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1from transformers import AutoModelForCausalLM, AutoTokenizer
2device = "cuda" # the device to load the model onto
3model = AutoModelForCausalLM.from_pretrained(
4 model_path, torch_dtype="auto", device_map="auto"
5)
6tokenizer = AutoTokenizer.from_pretrained(model_path)
7
8def build_user_query(question, pred_answer, answer, base_prompt):
9 input_text = base_prompt.replace("{{question}}", question)
10 input_text = input_text.replace("{{pred_step}}", pred_answer)
11 input_text = input_text.replace("{{answer}}", answer)
12 input_text = input_text.replace("{{analysis}}", "") # default set analysis to blank, if exist, you can pass in the corresponding parameter.
13 return input_text
14
15chat_prompt = """<|im_start|>system
16You are a helpful assistant.<|im_end|>
17<|im_start|>human
18{}<|im_end|>
19<|im_start|>gpt
20"""
21
22basic_prompt = """## 任务描述\n \n你是一个数学老师，学生提交了题目的解题步骤，你需要参考`题干`，`解析`和`答案`，判断`学生解题步骤`的结果是否正确。忽略`学生解题步骤`中的错误，只关注最后的答案。答案可能出现在`解析`中，也可能出现在`答案`中。\n \n## 输入内容\n \n题干:\n \n```\n{{question}}\n```\n \n解析:\n \n```\n{{analysis}}\n \n```\n \n答案:\n \n```\n{{answer}}\n```\n \n学生解题步骤:\n \n```\n{{pred_step}}\n```\n \n输出:"""
23
24base_prompt = chat_prompt.format(basic_prompt)
25
26def build_user_query(question, pred_answer, answer, base_prompt):
27 input_text = base_prompt.replace("{{question}}", question)
28 input_text = input_text.replace("{{pred_step}}", pred_answer)
29 input_text = input_text.replace("{{answer}}", answer)
30 input_text = input_text.replace("{{analysis}}", "") # default set analysis to blank, if exist, you can pass in the corresponding parameter.
31 return input_text
32
33prompt = build_user_query("1+1=", "3", "2", base_prompt)
34
35model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
36
37generated_ids = model.generate(model_inputs.input_ids, temperature=0, max_new_tokens=16, eos_token_id=100005)
38generated_ids = [
39 output_ids[len(input_ids) :]
40 for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
41]
42
43response = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]