Create README.md

README.md

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+```python
+import os
+import torch
+import platform
+import subprocess
+from transformers import AutoModelForCausalLM, AutoTokenizer
+#from transformers.generation.utils import GenerationConfig
+from transformers.generation import GenerationConfig
+import json
+from tqdm import tqdm
+import re
+
+def init_model():
+    print("init model ...")
+    '''
+    model = AutoModelForCausalLM.from_pretrained(
+        "./exportchatml",
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    model.generation_config = GenerationConfig.from_pretrained(
+        "./exportchatml"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        "./exportchatml",
+        use_fast=False,
+        trust_remote_code=True
+    )
+    '''
+    tokenizer = AutoTokenizer.from_pretrained(
+        "./exportchatml", trust_remote_code=True, resume_download=True,
+    )
+
+    device_map = "auto"
+
+    model = AutoModelForCausalLM.from_pretrained(
+        "./exportchatml",
+        device_map=device_map,
+        trust_remote_code=True,
+        resume_download=True,
+    ).eval()
+    '''
+    config = GenerationConfig.from_pretrained(
+        "./exportchatml", trust_remote_code=True, resume_download=True,
+    )
+    '''
+    config = GenerationConfig(chat_format='chatml', eos_token_id=151643, pad_token_id=151643, max_window_size=6144, max_new_tokens=512, do_sample=True, top_k=0, top_p=0.5)
+    
+    return model, tokenizer, config
+
+def main():
+    model, tokenizer, config = init_model()
+    #tokenizer = AutoTokenizer.from_pretrained("./exportchatml", trust_remote_code=True)
+    #model = AutoModelForCausalLM.from_pretrained("./exportchatml", trust_remote_code=True)
+    #model = model.eval()
+    
+    with open('CMB-test-choice-question-merge.json', 'r', encoding='utf-8') as file:
+        data = json.load(file)
+    
+    results = []
+    for i in tqdm(range(len(data))):
+        messages = []
+        
+        test_id = data[i]['id']
+        exam_type = data[i]['exam_type']
+        exam_class = data[i]['exam_class']
+        question_type = data[i]['question_type']
+        question = data[i]['question']
+        option_str = 'A. ' + data[i]['option']['A'] + '\nB. ' + data[i]['option']['B']+ '\nC. ' + data[i]['option']['C']+ '\nD. ' + data[i]['option']['D']
+        
+        #prompt = f'以下是中国{exam_type}中{exam_class}考试的一道{question_type}，不需要做任何分析和解释，直接给出正确选项。\n{question}\n{option_str}'
+        prompt = f'以下是一道{question_type}，不需要做任何分析解释，直接给出正确选项：\n{question}\n{option_str}'
+        #messages.append({"role": "user", "content": prompt})
+        #history = ''
+        #response = model.chat(tokenizer, messages,history)
+        #print(response)
+        #print(type(response))
+        '''
+        generation_config = GenerationConfig(max_new_tokens=1024)
+        
+        text = 'User: '+prompt+'<|endoftext|>\n Assistant: '
+        inputs = tokenizer.encode(text, return_tensors="pt").to('cpu')
+        outputs = model.generate(inputs, generation_config=generation_config)
+        output = tokenizer.decode(outputs[0])
+        response = output.replace(inputs, '')
+        print(response)
+        '''
+        history = []
+        response, history = model.chat(tokenizer, prompt, history=history, generation_config=config)
+        print(response)
+        matches = re.findall("[ABCDE]", response)
+        print(matches)
+        final_result = "".join(matches)
+    
+        info = {
+                "id": test_id,
+                "model_answer": final_result
+            }
+        results.append(info)
+        
+        history.clear()
+
+    with open('output.json', 'w', encoding="utf-8") as f1:
+        json.dump(results, f1, ensure_ascii=False, indent=4)
+
+if __name__ == "__main__":
+    main()
+```