diff --git a/studio/backend/core/inference/inference.py b/studio/backend/core/inference/inference.py
index 41a907820f..9abf8d270f 100644
--- a/studio/backend/core/inference/inference.py
+++ b/studio/backend/core/inference/inference.py
@@ -214,37 +214,29 @@ class InferenceBackend:
     def activate_lora_adapter(self, base_model_name: str, lora_path: str) -> Tuple[bool, Optional[str]]:
         """
         Activates a specific LoRA adapter on what is assumed to be a clean base model.
+        Uses PeftModel.from_pretrained() which correctly wraps the base model.
         """
         model = self.models[base_model_name].get("model")
         adapter_name_to_load = lora_path.split("/")[-1].replace(".", "_")
         print(f"[DEBUG] activate_lora_adapter called. base_model_name='{base_model_name}', lora_path='{lora_path}'")
         print(f"[DEBUG] adapter_name_to_load='{adapter_name_to_load}'")
-        print(f"[DEBUG] Model type BEFORE load_adapter: {model.__class__.__name__}")
-        print(f"[DEBUG] Has peft_config? {hasattr(model, 'peft_config')}, keys={list(getattr(model, 'peft_config', {}).keys())}")
+        print(f"[DEBUG] Model type BEFORE: {model.__class__.__name__}")
 
         try:
-            # At this point, the model should be clean thanks to revert_to_base_model.
-            # We can now safely load and set the new adapter.
-
-            # Step 3: Load the new adapter.
-            print(f"[DEBUG] Calling model.load_adapter('{lora_path}', adapter_name='{adapter_name_to_load}')...")
-            model.load_adapter(lora_path, adapter_name=adapter_name_to_load)
-            print(f"[DEBUG] Model type AFTER load_adapter: {model.__class__.__name__}")
-            print(f"[DEBUG] peft_config keys AFTER load: {list(getattr(model, 'peft_config', {}).keys())}")
-
-            # Step 4: Set the new adapter as active.
-            print(f"[DEBUG] Calling model.set_adapter('{adapter_name_to_load}')...")
-            model.set_adapter(adapter_name_to_load)
-            print(f"[DEBUG] activate_lora_adapter SUCCESS. Model type: {model.__class__.__name__}")
+            # Use PeftModel.from_pretrained to wrap the clean base model with the adapter.
+            # This is the correct approach after model.unload() + del peft_config.
+            print(f"[DEBUG] Calling PeftModel.from_pretrained(model, '{lora_path}', adapter_name='{adapter_name_to_load}')...")
+            model = PeftModel.from_pretrained(model, lora_path, adapter_name=adapter_name_to_load)
+            self.models[base_model_name]["model"] = model
+            print(f"[DEBUG] Model type AFTER: {model.__class__.__name__}")
+            print(f"[DEBUG] activate_lora_adapter SUCCESS.")
 
             return True, adapter_name_to_load
         except Exception as e:
-            # This will catch the "already exists" error if revert_to_base_model failed.
             print(f"[DEBUG] activate_lora_adapter FAILED: {e}")
             import traceback
             traceback.print_exc()
             return False, None
-    pass
 
     def load_adapter(self, base_model_name: str, adapter_path: str, adapter_name: str = None) -> bool:
         """