diff --git a/studio/backend/main.py b/studio/backend/main.py
index e957c668e3..ce67200456 100644
--- a/studio/backend/main.py
+++ b/studio/backend/main.py
@@ -128,7 +128,7 @@ def setup_frontend(app: FastAPI, build_path: Path):
 
         @app.get("/")
         async def serve_root():
-            return FileResponse(build_path / "index.html")
+            return FileResponse(build_path / "index.html", headers={"Cache-Control": "no-cache, no-store, must-revalidate"})
 
         @app.get("/{full_path:path}")
         async def serve_frontend(full_path: str):
@@ -139,7 +139,7 @@ def setup_frontend(app: FastAPI, build_path: Path):
             if file_path.is_file():
                 return FileResponse(file_path)
 
-            return FileResponse(build_path / "index.html")
+            return FileResponse(build_path / "index.html", headers={"Cache-Control": "no-cache, no-store, must-revalidate"})
 
         return True
     return False
diff --git a/test_lora.py b/test_lora.py
new file mode 100644
index 0000000000..8d48f20bba
--- /dev/null
+++ b/test_lora.py
@@ -0,0 +1,83 @@
+from unsloth import FastLanguageModel
+from peft import PeftModel, PeftModelForCausalLM
+
+lora_path="/home/support/new-ui-prototype/outputs/meta-llama_Llama-3.1-8B-Instruct_1771048481"
+adapter_name_to_load="test"
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="/home/support/new-ui-prototype/outputs/meta-llama_Llama-3.1-8B-Instruct_1771048481",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+
+# Quick sanity check
+FastLanguageModel.for_inference(model)
+print("Model loaded successfully!")
+print(f"Model type before unloading: {type(model)}")
+print(f"Tokenizer: {type(tokenizer)}")
+print(f"Model class before unloading: {model.__class__.__name__}")
+# Test generation
+#inputs = tokenizer("What is the capital of France?", return_tensors="pt").to(model.device)
+#outputs = model.generate(**inputs, max_new_tokens=32)
+#print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+
+print("unloading base model")
+if isinstance(model, (PeftModel, PeftModelForCausalLM)):
+        print("Model is a PeftModel. Unloading adapters...")
+        unwrapped_base_model = model.unload()
+        model = unwrapped_base_model
+        #if hasattr(model, 'peft_config') and model.peft_config:
+        #        print("Found lingering adapter configurations. Deleting them now...")
+        #        # Create a static list of keys before iterating and deleting
+        #        for name in list(model.peft_config.keys()):
+        #            if name == "default":
+        #                continue
+        #            print(f"Deleting adapter config: '{name}'")
+        #            mode=model.delete_adapter(name)
+        #model.disable_adapters()
+        if hasattr(model, 'peft_config'):
+            del model.peft_config
+print(f"Model type post unloading{type(model)}")
+print(f"Model class post unloading: {model.__class__.__name__}")
+#print(f"model: {model}")
+
+#print("generating using base model")
+#inputs = tokenizer("What is the capital of Lebanon?", return_tensors="pt").to(model.device)
+#outputs = model.generate(**inputs, max_new_tokens=32)
+#print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+#print(f"model config: {model.config}")
+#print(f"model: {model}")
+#print(f"model.peft_config: {model.peft_config}")
+
+#print("loading lora dapter")
+#model.load_adapter(lora_path, adapter_name=adapter_name_to_load)
+#model.enable_adapters
+#model.set_adapter(adapter_name_to_load)
+model = PeftModel.from_pretrained(model, lora_path, adapter_name=adapter_name_to_load)
+print(f"Model type {type(model)}")
+print(f"Model class: {model.__class__.__name__}")
+#print(f"model: {model}")
+#print("generating using peft model hopefully")
+#inputs = tokenizer("What is the capital of Brasil?", return_tensors="pt").to(model.device)
+#outputs = model.generate(**inputs, max_new_tokens=32)
+#print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+print("unloading base model")
+if isinstance(model, (PeftModel, PeftModelForCausalLM)):
+        print("Model is a PeftModel. Unloading adapters...")
+        unwrapped_base_model = model.unload()
+        model = unwrapped_base_model
+        #if hasattr(model, 'peft_config') and model.peft_config:
+        #        print("Found lingering adapter configurations. Deleting them now...")
+        #        # Create a static list of keys before iterating and deleting
+        #        for name in list(model.peft_config.keys()):
+        #            if name == "default":
+        #                continue
+        #            print(f"Deleting adapter config: '{name}'")
+        #            mode=model.delete_adapter(name)
+        #model.disable_adapters()
+        if hasattr(model, 'peft_config'):
+            del model.peft_config
+print(f"Model type post unloading{type(model)}")
+print(f"Model class post unloading: {model.__class__.__name__}")