diff --git a/llms/mlx_lm/tuner/utils.py b/llms/mlx_lm/tuner/utils.py
index 993f216a..079011f0 100644
--- a/llms/mlx_lm/tuner/utils.py
+++ b/llms/mlx_lm/tuner/utils.py
@@ -1,10 +1,11 @@
 import mlx.core as mx
+import mlx.nn as nn
 from mlx.utils import tree_unflatten
 
 from .lora import LoRALinear
 
 
-def apply_lora_layers(model, adapter_file: str):
+def apply_lora_layers(model: nn.Module, adapter_file: str) -> nn.Module:
     adapters = list(mx.load(adapter_file).items())
     linear_replacements = {}
     lora_layers = set(
diff --git a/llms/mlx_lm/utils.py b/llms/mlx_lm/utils.py
index 522208c1..4a53ee9d 100644
--- a/llms/mlx_lm/utils.py
+++ b/llms/mlx_lm/utils.py
@@ -13,6 +13,7 @@ from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer
 
 # Local imports
 from .models import llama, mixtral, phi2, plamo, qwen
+from .tuner.utils import apply_lora_layers
 
 # Constants
 MODEL_MAPPING = {
@@ -98,11 +99,14 @@ def generate_step(
     """
 
     def sample(logits: mx.array) -> Tuple[mx.array, float]:
+        softmax_logits = mx.softmax(logits)
+
         if temp == 0:
             token = mx.argmax(logits, axis=-1)
         else:
             token = mx.random.categorical(logits * (1 / temp))
-            prob = mx.softmax(logits / temp)[0, token]
+
+        prob = softmax_logits[0, token]
         return token, prob
 
     y = prompt
@@ -237,7 +241,7 @@ def load_model(model_path: Path) -> nn.Module:
 
 
 def load(
-    path_or_hf_repo: str, tokenizer_config={}
+    path_or_hf_repo: str, tokenizer_config={}, adapter_file: str = None
 ) -> Tuple[nn.Module, PreTrainedTokenizer]:
     """
     Load the model and tokenizer from a given path or a huggingface repository.
@@ -246,8 +250,10 @@ def load(
         model_path (Path): The path or the huggingface repository to load the model from.
         tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer.
             Defaults to an empty dictionary.
+        adapter_file (str, optional): Path to the adapter file. If provided, applies LoRA layers to the model.
+            Defaults to None.
     Returns:
-        nn.Module: The loaded model.
+        Tuple[nn.Module, PreTrainedTokenizer]: A tuple containing the loaded model and tokenizer.
 
     Raises:
         FileNotFoundError: If config file or safetensors are not found.
@@ -256,6 +262,9 @@ def load(
     model_path = get_model_path(path_or_hf_repo)
 
     model = load_model(model_path)
+    if adapter_file is not None:
+        model = apply_lora_layers(model, adapter_file)
+
     tokenizer = AutoTokenizer.from_pretrained(model_path, **tokenizer_config)
     return model, tokenizer