diff --git a/q25_14_ins_int8.py b/q25_14_ins_int8.py
deleted file mode 100644
index 36fa3098625c767c2cfa873122f1789852e1f85d..0000000000000000000000000000000000000000
--- a/q25_14_ins_int8.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import os
-import re 
-import torch 
-import json 
-# import time
-# import pynvml
-from tqdm import tqdm
-import torch.distributed as dist
-from vllm import LLM, SamplingParams
-os.environ["NCCL_P2P_DISABLE"] = "1"
-
-torch.cuda.empty_cache()
-os.environ["TRANSFORMERS_OFFLINE"] = "1"
-os.environ["HF_DATASETS_OFFLINE"] = "1"
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-texts_path = "/home/limeng/NLP/LLM/data/jcza/record_text2.txt"
-model_path = "/home/limeng/NLP/LLM/model/Qwen2.5-14B-Instruct-GPTQ-Int8"
-output_path = "/home/limeng/NLP/LLM/code/0220/result"
-
-llm = LLM(model=model_path, dtype="half",gpu_memory_utilization=0.9)
-sampling_params = SamplingParams(temperature=0.3, top_p=0.3, max_tokens = 1500)
-
-
-with open(texts_path, "r", encoding="utf-8") as file:
-        for i,line in enumerate(file):
-            medical_record = line.strip()  # 处理每一行，去除首尾空白字符
-            prompt = f"""
-你是一个专业的医疗信息抽取助手。请从以下病历数据中严格按照#### 示例 JSON 提取字段信息，并确保：
-1. **字段必须完整**，与示例 JSON 结构完全一致，不可缺失/新增/改动任何字段
-2. **重点字段精确**：
-   - 手术史需抽提所有手术名称+时间（格式："手术名称": "时间"）
-   - 化疗方案需按"方案名": {{时间+具体用药列表}} 格式提取
-   - 放疗方案需按"方案描述": {{时间+次数+剂量}} 格式提取
-3. **严格空值处理**：
-   - 字符串字段填"无"
-   - 列表/字典字段填空列表[]/空字典{{}}
-   - 嵌套结构需保持完整（如肿瘤患病史必须含"肿瘤类型"/"肿瘤结局"字段）
-4. **严格JSON格式**：
-   - 保持与示例完全相同的缩进/标点格式
-   - 确保所有括号闭合，逗号正确
-   - 生成第一个完整JSON后立即停止，禁止解释性文字   
-
-#### 病历数据
-{medical_record}
-
-#### 示例 JSON
-{{
-    "消瘦": "有",
-    "呕吐": "有",
-    "恶心": "有",
-    "腹部肿块": "有",
-    "腹胀": "有",
-    "腹痛": "有",
-    "里急后重": "有",
-    "腹泻": "有",
-    "大便形状改变": "有",
-    "排便困难": "有",
-    "黑便": "有",
-    "便血": "有",
-    "大便习惯和性状改变": "有",
-    "肠梗阻": "有",
-    "肠穿孔": "有",
-    "手术史": {{
-        "直肠癌经腹前切除+末端回肠造口术": "2017-01-11日",
-        "冠脉支架置入术":" 2019年",
-    }},
-    "肝转移": "有",
-    "肺转移": "有",
-    "腹膜转移": "有",
-    "骨转移": "有",
-    "远处转移": "有",
-    "锁骨上转移": "有",
-    "腹股沟转移": "有",
-    "腹膜后淋巴结转移": "有",
-    "其他远处淋巴转移": "无",
-    "化疗方案": {{
-        "2011-1-12行XELOX方案": 
-        {{"时间": "2011-1-12"
-        "具体用药": [
-        "卡培他宾1.5g 2/日d1-14",
-        "奥沙利铂200mg d1"
-                    ]
-        }},
-        "2022-7-17、8-9行FOLFOX+西妥昔单抗方案":
-        {{ "时间": "2022-7-17、8-9"
-        "具体用药": [
-        "奥沙利铂140mg 静滴D1",
-        "亚叶酸钙0.6g 静滴D1",
-        "5-FU 0.4g 静滴D1",
-        "5-FU 4.0g 化疗泵入44h",
-        "西妥昔单抗800mg 静滴D1"
-        ]}}
-    }},
-    "放疗方案": 
-    {{
-        "盆腔复发灶": {{
-            "时间": "2023-04-20至2023-05-30",
-            "次数": "25",
-            "单次剂量":"2Gy",
-            "总剂量":"50Gy"
-        }},
-        "照射方法为适形调强放疗IMRT，分割方法为常规分割，疗效评估为PR": {{
-            "时间": "2021年11月15日开始，2021年12月24日结束",
-            "次数": "25",
-            "单次剂量":"2Gy",
-            "总剂量":""
-        }},
-    }},
-    "仍需治疗的其他疾病情况": ["高血压", "糖尿病"],
-    "入院前仍在服用的治疗药物": ["硝苯地平缓释片", "达格列净", "格列齐特"],
-    "高血压史": "有",
-    "伤寒史": "有",
-    "结核史": "有",
-    "病毒性肝炎史": "有",
-    "糖尿病史": "有",
-    "冠心病史": "有",
-    "冠脉支架放置": "有",
-    "脑卒中史": "有",
-    "其他非肿瘤疾病": [
-        "慢性乙型病毒性肝炎",
-    ],
-    "肿瘤患病史": {{
-        "肿瘤类型": "左肺腺癌",
-        "肿瘤结局": "治愈"
-    }}
-    "吸烟史": {{
-        "吸烟年数": "25年",
-        "日吸烟量": "10支/天",
-        "是否戒烟": "已戒烟15年"
-    }},
-    "饮酒史": "有"
-    "婚育史": {{
-        "是否已婚": "已婚",
-        "是否已育": "已育",
-        "已育数量": "1女"
-    }},
-    "结直肠癌家族史": {{
-        "遗传性结直肠癌类型": "无",
-        "亲属类型": "弟弟",
-        "其他遗传性肿瘤": "结肠癌"
-    }},
-    "体温": "36.0℃",
-    "呼吸": "18次/分",
-    "心率": "80次/分",
-    "血压": "120/80mmHg",
-    "BMI": "19.6"
-    "直肠指诊": {{
-        "直肠指诊姿势": "膝胸位",
-        "直肠指诊是否触及肿块": "有",
-        "直肠指诊肿块下缘到肛缘距离": "5cm",
-        "直肠指诊肿块下缘到齿状线距离": "无",
-        "直肠指诊肿块活动度": "尚可",
-        "直肠指诊指套推出是否染血": "有"
-    }},
-    "贫血貌": "有",
-    "巩膜黄染": "有",
-    "锁骨上淋巴结肿大": "有",
-    "腹壁静脉曲张": "有",
-    "肠形": "有",
-    "腹部压痛": "有",
-}}    
-"""
-            try:
-                # 调用模型生成结果
-                outputs = llm.generate(prompt, sampling_params)
-                model_output = outputs[0].outputs[0].text
-                print(model_output)
-                # 尝试提取 JSON 部分
-                json_str = re.search(r'```json\n(.*?)\n```', model_output, re.DOTALL)
-                if json_str:
-                    json_str = json_str.group(1)
-                    result_json = json.loads(json_str)
-                else:
-                    # 如果未找到 JSON 部分，直接保存原始文本
-                    json_str = re.search(r'#### JSON 提取结果\n(.*?)\n#### JSON 提取结果', model_output, re.DOTALL)
-                    if json_str:
-                        result_json = json.loads(json_str.group(1))
-                    else:
-                        # 如果未找到 #### JSON 提取结果 格式，尝试提取 #### JSON 输出 格式
-                        json_str = re.search(r'#### JSON 输出\n(.*?)\n#### JSON 输出', model_output, re.DOTALL)
-                        if json_str:
-                            result_json = json.loads(json_str.group(1))
-                        else:
-                            json_str = re.search(r'#### JSON 提取结果\n(.*?) 根据提', model_output, re.DOTALL)
-                            if json_str:
-                                result_json = json.loads(json_str.group(1))
-                            else:
-                                result_json = {"error": "Invalid model output format", "original_text": medical_record, "model_output": model_output}
-
-                # 保存结果到 JSON 文件
-                file_name = f"long_txt{i}.json"
-                file_path = os.path.join(output_path, file_name)
-                with open(file_path, 'w', encoding='utf-8') as f:
-                    json.dump({"text_record": medical_record, "extracted_info": result_json}, f, ensure_ascii=False, indent=4)
-
-            except Exception as e:
-                # 捕获异常并记录错误
-                print(f"Error processing record {i}: {e}")
-                # 保存原始文本
-                file_name = f"long_txt{i}_error.json"
-                file_path = os.path.join(output_path, file_name)
-                with open(file_path, 'w', encoding='utf-8') as f:
-                    json.dump({"text_record": medical_record, "error": str(e)}, f, ensure_ascii=False, indent=4)
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