diff --git a/src/py/flwr/cli/new/new.py b/src/py/flwr/cli/new/new.py
index e7dbee894314..68dff311d7c3 100644
--- a/src/py/flwr/cli/new/new.py
+++ b/src/py/flwr/cli/new/new.py
@@ -240,6 +240,8 @@ def new(
             MlFramework.HUGGINGFACE.value,
             MlFramework.MLX.value,
             MlFramework.TENSORFLOW.value,
+            MlFramework.NUMPY.value,
+            MlFramework.SKLEARN.value,
         ]
         if framework_str in frameworks_with_tasks:
             files[f"{import_name}/task.py"] = {
diff --git a/src/py/flwr/cli/new/templates/app/code/client.jax.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.jax.py.tpl
index 046de57f3cf3..bc0c9ff66489 100644
--- a/src/py/flwr/cli/new/templates/app/code/client.jax.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/client.jax.py.tpl
@@ -17,37 +17,31 @@ from $import_name.task import (
 
 # Define Flower Client and client_fn
 class FlowerClient(NumPyClient):
-    def __init__(self):
+    def __init__(self, input_dim):
         self.train_x, self.train_y, self.test_x, self.test_y = load_data()
         self.grad_fn = jax.grad(loss_fn)
-        model_shape = self.train_x.shape[1:]
-
-        self.params = load_model(model_shape)
-
-    def get_parameters(self, config):
-        return get_params(self.params)
-
-    def set_parameters(self, parameters):
-        set_params(self.params, parameters)
+        self.params = load_model((input_dim,))
 
     def fit(self, parameters, config):
-        self.set_parameters(parameters)
+        set_params(self.params, parameters)
         self.params, loss, num_examples = train(
             self.params, self.grad_fn, self.train_x, self.train_y
         )
-        parameters = self.get_parameters(config={})
-        return parameters, num_examples, {"loss": float(loss)}
+        return get_params(self.params), num_examples, {"loss": float(loss)}
 
     def evaluate(self, parameters, config):
-        self.set_parameters(parameters)
+        set_params(self.params, parameters)
         loss, num_examples = evaluation(
             self.params, self.grad_fn, self.test_x, self.test_y
         )
         return float(loss), num_examples, {"loss": float(loss)}
 
 def client_fn(context: Context):
+
+    input_dim = context.run_config["input-dim"]
+
     # Return Client instance
-    return FlowerClient().to_client()
+    return FlowerClient(input_dim).to_client()
 
 
 # Flower ClientApp
diff --git a/src/py/flwr/cli/new/templates/app/code/client.mlx.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.mlx.py.tpl
index f3105103842d..dc677d6388d9 100644
--- a/src/py/flwr/cli/new/templates/app/code/client.mlx.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/client.mlx.py.tpl
@@ -4,7 +4,7 @@ import mlx.core as mx
 import mlx.nn as nn
 import mlx.optimizers as optim
 from flwr.client import NumPyClient, ClientApp
-from flwr.common import Context
+from flwr.common.config import UserConfig
 
 from $import_name.task import (
     batch_iterate,
@@ -22,37 +22,24 @@ class FlowerClient(NumPyClient):
     def __init__(
         self,
         data,
-        num_layers,
-        hidden_dim,
+        run_config: UserConfig,
         num_classes,
-        batch_size,
-        learning_rate,
-        num_epochs,
     ):
-        self.num_layers = num_layers
-        self.hidden_dim = hidden_dim
-        self.num_classes = num_classes
-        self.batch_size = batch_size
-        self.learning_rate = learning_rate
-        self.num_epochs = num_epochs
+        num_layers = run_config["num-layers"]
+        hidden_dim = run_config["hidden-dim"]
+        input_dim = run_config["input-dim"]
+        batch_size = run_config["batch-size"]
+        learning_rate = run_config["lr"]
+        self.num_epochs = run_config["local-epochs"]
 
         self.train_images, self.train_labels, self.test_images, self.test_labels = data
-        self.model = MLP(
-            num_layers, self.train_images.shape[-1], hidden_dim, num_classes
-        )
+        self.model = MLP(num_layers, input_dim, hidden_dim, num_classes)
         self.optimizer = optim.SGD(learning_rate=learning_rate)
         self.loss_and_grad_fn = nn.value_and_grad(self.model, loss_fn)
-        self.num_epochs = num_epochs
         self.batch_size = batch_size
 
-    def get_parameters(self, config):
-        return get_params(self.model)
-
-    def set_parameters(self, parameters):
-        set_params(self.model, parameters)
-
     def fit(self, parameters, config):
-        self.set_parameters(parameters)
+        set_params(self.model, parameters)
         for _ in range(self.num_epochs):
             for X, y in batch_iterate(
                 self.batch_size, self.train_images, self.train_labels
@@ -60,10 +47,10 @@ class FlowerClient(NumPyClient):
                 _, grads = self.loss_and_grad_fn(self.model, X, y)
                 self.optimizer.update(self.model, grads)
                 mx.eval(self.model.parameters(), self.optimizer.state)
-        return self.get_parameters(config={}), len(self.train_images), {}
+        return get_params(self.model), len(self.train_images), {}
 
     def evaluate(self, parameters, config):
-        self.set_parameters(parameters)
+        set_params(self.model, parameters)
         accuracy = eval_fn(self.model, self.test_images, self.test_labels)
         loss = loss_fn(self.model, self.test_images, self.test_labels)
         return loss.item(), len(self.test_images), {"accuracy": accuracy.item()}
@@ -73,19 +60,10 @@ def client_fn(context: Context):
     partition_id = context.node_config["partition-id"]
     num_partitions = context.node_config["num-partitions"]
     data = load_data(partition_id, num_partitions)
-
-    num_layers = context.run_config["num-layers"]
-    hidden_dim = context.run_config["hidden-dim"]
     num_classes = 10
-    batch_size = context.run_config["batch-size"]
-    learning_rate = context.run_config["lr"]
-    num_epochs = context.run_config["local-epochs"]
 
     # Return Client instance
-    return FlowerClient(
-        data, num_layers, hidden_dim, num_classes, batch_size, learning_rate, num_epochs
-    ).to_client()
-
+    return FlowerClient(data, context.run_config, num_classes).to_client()
 
 # Flower ClientApp
 app = ClientApp(
diff --git a/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
index e35c3c78f6e2..852b6a9daa70 100644
--- a/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
@@ -4,13 +4,14 @@ from flwr.client import NumPyClient, ClientApp
 from flwr.common import Context
 import numpy as np
 
+from $import_name.task import get_dummy_model
+
 
 class FlowerClient(NumPyClient):
-    def get_parameters(self, config):
-        return [np.ones((1, 1))]
 
     def fit(self, parameters, config):
-        return ([np.ones((1, 1))], 1, {})
+        model = get_dummy_model()
+        return ([model], 1, {})
 
     def evaluate(self, parameters, config):
         return float(0.0), 1, {"accuracy": float(1.0)}
diff --git a/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl
index 2d3d1c7f163a..e64e2e44e45a 100644
--- a/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/client.sklearn.py.tpl
@@ -2,41 +2,17 @@
 
 import warnings
 
-import numpy as np
 from flwr.client import NumPyClient, ClientApp
 from flwr.common import Context
-from flwr_datasets import FederatedDataset
-from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import log_loss
 
-
-def get_model_parameters(model):
-    if model.fit_intercept:
-        params = [
-            model.coef_,
-            model.intercept_,
-        ]
-    else:
-        params = [model.coef_]
-    return params
-
-
-def set_model_params(model, params):
-    model.coef_ = params[0]
-    if model.fit_intercept:
-        model.intercept_ = params[1]
-    return model
-
-
-def set_initial_params(model):
-    n_classes = 10  # MNIST has 10 classes
-    n_features = 784  # Number of features in dataset
-    model.classes_ = np.array([i for i in range(10)])
-
-    model.coef_ = np.zeros((n_classes, n_features))
-    if model.fit_intercept:
-        model.intercept_ = np.zeros((n_classes,))
-
+from $import_name.task import (
+    get_model,
+    get_model_params,
+    load_data,
+    set_initial_params,
+    set_model_params,
+)
 
 class FlowerClient(NumPyClient):
     def __init__(self, model, X_train, X_test, y_train, y_test):
@@ -46,9 +22,6 @@ class FlowerClient(NumPyClient):
         self.y_train = y_train
         self.y_test = y_test
 
-    def get_parameters(self, config):
-        return get_model_parameters(self.model)
-
     def fit(self, parameters, config):
         set_model_params(self.model, parameters)
 
@@ -57,7 +30,7 @@ class FlowerClient(NumPyClient):
             warnings.simplefilter("ignore")
             self.model.fit(self.X_train, self.y_train)
 
-        return get_model_parameters(self.model), len(self.X_train), {}
+        return get_model_params(self.model), len(self.X_train), {}
 
     def evaluate(self, parameters, config):
         set_model_params(self.model, parameters)
@@ -71,21 +44,13 @@ class FlowerClient(NumPyClient):
 def client_fn(context: Context):
     partition_id = context.node_config["partition-id"]
     num_partitions = context.node_config["num-partitions"]
-    fds = FederatedDataset(dataset="mnist", partitioners={"train": num_partitions})
-    dataset = fds.load_partition(partition_id, "train").with_format("numpy")
-
-    X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
 
-    # Split the on edge data: 80% train, 20% test
-    X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
-    y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
+    X_train, X_test, y_train, y_test = load_data(partition_id, num_partitions)
 
     # Create LogisticRegression Model
-    model = LogisticRegression(
-        penalty="l2",
-        max_iter=1,  # local epoch
-        warm_start=True,  # prevent refreshing weights when fitting
-    )
+    penalty = context.run_config["penalty"]
+    local_epochs = context.run_config["local-epochs"]
+    model = get_model(penalty, local_epochs)
 
     # Setting initial parameters, akin to model.compile for keras models
     set_initial_params(model)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.jax.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.jax.py.tpl
index 514185fde970..268e1a05d11a 100644
--- a/src/py/flwr/cli/new/templates/app/code/server.jax.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/server.jax.py.tpl
@@ -1,16 +1,22 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server.strategy import FedAvg
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
 
+from $import_name.task import get_params, load_model
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
+    input_dim = context.run_config["input-dim"]
+
+    # Initialize global model
+    params = get_params(load_model((input_dim,)))
+    initial_parameters = ndarrays_to_parameters(params)
 
     # Define strategy
-    strategy = FedAvg()
+    strategy = FedAvg(initial_parameters=initial_parameters)
     config = ServerConfig(num_rounds=num_rounds)
 
     return ServerAppComponents(strategy=strategy, config=config)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.mlx.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.mlx.py.tpl
index c99c72574813..9ab9e6dfa532 100644
--- a/src/py/flwr/cli/new/templates/app/code/server.mlx.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/server.mlx.py.tpl
@@ -1,16 +1,28 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
 from flwr.server.strategy import FedAvg
 
+from $import_name.task import get_params, MLP
+
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
 
+    num_classes = 10
+    num_layers = context.run_config["num-layers"]
+    input_dim = context.run_config["input-dim"]
+    hidden_dim = context.run_config["hidden-dim"]
+
+    # Initialize global model
+    model = MLP(num_layers, input_dim, hidden_dim, num_classes)
+    params = get_params(model)
+    initial_parameters = ndarrays_to_parameters(params)
+
     # Define strategy
-    strategy = FedAvg()
+    strategy = FedAvg(initial_parameters=initial_parameters)
     config = ServerConfig(num_rounds=num_rounds)
 
     return ServerAppComponents(strategy=strategy, config=config)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
index c99c72574813..c090bd52303a 100644
--- a/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
@@ -1,16 +1,22 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
 from flwr.server.strategy import FedAvg
 
+from $import_name.task import get_dummy_model
+
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
 
+    # Initial model
+    model = get_dummy_model()
+    dummy_parameters = ndarrays_to_parameters([model])
+
     # Define strategy
-    strategy = FedAvg()
+    strategy = FedAvg(initial_parameters=dummy_parameters)
     config = ServerConfig(num_rounds=num_rounds)
 
     return ServerAppComponents(strategy=strategy, config=config)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl
index 678ba9326229..97e60045aeb9 100644
--- a/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/server.sklearn.py.tpl
@@ -1,19 +1,32 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
 from flwr.server.strategy import FedAvg
 
+from $import_name.task import get_model, get_model_params, set_initial_params
+
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
 
+    # Create LogisticRegression Model
+    penalty = context.run_config["penalty"]
+    local_epochs = context.run_config["local-epochs"]
+    model = get_model(penalty, local_epochs)
+
+    # Setting initial parameters, akin to model.compile for keras models
+    set_initial_params(model)
+
+    initial_parameters = ndarrays_to_parameters(get_model_params(model))
+
     # Define strategy
     strategy = FedAvg(
         fraction_fit=1.0,
         fraction_evaluate=1.0,
         min_available_clients=2,
+        initial_parameters=initial_parameters,
     )
     config = ServerConfig(num_rounds=num_rounds)
 
diff --git a/src/py/flwr/cli/new/templates/app/code/task.numpy.py.tpl b/src/py/flwr/cli/new/templates/app/code/task.numpy.py.tpl
new file mode 100644
index 000000000000..afbf71d108fc
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/task.numpy.py.tpl
@@ -0,0 +1,7 @@
+"""$project_name: A Flower / $framework_str app."""
+
+import numpy as np
+
+def get_dummy_model():
+    return np.ones((1,1))
+
diff --git a/src/py/flwr/cli/new/templates/app/code/task.sklearn.py.tpl b/src/py/flwr/cli/new/templates/app/code/task.sklearn.py.tpl
new file mode 100644
index 000000000000..6a9d4d49b778
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/task.sklearn.py.tpl
@@ -0,0 +1,64 @@
+"""$project_name: A Flower / $framework_str app."""
+
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from flwr_datasets import FederatedDataset
+from flwr_datasets.partitioner import IidPartitioner
+
+
+fds = None  # Cache FederatedDataset
+
+
+def load_data(partition_id: int, num_partitions: int):
+    """Load partition MNIST data."""
+    # Only initialize `FederatedDataset` once
+    global fds
+    if fds is None:
+        partitioner = IidPartitioner(num_partitions=num_partitions)
+        fds = FederatedDataset(
+            dataset="mnist",
+            partitioners={"train": partitioner},
+            )
+
+    dataset = fds.load_partition(partition_id, "train").with_format("numpy")
+
+    X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
+
+    # Split the on edge data: 80% train, 20% test
+    X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
+    y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
+
+    return X_train, X_test, y_train, y_test
+
+def get_model(penalty: str, local_epochs: int):
+
+    return LogisticRegression(
+        penalty=penalty,
+        max_iter=local_epochs,
+        warm_start=True,
+    )
+
+def get_model_params(model):
+    if model.fit_intercept:
+        params = [
+            model.coef_,
+            model.intercept_,
+        ]
+    else:
+        params = [model.coef_]
+    return params
+
+def set_model_params(model, params):
+    model.coef_ = params[0]
+    if model.fit_intercept:
+        model.intercept_ = params[1]
+    return model
+
+def set_initial_params(model):
+    n_classes = 10  # MNIST has 10 classes
+    n_features = 784  # Number of features in dataset
+    model.classes_ = np.array([i for i in range(10)])
+
+    model.coef_ = np.zeros((n_classes, n_features))
+    if model.fit_intercept:
+        model.intercept_ = np.zeros((n_classes,))
\ No newline at end of file
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl
index 31fff1c2a4c8..36d65139edff 100644
--- a/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/pyproject.jax.toml.tpl
@@ -26,6 +26,7 @@ clientapp = "$import_name.client_app:app"
 
 [tool.flwr.app.config]
 num-server-rounds = 3
+input-dim = 3
 
 [tool.flwr.federations]
 default = "local-simulation"
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl
index c1bfe804c709..3830c4b3fb15 100644
--- a/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/pyproject.mlx.toml.tpl
@@ -28,6 +28,7 @@ clientapp = "$import_name.client_app:app"
 num-server-rounds = 3
 local-epochs = 1
 num-layers = 2
+input-dim = 784 # 28*28
 hidden-dim = 32
 batch-size = 256
 lr = 0.1
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl
index 2b5778fec9a7..c351531fda0f 100644
--- a/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl
+++ b/src/py/flwr/cli/new/templates/app/pyproject.sklearn.toml.tpl
@@ -25,6 +25,8 @@ clientapp = "$import_name.client_app:app"
 
 [tool.flwr.app.config]
 num-server-rounds = 3
+penalty = "l2"
+local-epochs = 1
 
 [tool.flwr.federations]
 default = "local-simulation"