+      # Don't provide this method (remove it) if you don't need it.
+      def get_example_inputs(self, input_shapes: list | None = None, device: str = 'xpu'):
+        pass
 ```
 
 ### Model with Init Arguments
diff --git a/src/xe_forge/core/executor.py b/src/xe_forge/core/executor.py
index 8debed4..b0fb327 100644
--- a/src/xe_forge/core/executor.py
+++ b/src/xe_forge/core/executor.py
@@ -27,6 +27,11 @@
 logger = logging.getLogger(__name__)
 
 
+def _has_callable_attr(obj, attr_name):
+    """Check if object has a callable attribute with the given name."""
+    return hasattr(obj, attr_name) and callable(getattr(obj, attr_name))
+
+
 @dataclass
 class ComparisonResult:
     """Result of comparing original vs optimized kernel performance."""
@@ -153,7 +158,9 @@ def execute(
 
             # Create inputs if not provided
             if inputs is None:
-                if input_shapes:
+                if _has_callable_attr(model, "get_example_inputs"):
+                    inputs = model.get_example_inputs(input_shapes, self.device)
+                elif input_shapes:
                     inputs = self._create_inputs(
                         input_shapes, dtype=dtype, input_dtypes=input_dtypes
                     )
@@ -302,7 +309,10 @@ def _check_correctness(
 
             # Shared inputs with deterministic seed
             set_all_seeds(123)
-            inputs = self._create_inputs(input_shapes, dtype=dtype, input_dtypes=input_dtypes)
+            if _has_callable_attr(original_model, "get_example_inputs"):
+                inputs = original_model.get_example_inputs(input_shapes, self.device)
+            else:
+                inputs = self._create_inputs(input_shapes, dtype=dtype, input_dtypes=input_dtypes)
 
             inputs_orig = [inp.clone() for inp in inputs]
             inputs_opt = [inp.clone() for inp in inputs]
diff --git a/src/xe_forge/core/spec_loader.py b/src/xe_forge/core/spec_loader.py
index 53d76a9..f407ff2 100644
--- a/src/xe_forge/core/spec_loader.py
+++ b/src/xe_forge/core/spec_loader.py
@@ -234,8 +234,8 @@ def get_flop(
 
         # Substitute dimension values into formula, then evaluate via ai_bench
         formula = str(variant.flop_formula)
-        for dim, value in variant.dims.items():
-            formula = formula.replace(dim, str(value))
+        for key in sorted(variant.dims.keys(), key=len, reverse=True):
+            formula = formula.replace(key, str(variant.dims[key]))
         return eval_eq(formula)
 
     def get_rtol(