diff --git a/unsloth/chat_templates.py b/unsloth/chat_templates.py
index 2e3761f5..a2a02d7e 100644
--- a/unsloth/chat_templates.py
+++ b/unsloth/chat_templates.py
@@ -528,6 +528,7 @@ def get_chat_template(
         chat_template, stop_word = chat_template
         assert(type(chat_template) is str)
         assert(type(stop_word) is str)
+        ollama_modelfile = None
 
     elif type(chat_template) is str:
 
diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index f2f79de8..3d969d7d 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -1423,9 +1423,38 @@ def get_peft_model(
         transformers_set_seed(random_state)
 
         if isinstance(model, PeftModelForCausalLM):
-            raise TypeError(
-                "Unsloth: Your model already has LoRA adapters. No need to run this again!"
+            # Check if exactly the same and then pass through!
+            assert(hasattr(model, "peft_config"))
+
+            peft_config = model.peft_config["default"].to_dict()
+            check_parameters = [
+                "r", "lora_alpha", "lora_dropout",
+                "bias", "layers_to_transform", "layers_pattern",
+                "use_rslora", "modules_to_save", "init_lora_weights",
+            ]
+            check_all = True
+            for param in check_parameters:
+                check_all = check_all and (peft_config[param] == eval(param))
+            pass
+            check_all = check_all and (
+                len(set(peft_config["target_modules"]) ^ set(target_modules)) == 0
             )
+            check_all = check_all and (
+                (loftq_config == {} or loftq_config is None) and \
+                (peft_config["loftq_config"] == {} or peft_config["loftq_config"] is None)
+            )
+
+            if check_all:
+                # Simply pass through!
+                logger.warning(
+                    "Unsloth: Already have LoRA adapters! We shall skip this step."
+                )
+                return model
+            else:
+                raise TypeError(
+                    "Unsloth: Your model already has LoRA adapters. Your new parameters are different."
+                )
+            pass
         pass
 
         if loftq_config is None: loftq_config = {}
diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index de1e2e57..d7c0f076 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -91,21 +91,37 @@ def from_pretrained(
         model_name = _get_model_name(model_name, load_in_4bit)
 
         # First check if it's a normal model via AutoConfig
-        is_peft = False
         try:
             model_config = AutoConfig.from_pretrained(model_name, token = token, revision = revision)
-            is_peft = False
+            is_model = True
+        except:
+            is_model = False
+        try:
+            peft_config = PeftConfig .from_pretrained(model_name, token = token, revision = revision)
+            is_peft = True
         except:
-            try:
-                # Most likely a PEFT model
-                peft_config = PeftConfig.from_pretrained(model_name, token = token, revision = revision)
-            except:
-                raise RuntimeError(f"Unsloth: `{model_name}` is not a full model or a PEFT model.")
-            
+            is_peft = False
+
+        # Cannot be both!
+        if is_model and is_peft:
+            raise RuntimeError(
+                "Unsloth: You repo has a LoRA adapter and a base model.\n"\
+                "You have 2 files `config.json` and `adapter_config.json`.\n"\
+                "We must only allow one config file.\n"\
+                "Please separate the LoRA and base models to 2 repos."
+            )
+        elif not is_model and not is_peft:
+            raise RuntimeError(
+                f"Unsloth: `{model_name}` is not a base model or a PEFT model.\n"\
+                "We could not locate a `config.json` or `adapter_config.json` file"
+            )
+        pass
+
+        # Get base model for PEFT:
+        if is_peft:
             # Check base model again for PEFT
             model_name = _get_model_name(peft_config.base_model_name_or_path, load_in_4bit)
-            model_config = AutoConfig.from_pretrained(model_name, token = token)
-            is_peft = True
+            model_config = AutoConfig.from_pretrained(model_name, token = token, revision = revision)
         pass
 
         model_type = model_config.model_type