diff --git a/mqboost/__init__.py b/mqboost/__init__.py
index 2d6f82d..ee48a94 100644
--- a/mqboost/__init__.py
+++ b/mqboost/__init__.py
@@ -3,4 +3,4 @@
 from mqboost.optimize import MQOptimizer
 from mqboost.regressor import MQRegressor
 
-__version__ = "0.2.8"
+__version__ = "0.2.9"
diff --git a/mqboost/base.py b/mqboost/base.py
index 59585dc..827da11 100644
--- a/mqboost/base.py
+++ b/mqboost/base.py
@@ -29,6 +29,7 @@ def _isin(cls, text: str) -> None:
 ModelLike = lgb.basic.Booster | xgb.Booster
 DtrainLike = lgb.basic.Dataset | xgb.DMatrix
 ParamsLike = dict[str, float | int | str | bool]
+WeightLike = list[float] | list[int] | np.ndarray | pd.Series
 
 
 # Name
diff --git a/mqboost/dataset.py b/mqboost/dataset.py
index e43a5e9..3883dc5 100644
--- a/mqboost/dataset.py
+++ b/mqboost/dataset.py
@@ -1,5 +1,6 @@
 from typing import Callable, Optional
 
+import numpy as np
 import pandas as pd
 
 from mqboost.base import (
@@ -9,6 +10,7 @@
     FittingException,
     ModelName,
     TypeName,
+    WeightLike,
     XdataLike,
     YdataLike,
 )
@@ -32,6 +34,7 @@ class MQDataset:
             Must be in ascending order and contain no duplicates.
         data (pd.DataFrame | pd.Series | np.ndarray): The input features.
         label (pd.Series | np.ndarray): The target labels (if provided).
+        weight (list[float] | list[int] | np.ndarray | pd.Series): Weight for each instance (if provided).
         model (str): The model type (LightGBM or XGBoost).
         reference (MQBoost | None): Reference dataset for label encoding and label mean.
 
@@ -52,6 +55,7 @@ def __init__(
         alphas: AlphaLike,
         data: XdataLike,
         label: YdataLike | None = None,
+        weight: WeightLike | None = None,
         model: str = ModelName.lightgbm.value,
         reference: Optional["MQDataset"] = None,
     ) -> None:
@@ -85,6 +89,10 @@ def __init__(
             self._label = prepare_y(y=label - self._label_mean, alphas=self._alphas)
             self._is_none_label = False
 
+        if weight is not None:
+            _weight = np.array(weight) if not isinstance(weight, np.ndarray) else weight
+            self._weight = prepare_y(y=_weight, alphas=self._alphas)
+
     @property
     def train_dtype(self) -> Callable:
         """Get the data type function for training data."""
@@ -123,14 +131,20 @@ def label(self) -> pd.DataFrame:
 
     @property
     def label_mean(self) -> float:
+        """Get the label mean."""
         self.__label_available()
         return self._label_mean
 
+    @property
+    def weight(self) -> WeightLike | None:
+        """Get the weights."""
+        return getattr(self, "_weight", None)
+
     @property
     def dtrain(self) -> DtrainLike:
         """Get the training data in the required format for the model."""
         self.__label_available()
-        return self._train_dtype(data=self._data, label=self._label)
+        return self._train_dtype(data=self._data, label=self._label, weight=self.weight)
 
     @property
     def dpredict(self) -> DtrainLike | Callable:
diff --git a/mqboost/optimize.py b/mqboost/optimize.py
index ef800dc..de98dbe 100644
--- a/mqboost/optimize.py
+++ b/mqboost/optimize.py
@@ -200,6 +200,7 @@ def __optuna_objective(
                 params=params,
                 dtrain=dtrain,
                 evals=[(dvalid, "valid")],
+                num_boost_round=100,
             )
             _gbm = xgb.train(**model_params)
             _preds = _gbm.predict(data=deval) + self._label_mean
diff --git a/pyproject.toml b/pyproject.toml
index 0ad6d87..3aca39d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mqboost"
-version = "0.2.8"
+version = "0.2.9"
 description = "Monotonic composite quantile gradient boost regressor"
 authors = ["RektPunk <rektpunk@gmail.com>"]
 readme = "README.md"