diff --git a/textwiz/loader.py b/textwiz/loader.py
index 8e99731..b77f643 100644
--- a/textwiz/loader.py
+++ b/textwiz/loader.py
@@ -2,6 +2,7 @@
 import re
 import math
 import importlib.metadata
+import difflib
 from packaging import version
 
 import torch
@@ -34,6 +35,28 @@
 }
 
 
+
+def check_model_name(model_name: str, available_models: list[str] | tuple[str] = ALLOWED_MODELS):
+    """Ensure that the `model_name` is valid, and raise an error trying to find closest model otherwise.
+
+    Parameters
+    ----------
+    model_name : str
+        The model name.
+    available_models : list[str] | tuple[str], optional
+        List of available models to check for closest match. By default check all models.
+    """
+
+    if model_name not in available_models:
+        closest_match = difflib.get_close_matches(model_name, available_models, n=1)
+        if len(closest_match) > 0:
+            raise ValueError(f'The model name you provided is invalid. Perhaps you meant "{closest_match[0]}"?')
+        else:
+            raise ValueError(f'The model name you provided is invalid.') 
+    else:
+        return
+
+
 def get_model_params(model_name: str) -> float:
     """Return the approximate number of params of the model, in billions.
 
@@ -47,10 +70,7 @@ def get_model_params(model_name: str) -> float:
     float
         The number of parameters.
     """
-
-    if model_name not in ALLOWED_MODELS:
-        raise ValueError(f'The model name must be one of {*ALLOWED_MODELS,}.')
-    
+    check_model_name(model_name)
     return ALL_MODELS_PARAMS[model_name]
 
 
@@ -67,10 +87,7 @@ def get_model_dtype(model_name: str) -> torch.dtype:
     torch.dtype
         The default dtype.
     """
-
-    if model_name not in ALLOWED_MODELS:
-        raise ValueError(f'The model name must be one of {*ALLOWED_MODELS,}.')
-    
+    check_model_name(model_name)
     return ALL_MODELS_DTYPES[model_name]
 
 
@@ -87,10 +104,7 @@ def get_model_context_size(model_name: str) -> int:
     int
         The context size.
     """
-
-    if model_name not in ALLOWED_MODELS:
-        raise ValueError(f'The model name must be one of {*ALLOWED_MODELS,}.')
-    
+    check_model_name(model_name)
     return ALL_MODELS_CONTEXT_SIZE[model_name]
 
 
@@ -270,8 +284,8 @@ def load_model(model_name: str, quantization_8bits: bool = False, quantization_4
         The model.
     """
 
-    if model_name not in ALLOWED_MODELS:
-        raise ValueError(f'The model name must be one of {*ALLOWED_MODELS,}.')
+    # Check name
+    check_model_name(model_name)
     
     # Check package versions
     check_versions(model_name)
@@ -408,8 +422,8 @@ def load_tokenizer(model_name: str):
         The tokenizer.
     """
 
-    if model_name not in ALLOWED_MODELS:
-        raise ValueError(f'The model name must be one of {*ALLOWED_MODELS,}.') 
+    # Check name
+    check_model_name(model_name)
     
     # Check package versions
     check_versions(model_name)
diff --git a/textwiz/models/causal.py b/textwiz/models/causal.py
index 5909b84..63483c4 100644
--- a/textwiz/models/causal.py
+++ b/textwiz/models/causal.py
@@ -34,8 +34,8 @@ def __init__(self, model_name: str, quantization_8bits: bool = False, quantizati
                  dtype: torch.dtype | None = None, max_fraction_gpu_0: float = 0.8, max_fraction_gpus: float = 0.8,
                  device_map: dict | str | None = None, gpu_rank: int = 0):
         
-        if model_name not in loader.ALLOWED_CAUSAL_MODELS:
-            raise ValueError(f'The model name must be one of {*loader.ALLOWED_CAUSAL_MODELS,}.')
+        # Check name against only causal models
+        loader.check_model_name(model_name, loader.ALLOWED_CAUSAL_MODELS)
         
         super().__init__(model_name, quantization_8bits, quantization_4bits, dtype, max_fraction_gpu_0,
                          max_fraction_gpus, device_map, gpu_rank)
diff --git a/textwiz/models/embedding.py b/textwiz/models/embedding.py
index c3522a1..e9716e0 100644
--- a/textwiz/models/embedding.py
+++ b/textwiz/models/embedding.py
@@ -14,8 +14,8 @@ def __init__(self, model_name: str, quantization_8bits: bool = False, quantizati
                  dtype: torch.dtype | None = None, max_fraction_gpu_0: float = 0.8, max_fraction_gpus: float = 0.8,
                  device_map: dict | str | None = None, gpu_rank: int = 0):
         
-        if model_name not in loader.ALLOWED_EMBEDDING_MODELS:
-            raise ValueError(f'The model name must be one of {*loader.ALLOWED_EMBEDDING_MODELS,}.')
+        # Check name against only embedding models
+        loader.check_model_name(model_name, loader.ALLOWED_EMBEDDING_MODELS)
         
         super().__init__(model_name, quantization_8bits, quantization_4bits, dtype, max_fraction_gpu_0,
                          max_fraction_gpus, device_map, gpu_rank)