diff --git a/models/dummy_model.py b/models/dummy_model.py
index 8c15b556b5dfe0eac1358aa4a8011ee2abe07afb..5126746b8968f7ba6a047c76766772c6de08b2ec 100644
--- a/models/dummy_model.py
+++ b/models/dummy_model.py
@@ -50,29 +50,3 @@ class DummyModel(ShopBenchBaseModel):
             # Note: As this is dummy model, we are returning random responses for non-multiple choice tasks.
             # For generation tasks, this should ideally return an unconstrained string.
 
-class Vicuna2ZeroShot(ShopBenchBaseModel):
-    """
-    A baseline solution that uses Vicuna-7B to generate answers with zero-shot prompting. 
-    """
-    def __init__(self):
-        random.seed(AICROWD_RUN_SEED)
-        ### model_path = 'lmsys/vicuna-7b-v1.5'
-        ### Before submitting, please put necessary files to run Vicuna-7B at the corresponding path, and submit them with `git lfs`. 
-        self.tokenizer = AutoTokenizer.from_pretrained('./models/vicuna-7b-v1.5/', trust_remote_code=True)
-        self.model = AutoModelForCausalLM.from_pretrained('./models/vicuna-7b-v1.5/', device_map='auto', torch_dtype='auto', trust_remote_code=True, do_sample=True)
-        self.system_prompt = "You are a helpful online shopping assistant. Please answer the following question about online shopping and follow the given instructions.\n\n"
-
-
-    def predict(self, prompt: str, is_multiple_choice: bool) -> str:
-        prompt = self.system_prompt + prompt
-        inputs = self.tokenizer(prompt, return_tensors='pt')
-        inputs.input_ids = inputs.input_ids.cuda()
-        if is_multiple_choice:
-            # only one token for multiple choice questions. 
-            generate_ids = self.model.generate(inputs.input_ids, max_new_tokens=1, temperature=0)
-        else:
-            # 100 tokens for non-multiple choice questions. 
-            generate_ids = self.model.generate(inputs.input_ids, max_new_tokens=100, temperature=0)
-        result = self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        generation = result[len(prompt):]
-        return generation