diff --git a/pretrain/scripts/v4-corpus-ratio-abci/merge/merge.py b/pretrain/scripts/v4-corpus-ratio-abci/merge/merge.py
index a63c6d00..7f8e0852 100644
--- a/pretrain/scripts/v4-corpus-ratio-abci/merge/merge.py
+++ b/pretrain/scripts/v4-corpus-ratio-abci/merge/merge.py
@@ -1,9 +1,11 @@
 # Script to apply average merging to Hugging Face models.
 # This script can work with the pretrain Python environment.
-# 
+#
 # Usage:
 #     python merge.py \
 #         --source-models /path/to/model1 /path/to/model2 \
+#         --source-weights 1.0 2.0 \  # Optional
+#         --aggregation-method average \  # Optional,
 #         --output-model /path/to/output_model
 
 import argparse
@@ -31,6 +33,25 @@ def parse_args():
             " All models should be in the same format and have compatible parameters."
         ),
     )
+    p.add_argument(
+        "--source-weights",
+        type=float,
+        nargs="+",
+        default=None,
+        help=(
+            "Weights for each source model. If not provided, "
+            "all models will be treated equally (weight = 1)."
+        ),
+    )
+    p.add_argument(
+        "--aggregation-method",
+        choices=["average", "sum"],
+        default="average",
+        help=(
+            "Method to aggregate parameters. "
+            "'average' will average the parameters, while 'sum' will sum them up."
+        ),
+    )
     p.add_argument(
         "--output-model",
         type=pathlib.Path,
@@ -44,7 +65,7 @@ def parse_args():
 def iter_params(model_path: pathlib.Path) -> tuple[str, torch.Tensor]:
     """
     Iterate through the parameters of a model stored in a .safetensors file.
-    
+
     Args:
         model_path (pathlib.Path): Path to the .safetensors file.
 
@@ -62,17 +83,34 @@ def iter_params(model_path: pathlib.Path) -> tuple[str, torch.Tensor]:
 def main():
     args = parse_args()
 
+    logging.info(f"Source models: {args.source_models}")
+
+    if args.source_weights is not None:
+        if len(args.source_weights) != len(args.source_models):
+            raise ValueError("Number of source weights must match number of source models.")
+    else:
+        args.source_weights = [1.0] * len(args.source_models)
+
+    logging.info(f"Source weights: {args.source_weights}")
+
+    match args.aggregation_method:
+        case "average":
+            if any(x <= 0 for x in args.source_weights):
+                raise ValueError("All source weights must be positive for --aggregation-method=average.")
+            denominator = sum(args.source_weights)
+        case "sum":
+            denominator = 1.0
+        case _:
+            raise ValueError(f"Unknown aggregation method: {args.aggregation_method}")
+
+    logging.info(f"Aggregation method: {args.aggregation_method}, denominator: {denominator}")
+
     # Initialize a dictionary to hold the sum of parameters
     param_sums = {}
     model_count = len(args.source_models)
-    
-    if model_count == 0:
-        raise ValueError("No input models provided for merging.")
-
-    logging.info(f"Source models: {args.source_models}")
 
     # Iterate through each model and accumulate the parameters
-    for model_path in args.source_models:
+    for model_path, weight in zip(args.source_models, args.source_weights):
         if not model_path.exists():
             raise FileNotFoundError(f"Model path {model_path} does not exist.")
         if not model_path.is_dir():
@@ -81,25 +119,25 @@ def main():
 
         for key, tensor in iter_params(model_path):
             if key not in param_sums:
-                param_sums[key] = tensor
+                param_sums[key] = tensor * weight
             else:
                 if param_sums[key].shape != tensor.shape:
                     raise ValueError(f"Shape mismatch for key '{key}': "
                                      f"{param_sums[key].shape} vs {tensor.shape}")
-                param_sums[key] += tensor
-    
-    # Average the parameters
+                param_sums[key] += tensor * weight
+
+    # Normalize the parameters
     for key in param_sums:
-        param_sums[key] /= model_count
+        param_sums[key] /= denominator
 
     logging.info("Merging completed. Saving the merged model...")
     args.output_model.mkdir(parents=True, exist_ok=True)
-    
+
     # Copy original files other than .safetensors
     for file in args.source_models[0].iterdir():
         if file.suffix != ".safetensors":
             shutil.copy(file, args.output_model / file.name)
-    
+
     # There should be `model.safetensors.index.json` file
     # containing the mapping of parameter names to their destination file names.
     index_file = args.output_model / "model.safetensors.index.json"
@@ -107,16 +145,16 @@ def main():
         raise FileNotFoundError(f"Index file {index_file} does not exist.")
     with index_file.open("r") as f:
         weight_map = json.load(f)["weight_map"]
-    
+
     # Check if the weight map is consistent with the parameters
     if set(weight_map.keys()) != set(param_sums.keys()):
         raise ValueError("Weight map keys do not match the parameter keys.")
-    
+
     # Make inverse mapping for saving
     output_map = {k: [] for k in set(weight_map.values())}
     for k, v in weight_map.items():
         output_map[v].append(k)
-    
+
     metadata = {"format": "pt"}
 
     # Save all parameters
@@ -125,7 +163,7 @@ def main():
         output_path = args.output_model / file_name
         logging.info(f"  Saving parameters to {output_path}")
         safetensors.torch.save_file(tensors, output_path, metadata=metadata)
-    
+
     logging.info("Merged model saved successfully.")