From 609359d8f2d983043c93603e017c27b3544c744c Mon Sep 17 00:00:00 2001
From: yilun_jin <jyl.jal123@gmail.com>
Date: Fri, 22 Mar 2024 05:07:11 +0000
Subject: [PATCH] Update models/dummy_model.py
---
models/dummy_model.py | 27 +++++++++++++++++++++++++++
1 file changed, 27 insertions(+)
diff --git a/models/dummy_model.py b/models/dummy_model.py
index e126fe5..8c15b55 100644
--- a/models/dummy_model.py
+++ b/models/dummy_model.py
@@ -49,3 +49,30 @@ class DummyModel(ShopBenchBaseModel):
return str(possible_responses)
# 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
--
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