diff --git a/test_lora.py b/test_lora.py
deleted file mode 100644
index 8d48f20bba..0000000000
--- a/test_lora.py
+++ /dev/null
@@ -1,83 +0,0 @@
-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__}")