diff --git a/llms/mlx_lm/tuner/trainer.py b/llms/mlx_lm/tuner/trainer.py
index 2bfc8142..e6571ee5 100644
--- a/llms/mlx_lm/tuner/trainer.py
+++ b/llms/mlx_lm/tuner/trainer.py
@@ -128,8 +128,8 @@ def train(
     loss: callable = default_loss,
 ):
     # Create checkpoints directory if it does not exist
-    if not os.path.exists('checkpoints'):
-        os.makedirs('checkpoints')
+    if not os.path.exists("checkpoints"):
+        os.makedirs("checkpoints")
 
     # Create value and grad function for loss
     loss_value_and_grad = nn.value_and_grad(model, loss)
diff --git a/llms/mlx_lm/tuner/utils.py b/llms/mlx_lm/tuner/utils.py
index b7fada8a..6756bfca 100644
--- a/llms/mlx_lm/tuner/utils.py
+++ b/llms/mlx_lm/tuner/utils.py
@@ -16,6 +16,14 @@ def linear_to_lora_layers(model: nn.Module, num_lora_layers: int):
         num_lora_layers (int): The number of blocks to convert to lora layers
         starting from the last layer.
     """
+
+    def check_lora_layers(num_model):
+        if num_lora_layers > num_model:
+            raise ValueError(
+                f"Requested {num_lora_layers} LoRA layers "
+                f"but the model only has {num_model_layers} layers."
+            )
+
     if model.model_type in [
         "mistral",
         "llama",
@@ -24,6 +32,8 @@ def linear_to_lora_layers(model: nn.Module, num_lora_layers: int):
         "stablelm_epoch",
         "qwen2",
     ]:
+        check_lora_layers(len(model.model.layers))
+
         for l in model.model.layers[len(model.model.layers) - num_lora_layers :]:
             l.self_attn.q_proj = LoRALinear.from_linear(l.self_attn.q_proj)
             l.self_attn.v_proj = LoRALinear.from_linear(l.self_attn.v_proj)
@@ -32,11 +42,15 @@ def linear_to_lora_layers(model: nn.Module, num_lora_layers: int):
                     l.block_sparse_moe.gate
                 )
     elif model.model_type == "olmo":
+        check_lora_layers(len(model.model.transformer.blocks))
+
         for l in model.model.transformer.blocks[
             len(model.model.transformer.blocks) - num_lora_layers :
         ]:
             l.att_proj = LoRALinear.from_linear(l.att_proj)
     elif model.model_type == "phi-msft":
+        check_lora_layers(len(model.transformer.h))
+
         for l in model.transformer.h[len(model.transformer.h) - num_lora_layers :]:
             l.mixer.Wqkv = LoRALinear.from_linear(l.mixer.Wqkv)
             l.moe.gate = LoRALinear.from_linear(l.moe.gate)
diff --git a/llms/setup.py b/llms/setup.py
index 00fd0e69..f34fbaf7 100644
--- a/llms/setup.py
+++ b/llms/setup.py
@@ -8,7 +8,7 @@ with open(Path(__file__).parent / "mlx_lm/requirements.txt") as fid:
     requirements = [str(r) for r in pkg_resources.parse_requirements(fid)]
 setup(
     name="mlx-lm",
-    version="0.0.8",
+    version="0.0.10",
     description="LLMs on Apple silicon with MLX and the Hugging Face Hub",
     long_description=open("README.md", encoding="utf-8").read(),
     long_description_content_type="text/markdown",