diff --git a/setup.py b/setup.py
index 373cfeea06..765b65a66a 100644
--- a/setup.py
+++ b/setup.py
@@ -71,15 +71,15 @@
 EXTRAS = {
     "test": [
         "parameterized",
+        "peft>=0.8.0",
         "pytest",
         "pytest-xdist",
-        "accelerate",
         "pytest-cov",
         "pytest-xdist",
         "scikit-learn",
         "Pillow",
     ],
-    "peft": ["peft>=0.4.0"],
+    "peft": ["peft>=0.8.0"],
     "diffusers": ["diffusers>=0.18.0"],
     "deepspeed": ["deepspeed>=0.9.5"],
     "benchmark": ["wandb", "ghapi", "openrlbenchmark==0.2.1a5", "requests", "deepspeed"],
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 5e5c3ec9c9..d2885f1d2b 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -2,7 +2,15 @@
 
 import torch
 
-from trl.trainer.utils import pad
+from trl import is_peft_available
+from trl.trainer.model_config import ModelConfig
+from trl.trainer.utils import get_peft_config, pad
+
+
+if is_peft_available():
+    from peft import LoraConfig
+
+from .testing_utils import require_peft
 
 
 class TestPad(unittest.TestCase):
@@ -55,3 +63,37 @@ def test_pad_2_dim_right_multidim(self):
             ]
         )
         self.assertTrue(torch.equal(output, expected))
+
+
+@require_peft
+class TestGetPEFTConfig(unittest.TestCase):
+    def test_create_peft_config_use_peft_false(self):
+        """Test that when use_peft is False, the function returns None."""
+        model_config = ModelConfig(use_peft=False)
+        peft_config = get_peft_config(model_config)
+        self.assertIsNone(peft_config)
+
+    def test_create_peft_config_use_peft_true(self):
+        """Test that when use_peft is True, the function returns a LoraConfig object."""
+        # Provide non-default values to the model config for testing
+        peft_kwargs = {
+            "lora_r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.1,
+            "lora_task_type": "SEQ_CLS",
+            "use_rslora": True,
+            "lora_target_modules": ["up_proj", "down_proj"],
+            "lora_modules_to_save": ["up_proj"],
+        }
+        model_config = ModelConfig(use_peft=True, **peft_kwargs)
+        peft_config = get_peft_config(model_config)
+        self.assertTrue(isinstance(peft_config, LoraConfig))
+        for arg, value in peft_kwargs.items():
+            # Test that lists of modules are converted to sets
+            if arg == "lora_target_modules":
+                value = set(value)
+            # Rename the argument to match the LoraConfig attribute name
+            if arg in ["lora_r", "lora_task_type", "lora_target_modules", "lora_modules_to_save"]:
+                arg = arg[len("lora_") :] if arg.startswith("lora_") else arg
+
+            self.assertEqual(getattr(peft_config, arg), value)
diff --git a/trl/trainer/model_config.py b/trl/trainer/model_config.py
index b16a07421d..bc0caf93d2 100644
--- a/trl/trainer/model_config.py
+++ b/trl/trainer/model_config.py
@@ -64,6 +64,15 @@ class ModelConfig:
     lora_task_type: str = field(
         default="CAUSAL_LM", metadata={"help": "The task_type to pass for LoRA (use SEQ_CLS for reward modeling)"}
     )
+    use_rslora: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Use Rank-Stabilized LoRA (https://huggingface.co/papers/2312.03732), which sets the adapter "
+                "scaling factor to lora_alpha/√r, instead of the original default value of `lora_alpha/r`."
+            )
+        },
+    )
     load_in_8bit: bool = field(
         default=False, metadata={"help": "use 8 bit precision for the base model - works only with LoRA"}
     )
diff --git a/trl/trainer/utils.py b/trl/trainer/utils.py
index 12744c6370..0fb88fbaca 100644
--- a/trl/trainer/utils.py
+++ b/trl/trainer/utils.py
@@ -811,12 +811,13 @@ def get_peft_config(model_config: ModelConfig) -> "Optional[PeftConfig]":
         )
 
     peft_config = LoraConfig(
+        task_type=model_config.lora_task_type,
         r=model_config.lora_r,
+        target_modules=model_config.lora_target_modules,
         lora_alpha=model_config.lora_alpha,
         lora_dropout=model_config.lora_dropout,
         bias="none",
-        task_type=model_config.lora_task_type,
-        target_modules=model_config.lora_target_modules,
+        use_rslora=model_config.use_rslora,
         modules_to_save=model_config.lora_modules_to_save,
     )