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..ffe782d274fc 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
@@ -1,9 +1,9 @@
 """$project_name: A Flower / $framework_str app."""
 
 import jax
-from flwr.client import NumPyClient, ClientApp
-from flwr.common import Context
 
+from flwr.client import ClientApp, NumPyClient
+from flwr.common import Context
 from $import_name.task import (
     evaluation,
     get_params,
@@ -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/server.jax.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.jax.py.tpl
index 514185fde970..60bbcaf3c175 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.server.strategy import FedAvg
+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, 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/task.jax.py.tpl b/src/py/flwr/cli/new/templates/app/code/task.jax.py.tpl
index fc6ef9dee3dd..428f752845c1 100644
--- a/src/py/flwr/cli/new/templates/app/code/task.jax.py.tpl
+++ b/src/py/flwr/cli/new/templates/app/code/task.jax.py.tpl
@@ -2,9 +2,9 @@
 
 import jax
 import jax.numpy as jnp
+import numpy as np
 from sklearn.datasets import make_regression
 from sklearn.model_selection import train_test_split
-import numpy as np
 
 key = jax.random.PRNGKey(0)
 
@@ -33,7 +33,7 @@ def train(params, grad_fn, X, y):
     num_examples = X.shape[0]
     for epochs in range(50):
         grads = grad_fn(params, X, y)
-        params = jax.tree_map(lambda p, g: p - 0.05 * g, params, grads)
+        params = jax.tree.map(lambda p, g: p - 0.05 * g, params, grads)
         loss = loss_fn(params, X, y)
     return params, loss, num_examples
 
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..28cbc5bbb527 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
@@ -9,8 +9,8 @@ description = ""
 license = "Apache-2.0"
 dependencies = [
     "flwr[simulation]>=1.10.0",
-    "jax==0.4.13",
-    "jaxlib==0.4.13",
+    "jax==0.4.30",
+    "jaxlib==0.4.30",
     "scikit-learn==1.3.2",
 ]
 
@@ -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"