diff --git a/src/openlayer/lib/core/base_model.py b/src/openlayer/lib/core/base_model.py
index e847e2bd..c69fabcb 100644
--- a/src/openlayer/lib/core/base_model.py
+++ b/src/openlayer/lib/core/base_model.py
@@ -39,6 +39,8 @@ class OpenlayerModel(abc.ABC):
     Refer to Openlayer's templates for examples of how to implement this class.
     """
 
+    custom_args: dict = {}
+
     def run_from_cli(self) -> None:
         """Run the model from the command line."""
         parser = argparse.ArgumentParser(description="Run data through a model.")
@@ -51,10 +53,26 @@ def run_from_cli(self) -> None:
             required=False,
             help="Directory to dump the results in",
         )
+        parser.add_argument(
+            "--custom-args",
+            type=str,
+            required=False,
+            help="Custom arguments in format 'key1=value1,key2=value2'",
+        )
 
         # Parse the arguments
         args = parser.parse_args()
 
+        # Parse custom arguments string
+        custom_args = {}
+        if args.custom_args:
+            pairs = args.custom_args.split(",")
+            for pair in pairs:
+                if "=" in pair:
+                    key, value = pair.split("=", 1)
+                    custom_args[key] = value
+        self.custom_args = custom_args
+
         return self.batch(
             dataset_path=args.dataset_path,
             output_dir=args.output_dir,
@@ -69,12 +87,16 @@ def batch(self, dataset_path: str, output_dir: str) -> None:
         elif dataset_path.endswith(".json"):
             df = pd.read_json(dataset_path, orient="records")
             fmt = "json"
+        else:
+            raise ValueError(f"Unsupported dataset format: {dataset_path}")
 
         # Call the model's run_batch method, passing in the DataFrame
-        output_df, config = self.run_batch_from_df(df)
+        output_df, config = self.run_batch_from_df(df, custom_args=self.custom_args)
         self.write_output_to_directory(output_df, config, output_dir, fmt)
 
-    def run_batch_from_df(self, df: pd.DataFrame) -> Tuple[pd.DataFrame, dict]:
+    def run_batch_from_df(
+        self, df: pd.DataFrame, custom_args: dict = None
+    ) -> Tuple[pd.DataFrame, dict]:
         """Function that runs the model and returns the result."""
         # Ensure the 'output' column exists
         if "output" not in df.columns:
@@ -83,6 +105,10 @@ def run_batch_from_df(self, df: pd.DataFrame) -> Tuple[pd.DataFrame, dict]:
         # Get the signature of the 'run' method
         run_signature = inspect.signature(self.run)
 
+        # If the model has a custom_args attribute, update it
+        if hasattr(self, "custom_args") and custom_args is not None:
+            self.custom_args.update(custom_args)
+
         for index, row in df.iterrows():
             # Filter row_dict to only include keys that are valid parameters
             # for the 'run' method
@@ -112,8 +138,7 @@ def run_batch_from_df(self, df: pd.DataFrame) -> Tuple[pd.DataFrame, dict]:
                 if "tokens" in processed_trace:
                     df.at[index, "tokens"] = processed_trace["tokens"]
                 if "context" in processed_trace:
-                    # Convert the context list to a string to avoid pandas issues
-                    df.at[index, "context"] = json.dumps(processed_trace["context"])
+                    df.at[index, "context"] = processed_trace["context"]
 
         config = {
             "outputColumnName": "output",
@@ -132,6 +157,9 @@ def run_batch_from_df(self, df: pd.DataFrame) -> Tuple[pd.DataFrame, dict]:
         if "context" in df.columns:
             config["contextColumnName"] = "context"
 
+        for k, v in self.custom_args.items():
+            config["metadata"][k] = v
+
         return df, config
 
     def write_output_to_directory(