diff --git a/examples/flowertune-llm/flowertune_llm/client_app.py b/examples/flowertune-llm/flowertune_llm/client_app.py
index 992b0f1a3e09..b61a733b29cf 100644
--- a/examples/flowertune-llm/flowertune_llm/client_app.py
+++ b/examples/flowertune-llm/flowertune_llm/client_app.py
@@ -3,7 +3,6 @@
 import os
 import warnings
 from typing import Dict, Tuple
-from collections import OrderedDict
 
 import torch
 from flwr.client import ClientApp, NumPyClient
@@ -12,7 +11,6 @@
 from flwr.common.typing import NDArrays, Scalar
 from omegaconf import DictConfig
 
-from peft import get_peft_model_state_dict, set_peft_model_state_dict
 from transformers import TrainingArguments
 from trl import SFTTrainer
 
@@ -24,6 +22,8 @@
 from flowertune_llm.models import (
     cosine_annealing,
     get_model,
+    set_parameters,
+    get_parameters,
 )
 
 # Avoid warnings
@@ -96,20 +96,6 @@ def fit(
         )
 
 
-def set_parameters(model, parameters: NDArrays) -> None:
-    """Change the parameters of the model using the given ones."""
-    peft_state_dict_keys = get_peft_model_state_dict(model).keys()
-    params_dict = zip(peft_state_dict_keys, parameters)
-    state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
-    set_peft_model_state_dict(model, state_dict)
-
-
-def get_parameters(model) -> NDArrays:
-    """Return the parameters of the current net."""
-    state_dict = get_peft_model_state_dict(model)
-    return [val.cpu().numpy() for _, val in state_dict.items()]
-
-
 def client_fn(context: Context) -> FlowerClient:
     """Create a Flower client representing a single organization."""
     partition_id = context.node_config["partition-id"]
diff --git a/examples/flowertune-llm/flowertune_llm/models.py b/examples/flowertune-llm/flowertune_llm/models.py
index 7d0c8f391687..e1609caeb2fc 100644
--- a/examples/flowertune-llm/flowertune_llm/models.py
+++ b/examples/flowertune-llm/flowertune_llm/models.py
@@ -2,13 +2,18 @@
 
 import torch
 from omegaconf import DictConfig
+from collections import OrderedDict
 from peft import (
     LoraConfig,
     get_peft_model,
+    get_peft_model_state_dict,
+    set_peft_model_state_dict,
 )
 from peft.utils import prepare_model_for_kbit_training
 from transformers import AutoModelForCausalLM, BitsAndBytesConfig
 
+from flwr.common.typing import NDArrays
+
 
 def cosine_annealing(
     current_round: int,
@@ -56,3 +61,17 @@ def get_model(model_cfg: DictConfig):
     )
 
     return get_peft_model(model, peft_config)
+
+
+def set_parameters(model, parameters: NDArrays) -> None:
+    """Change the parameters of the model using the given ones."""
+    peft_state_dict_keys = get_peft_model_state_dict(model).keys()
+    params_dict = zip(peft_state_dict_keys, parameters)
+    state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+    set_peft_model_state_dict(model, state_dict)
+
+
+def get_parameters(model) -> NDArrays:
+    """Return the parameters of the current net."""
+    state_dict = get_peft_model_state_dict(model)
+    return [val.cpu().numpy() for _, val in state_dict.items()]
diff --git a/examples/flowertune-llm/flowertune_llm/server_app.py b/examples/flowertune-llm/flowertune_llm/server_app.py
index 309166cc30a3..ff0da90c8b9b 100644
--- a/examples/flowertune-llm/flowertune_llm/server_app.py
+++ b/examples/flowertune-llm/flowertune_llm/server_app.py
@@ -9,9 +9,8 @@
 from flwr.server.strategy import FedAvg
 from omegaconf import DictConfig
 
-from flowertune_llm.models import get_model
+from flowertune_llm.models import get_model, get_parameters, set_parameters
 from flowertune_llm.dataset import replace_keys
-from flowertune_llm.client_app import get_parameters, set_parameters
 
 
 # Get function that will be executed by the strategy's evaluate() method
diff --git a/examples/flowertune-llm/pyproject.toml b/examples/flowertune-llm/pyproject.toml
index 20aa7267d9d5..5c057de2ea70 100644
--- a/examples/flowertune-llm/pyproject.toml
+++ b/examples/flowertune-llm/pyproject.toml
@@ -8,7 +8,7 @@ version = "1.0.0"
 description = "FlowerTune LLM: Federated LLM Fine-tuning with Flower"
 license = "Apache-2.0"
 dependencies = [
-    "flwr[simulation]==1.11.0",
+    "flwr[simulation]==1.11.1",
     "flwr-datasets>=0.3.0",
     "trl==0.8.1",
     "bitsandbytes==0.43.0",