From 1b65447a0f892eb2260ac9e5da24beb67d3196b6 Mon Sep 17 00:00:00 2001
From: yilun_jin <jyl.jal123@gmail.com>
Date: Sun, 24 Mar 2024 17:29:45 +0000
Subject: [PATCH] Update models/dummy_model.py
---
models/dummy_model.py | 26 --------------------------
1 file changed, 26 deletions(-)
diff --git a/models/dummy_model.py b/models/dummy_model.py
index 8c15b55..5126746 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
--
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