├── LICENSE ├── README.md ├── data ├── eval │ └── 药品适应症评测数据集.csv └── train │ └── sft-20k.json ├── gradio_cama-demo.py ├── gradio_chatglm_demo.py ├── gradio_chinese-llama_demo.py ├── lora └── lora_weights ├── pics ├── llm_kg1.jpg ├── llm_kg2.jpg └── medcopilot1.jpg ├── requirements.txt └── scripts ├── callbacks.py ├── merge_llama_plus.sh └── merge_llama_with_chinese_lora.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 启真医学大模型 & MedCopilot 2 | 3 | ## QiZhenGPT 4 | 5 | QiZhenGPT: An Open Source Chinese Medical Large Language Model 6 | 7 | 8 | 本项目利用[启真医学知识库](http://www.mk-base.com)构建的中文医学指令数据集，并基于此在[Chinese-LLaMA-Plus-7B](https://github.com/ymcui/Chinese-LLaMA-Alpaca)、[CaMA-13B](https://github.com/zjunlp/CaMA)、[ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)模型上进行指令精调，大幅提高了模型在中文医疗场景下效果，首先针对药品知识问答发布了评测数据集，后续计划优化疾病、手术、检验等方面的问答效果，并针对医患问答、病历自动生成等应用展开拓展。 9 | 10 | ## MedCopilot 11 | 12 | `MedCopilot`是一款基于`启真医学大模型`、[启真医学知识库](http://www.mk-base.com)、`医疗临床数据`研发的智慧医疗助手，旨在为患者、医生和医院管理提供全面的智慧支持。通过整合先进的人工智能技术、丰富的医学知识和临床数据，MedCopilot将成为医疗行业的新质生产力。 13 | 14 | MedCopilot目前已在[浙江大学第二附属医院正式上线使用](https://mp.weixin.qq.com/s/Pm519Et1POgoerkcdC2YRQ)。 15 | 16 | image-20230525165523821 17 | 18 | ## 更新记录 19 | 20 | ### 更新 21 | 22 | [2024/08/09] 更新MedCopilot相关信息; 23 | 24 | [2023/06/27] 开源启真医学大模型体验版（QiZhen-CaMA-13B-Checkpoint-12400），旨在提高医学领域疾病、药品知识问答的准确性; 25 | 26 | [2023/06/09] 开源启真医学大模型体验版（QiZhen-CaMA-13B-Checkpoint-6000），旨在提高医学领域药品知识问答的准确性; 27 | 28 | [2023/06/02] 开源启真医学大模型体验版（QiZhen-CaMA-13B-Checkpoint-3600），旨在提高医学领域药品知识问答的准确性; 29 | 30 | [2023/05/30] 开源[20k](./data/train/sft-20k.json)训练数据（该数据集来自于启真医学知识库收集整理的真实医患知识问答数据以及在启真医学知识库的药品文本知识基础上，通过对半结构化数据设置特定的问题模板构造的指令数据）； 31 | 32 | [2023/05/30] 开源启真医学大模型体验版（QiZhen-ChatGLM-6B- Checkpoint-2500），旨在提高医学领域药品知识问答的准确性； 33 | 34 | [2023/05/25] 开源[药品适应症评测数据集](./data/eval/%E8%8D%AF%E5%93%81%E9%80%82%E5%BA%94%E7%97%87%E8%AF%84%E6%B5%8B%E6%95%B0%E6%8D%AE%E9%9B%86.csv); 35 | 36 | [2023/05/24] 开源启真医学大模型体验版（QiZhen-Chinese-LLaMA-7B- Checkpoint-6000），旨在提高医学领域药品知识问答的准确性; 37 | 38 | [2023/05/23] 开源启真医学大模型体验版（QiZhen-Chinese-LLaMA-7B- Checkpoint-3500），旨在提高医学领域药品知识问答的准确性； 39 | 40 | ## MedCopilot功能细节 41 | 42 | ### 1. 功能清单助手 43 | 44 | MedCopilot与 HIS系统和电子病历系统深度融合，利用启真医学大模型分析各个系统数据，自动汇总医生当日重要的工作事项。 45 | 46 | 1. 入院患者统计与分析； 47 | 2. 手术情况统计与分析； 48 | 3. 会诊情况统计与分析； 49 | 4. 文书书写情况统计与分析； 50 | 5. 重点患者统计与分析； 51 | 52 | ### 2. 辅助诊疗助手 53 | 54 | MedCopilot结合启真医学知识库和患者临床数据，提供个性化诊断和治疗建议，帮助医生做出更准确的医疗决策。 55 | 56 | ### 3. 医疗质量助手 57 | 58 | MedCopilot依据国家医疗质量政策，实时监控医疗过程数据，及时发现和纠正潜在问题，提升整体医疗质量。 59 | 60 | ### 4. 病历文书助手 61 | 62 | MedCopilot综合分析患者诊疗数据，自动生成符合规范的病历文书，减少医生的重复性工作，提高工作效率。 63 | 64 | ### 5. 其他功能 65 | 66 | 1. 科研助手：论文解读 67 | 2. 健康助手：报告解读、慢病管理 68 | 69 | ## QizhenGPT细节 70 | 71 | ### 指令数据集构建 72 | 73 | 目前大多数开源的ChatLLM项目使用的是其他模型（如：ChatGPT）生成的指令数据，其不可避免的存在数据幻想的问题，数据幻想问题将严重影响LLM在实际场景中的应用和拓展。因此，本项目为了提高医疗领域的知识问答的准确性，使用如下方式构造指令数据集： 74 | 75 | 1. 启真医学知识库收录的真实医患知识问答数据（疾病、药品、检查检验、手术、预后、食物等），共计`560K`条指令数据； 76 | 2. 药品知识数据：在启真医学知识库的药品文本知识基础上，通过对半结构化数据设置特定的问题模板（如：“{药品}的适应病症是什么？”）构造指令数据集，共计`180K`条指令数据； 77 | 3. 疾病知识数据：在启真医学知识库的疾病文本知识基础上，通过对半结构化数据设置特定的问题模板（如：“{疾病}的典型症状是什么？”）构造指令数据集，共计`298K`条指令数据； 78 | 79 | ### 训练细节 80 | 81 | 1. QiZhen-Chinese-LLaMA-7B- Checkpoint-3500：本项目基于[Chinese-LLaMA-Plus-7B](https://github.com/ymcui/Chinese-LLaMA-Alpaca)进行指令微调，该项目在7张A800(80G)上进行训练，本次开源的是LoRA权重为训练过程中的第`3500 steps`（训练23h50min） ； 82 | 2. QiZhen-Chinese-LLaMA-7B- Checkpoint-6000：本项目基于[Chinese-LLaMA-Plus-7B](https://github.com/ymcui/Chinese-LLaMA-Alpaca)进行指令微调，该项目在7张A800(80G)上进行训练，本次开源的是LoRA权重为训练过程中的第`6000 steps`（训练40h56min）； 83 | 3. QiZhen-ChatGLM-6B- Checkpoint-2500：本项目基于[ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)进行指令微调，该项目在7张A800(80G)上进行训练，本次开源的是LoRA权重为训练过程中的第`2500 steps`（训练16h20min）； 84 | 4. QiZhen-CaMA-13B-Checkpoint-3600：本项目基于[CaMA-13B](https://github.com/zjunlp/CaMA)进行指令微调，该项目在7张A800(80G)上进行训练，本次开源的是LoRA权重为训练过程中的第`3600 steps`（训练37h37min）。 85 | 5. QiZhen-CaMA-13B-Checkpoint-6000：本项目基于[CaMA-13B](https://github.com/zjunlp/CaMA)进行指令微调，该项目在7张A800(80G)上进行训练，本次开源的是LoRA权重为训练过程中的第`6000 steps`（训练54h30min）。 86 | 6. QiZhen-CaMA-13B-Checkpoint-12400：本项目基于[CaMA-13B](https://github.com/zjunlp/CaMA)进行指令微调，该项目在6张A800(80G)上进行训练，本次开源的是LoRA权重为训练过程中的第`12400 steps`（训练114h46min）。 87 | 88 | ### 模型下载 89 | 90 | | 模型 | 指令数据集 | Base Model | LoRA下载 | 91 | | :--------------------------------------: | :--------: | :-------------------: | :----------------------------------------------------------: | 92 | | QiZhen-Chinese-LLaMA-7B- Checkpoint-3500 | 740K | Chinese-LLaMA-Plus-7B | [百度网盘](https://pan.baidu.com/s/1KQIF-dUsL7Nrj8UeNuFUiw?pwd=ivgg) | 93 | | QiZhen-Chinese-LLaMA-7B- Checkpoint-6000 | 740K | Chinese-LLaMA-Plus-7B | [百度网盘](https://pan.baidu.com/s/1KQIF-dUsL7Nrj8UeNuFUiw?pwd=ivgg) | 94 | | QiZhen-ChatGLM-6B- Checkpoint-2500 | 740K | ChatGLM-6B | [百度网盘](https://pan.baidu.com/s/1KQIF-dUsL7Nrj8UeNuFUiw?pwd=ivgg) | 95 | | QiZhen-CaMA-13B-Checkpoint-3600 | 740K | CaMA | [百度网盘](https://pan.baidu.com/s/1KQIF-dUsL7Nrj8UeNuFUiw?pwd=ivgg) | 96 | | QiZhen-CaMA-13B-Checkpoint-6000 | 740K | CaMA | [百度网盘](https://pan.baidu.com/s/1KQIF-dUsL7Nrj8UeNuFUiw?pwd=ivgg) | 97 | | QiZhen-CaMA-13B-Checkpoint-12400 | 1038K | CaMA | [百度网盘](https://pan.baidu.com/s/1KQIF-dUsL7Nrj8UeNuFUiw?pwd=ivgg) | 98 | 99 | ### A Quick Start 100 | 101 | #### QiZhen-Chinese-LLaMA-7B 102 | 103 | 1. 环境安装； 104 | 105 | ```bash 106 | pip install -r requirements.txt 107 | ``` 108 | 109 | 2. 获取Chinese-LLaMA-Plus-7B，详情见[这里](https://github.com/ymcui/Chinese-LLaMA-Alpaca/wiki/%E6%A8%A1%E5%9E%8B%E5%90%88%E5%B9%B6%E4%B8%8E%E8%BD%AC%E6%8D%A2)； 110 | 111 | 3. 下载LoRA，将模型下载并放在lora目录下； 112 | 113 | 4. 执行scripts/merge_llama_plus.sh 脚本； 114 | 115 | ``` 116 | sh scripts/merge_llama_plus.sh 117 | ``` 118 | 119 | 5. 修改`gradio_chinese-llama_demo.py`里的模型位置参数； 120 | 5. 启动demo； 121 | 122 | ``` 123 | python gradio_chinese-llama_demo.py 124 | ``` 125 | 126 | #### QiZhen-ChatGLM-6B 127 | 128 | 1. 环境安装； 129 | 130 | ```bash 131 | pip install -r requirements.txt 132 | ``` 133 | 134 | 2. 获取ChatGLM-6B，详情见[这里](https://github.com/THUDM/ChatGLM-6B)； 135 | 3. 下载LoRA，将模型下载并放在lora目录下； 136 | 137 | 4. 修改`gradio_chatglm_demo.py`里的模型位置参数； 138 | 139 | 5. 启动demo； 140 | 141 | ``` 142 | python gradio_chatglm_demo.py 143 | ``` 144 | 145 | #### QiZhen-CaMA-13B-Checkpoint-3600 146 | 147 | 1. 环境安装； 148 | 149 | ```bash 150 | pip install -r requirements.txt 151 | ``` 152 | 153 | 2. 获取CaMA，详情见[这里](https://github.com/zjunlp/CaMA#2-1)； 154 | 155 | 3. 下载LoRA，将模型下载并放在lora目录下； 156 | 157 | 4. 修改`gradio_cama_demo.py`里的模型位置参数； 158 | 159 | 5. 启动demo； 160 | ``` 161 | python gradio_cama_demo.py 162 | ``` 163 | 164 | ### 预研 165 | 166 | 启真医学大模型坚持“数据+知识双轮驱动”的技术路线，通过大模型技术和医学知识库的紧密结合，融合医疗应用场景中的实际需求，从医学数据治理、医学推理、医患问答、病历自动生成、临床决策辅助支持系统等方面开展迭代研发，助力大模型技术在医疗行业的落地和应用实践。下图是“启真医学知识库辅助大模型生成答案”的案例，该项技术在测试后择机发布。 167 | 168 | image-20230525165523821 169 | 170 | image-20230525165523821 171 | 172 | ### 模型效果对比 173 | 174 | #### 测试1 175 | 176 | - input：非布司他能治疗什么疾病 177 | - ChatGLM：非布司他是一种用于治疗哮喘的新药,属于β2受体激动剂。它已经被批准用于治疗以下疾病: 1. 哮喘:非布司他是一种β2受体激动剂,可以刺激β2受体,从而缓解哮喘症状,包括呼吸困难、气促和胸闷。 2. 慢阻肺:非布司他也可以用于缓解慢阻肺引起的症状,包括呼吸急促、气促和咳嗽。 3. 过敏性鼻炎:非布司他可以减轻过敏性鼻炎引起的症状,包括鼻塞、流涕和打喷嚏。 4. 其他疾病:非布司他也被用于治疗其他疾病,如胡萝卜素血症和皮肤划痕症等。 需要注意的是,非布司他是一种药物,其治疗效果和安全性因人而异,最好在医生的指导下使用。 178 | - ChatGPT：非布司他（Fubuxitam）是一种药物，目前我的知识截至于2021年，不包括最新的医学研究和药物进展。根据我了解到的信息，非布司他是一种血小板凝集抑制剂，通常用于预防和治疗血栓性疾病。它可用于预防心脏病、脑血管疾病（例如中风）、静脉血栓栓塞（例如深静脉血栓形成和肺栓塞）以及其他与血栓形成有关的情况。

然而，由于我无法提供最新的医学信息，我建议您咨询医疗专业人士或药剂师以获取关于非布司他的最新信息，并确切了解它在治疗特定疾病方面的适应症和有效性。 179 | - QiZhen-Chinese-LLaMA-7B- Checkpoint-3500：本品用于痛风患者高尿酸血症的长期治疗。
本品为处方药，必须由医生根据病情开处方拿药，并遵医嘱用药，包括用法、用量、用药时间等。不得擅自按照药物说明书自行用药。 180 | - QiZhen-CaMA-13B-Checkpoint-3600：本品用于痛风患者高尿酸血症的长期治疗。 181 | 182 | #### 测试2 183 | 184 | - Input：氟奋乃静癸酸酯注射液的适应症 185 | 186 | - ChatGLM：氟奋乃静癸酸酯注射液是一种药物,适应症如下: 1. 用于治疗抑郁症、焦虑症和失眠等神经精神症状; 2. 用于治疗癫痫、脑外伤和脑动脉硬化等神经系统疾病; 3. 用于缓解慢性疼痛,如痛经、关节炎等疼痛性疾病; 4. 用于改善学习能力和记忆能力,可用于儿童和青少年的智力开发和改善学习效果; 5. 用于预防艾滋病病毒感染和治疗艾滋病。 氟奋乃静癸酸酯注射液是一种口服用药,通常用于治疗抑郁症、焦虑症和失眠等疾病。需要根据医生的建议来制定治疗方案。使用氟奋乃静癸酸酯注射液时,应遵循医生的建议和药品说明书中的用药方法和剂量。 187 | 188 | - ChatGPT：氟奋乃静癸酸酯注射液（Flumazenil Injection）是一种用于治疗苯二氮䓬类药物（如安定类药物）过量或过度使用所引起的镇静或催眠状态的药物。它作为一种苯二氮䓬类受体拮抗剂，能够逆转苯二氮䓬类药物的中枢神经系统抑制作用。<氟奋乃静癸酸酯注射液主要用于以下适应症：

1. 苯二氮䓬类药物过量：当患者因安定类药物过量或过度使用而出现昏迷、嗜睡、呼吸抑制等症状时，氟奋乃静癸酸酯注射液可用于逆转这些症状。

2. 麻醉中逆转：氟奋乃静癸酸酯注射液可用于在手术或其他麻醉操作结束后逆转苯二氮䓬类药物的镇静效果，帮助患者恢复清醒状态。

3. 诊断用途：氟奋乃静癸酸酯注射液还可用于辅助诊断苯二氮䓬类药物中毒或过量情况。

请注意，氟奋乃静癸酸酯注射液应由专业医生根据具体情况进行合理的使用和监控。在使用该药物之前，应仔细阅读并遵循药物说明书，并遵循医生的指导和建议。 189 | 190 | - QiZhen-Chinese-LLaMA-7B- Checkpoint-3500：本品用于精神分裂症、躁狂症等。 也可用于肌紧张性疼痛。 191 | 192 | - QiZhen-CaMA-13B-Checkpoint-3600：本品用于精神分裂症的各种表现。 193 | 194 | #### 测试3 195 | 196 | - input：藏青果茶有哪些主要作用？ 197 | 198 | - ChatGLM：藏青果茶是一种天然的饮品,主要由青果、茶叶、蜂蜜等多种材料制成。它的主要作用包括: 1. 提高免疫力:藏青果茶中含有丰富的营养成分,如维生素C、维生素E等,可以增强人体的免疫力,提高身体的抵抗力,预防疾病的发生。 2. 促进消化:藏青果茶中含有丰富的纤维素,可以促进肠胃蠕动,帮助消化,预防便秘的发生。 3. 抗氧化作用:藏青果茶中的青果提取物具有较强的抗氧化作用,可以清除体内自由基,延缓衰老,预防疾病。 4. 降低血脂:藏青果茶中的茶叶提取物具有一定的降低血脂的作用,可以预防和治疗高血脂症。 5. 改善睡眠:藏青果茶中的蜂蜜具有一定的镇静作用,可以帮助缓解压力,改善睡眠质量,提高身体的自我修复能力。 199 | 200 | - ChatGPT：藏青果茶是一种以藏青果为主要原料制作的茶饮品，据我所知，它具有以下主要作用：

1. 抗氧化作用：藏青果富含多种天然抗氧化剂，如多酚类化合物和维生素C，可帮助清除体内自由基，减缓细胞老化，增强免疫力。

2. 促进消化：藏青果茶具有良好的消化刺激作用，有助于促进胃肠道蠕动，改善消化功能，缓解胃部不适和消化不良。

3. 提供营养：藏青果富含维生素C、维生素E、维生素B群、矿物质等营养物质，饮用藏青果茶可以为身体提供所需的营养元素。

4. 减肥瘦身：藏青果茶被认为具有减肥的效果，它可以促进新陈代谢，加速脂肪分解和燃烧，帮助减少体重和脂肪积累。

5. 抗炎镇痛：藏青果茶含有一些具有抗炎和镇痛作用的成分，可以缓解关节疼痛、肌肉酸痛和头痛等不适。

6. 改善睡眠：藏青果茶有助于放松神经系统，具有镇静和安抚作用，可以改善睡眠质量，缓解失眠问题。

需要注意的是，以上作用主要是基于藏青果茶的传统用途和某些研究结果，具体效果因人而异，且可能受到个体差异和使用方式的影响。如果您有特定的健康问题或需要服用药物，建议咨询医生或专业保健人员的建议。 201 | 202 | - QiZhen-Chinese-LLaMA-7B- Checkpoint-3500：清热解毒、生津止渴。 可缓解发热、口渴、咽喉肿痛等症状。 203 | 204 | - QiZhen-CaMA-13B-Checkpoint-3600：藏青果茶具有清热利咽、消肿止痛的功效。 205 | 206 | 藏青果茶可以用于缓解咽喉红肿疼痛、咽干灼热、声音嘶哑等症状。 207 | 208 | #### 测试4 209 | 210 | - input：赖特综合征临床表现有哪些？ 211 | 212 | - ChatGLM：赖特综合征(赖特综合征I型)是一种先天性心脏杂音,其临床表现包括: 213 | 214 | 1. 心脏杂音:赖特综合征I型的特征性心脏杂音是卵圆孔未闭引起的右向左分流杂音,通常表现为杂音强度、位置和频率的变异性。 215 | 216 | 2. 肺动脉高压:赖特综合征I型患者通常患有肺动脉高压,这可能导致呼吸衰竭和心脏功能衰竭。 217 | 218 | 3. 生长发育异常:大多数赖特综合征I型患者在儿童和青少年时期会表现出生长发育异常,如身材矮小、营养不良和巨人症。 219 | 220 | 4. 心脏结构异常:尽管杂音是赖特综合征I型的主要临床表现,但部分患者也可能患有其他心脏结构异常,如心脏瓣膜疾病和心律失常。 221 | 222 | 5. 其他症状:赖特综合征I型患者可能还具有其他症状,如疲劳、呼吸困难、胸痛、咳嗽、腹胀和便秘等。这些症状可能与其心脏杂音和肺动脉高压有关,但也可能与其他因素有关。 223 | 224 | 需要注意的是,赖特综合征I型是一种罕见的疾病,其临床表现可能因个体差异而有所不同。如果怀疑自己患有赖特综合征I型,请及时咨询医生进行诊断和治疗。 225 | 226 | - ChatGPT：赖特综合征是指胃食管反流病（GERD）引起的下食管括约肌失弛缓和食管蠕动减弱所导致的一种疾病。它的主要临床表现包括： 227 | 228 | 1. 吞咽困难：患者在吞咽时有被卡住的感觉，需要多次咽喉才能将食物吞咽下去。 229 | 230 | 2. 胸痛：赖特综合征的患者常常出现胸痛，特别是在吃饭或者躺下之后容易加重。 231 | 232 | 3. 嗳气和反酸：患者常感到嗳气和反酸，特别是在吃完饭或者躺下之后。 233 | 234 | 4. 声音嘶哑：由于口腔和喉咙区域受到反流物的刺激，患者的声音可能变得嘶哑。 235 | 236 | 5. 喉咙痛：反流物刺激喉咙黏膜所致。 237 | 238 | 需要注意的是，赖特综合征的症状与其他疾病相似，因此需要通过相关检查和诊断方法确定诊断。 239 | 240 | - QiZhen-CaMA-13B-Checkpoint-12400：赖特综合征的主要临床表现为急性或慢性关节炎、尿道炎、结膜炎及皮肤黏膜病变等。 241 | 242 | 赖特综合征是一种病因不明的慢性非特异性炎症性疾病，属于血清阴性脊柱关节炎的一种，其特征是反复发作性、游走性、不对称性的多关节炎，可伴有尿道炎、结膜炎、皮肤黏膜及眼部病变。 243 | 244 | 赖特综合征的主要临床表现为急性或慢性关节炎、尿道炎、结膜炎及皮肤黏膜病变等。 245 | 246 | 1.关节炎：最常见，任何关节均可受累，以膝、踝和足部小关节多见，局部可有红、肿、热、痛和功能障碍。 247 | 248 | 2.尿道炎：表现为尿频、尿急、尿痛、尿道口红肿、尿道分泌物增多等。 249 | 250 | 3.结膜炎：表现为单侧或双侧结膜炎症，可有结膜充血、水肿、眼睑浮肿、分泌物增多等。 251 | 252 | 4.皮肤黏膜病变：表现为皮肤脂溢性红斑、生殖器红斑、脓疱疹、皮肤脓疱疹、皮肤溃疡。 253 | 254 | ### 实验评测 255 | 256 | #### 药品适应症评测 257 | 258 | 评测标准：随机选择`94`种药品数据，按照“{药品}的适应病症”组成指令，分别让ChatGPT（gpt3.5）、ChatGLM、QiZhe做出回答，然后请专业的医学人员对三个`模型的答案`与`该药品的药品说明书`进行比对评分，以下是三个评分标准： 259 | 260 | - 标准1：模型答案命中一个适应症则回答正确； 261 | 262 | - 标准2：模型答案命中的适应症数目大于等于药品说明书适应症数目的1/2则回答正确； 263 | 264 | - 标准3：模型答案命中的适应症数目大于等于药品说明书适应症数目的2/3则回答正确； 265 | 266 | | 模型 | 标准1 | 标准2 | 标准3 | 267 | | :-------------------------------------: | :--------: | :--------: | :--------: | 268 | | ChatGLM | 39.36% | 23.16% | 14.74% | 269 | | ChatGPT | 47.87% | 30.85% | 15.96% | 270 | | QiZhen-Chinese-LLaMA-7B-Checkpoint-3500 | 77.66% | 55.32% | 40.00% | 271 | | QiZhen-Chinese-LLaMA-7B-Checkpoint-6000 | 90.43% | 73.40% | 65.96% | 272 | | QiZhen-CaMA-13B-Checkpoint-3600 | 82.29% | 60.62% | 47.92% | 273 | | QiZhen-CaMA-13B-Checkpoint-6000 | 90.43% | 80.85% | **72.34%** | 274 | | **QiZhen-CaMA-13B-Checkpoint-12400** | **91.49%** | **82.98%** | **72.34%** | 275 | 276 | **备注：** 277 | 278 | - 若QiZhen-Chinese-LLaMA-7B-Checkpoint-6000：回复有“复读现象”（我们正在持续修复这个问题），请将`repetition_penalty`参数调大； 279 | - QiZhen-ChatGLM-6B-Checkpoint-2500没有进行评测，因为我们在实验过程中发现ChatGLM在指令微调的过程中不能很好的满足医疗知识事实问答的要求：当要求其回复比较精准时，模型“复读”的现象比较严重；在解决“复读”的问题时，其回答的事实性很差（数据幻想严重）； 280 | - QiZhen-CaMA-13B-Checkpoint-3600：该版本回复内容基本没有“复读”现象； 281 | - QiZhen-CaMA-13B-Checkpoint-6000：相较于`QiZhen-CaMA-13B-Checkpoint-3600`我们增大了LoRA的参数量，效果得到了有效的提升； 282 | - 更详细的评测细节和数据后续会开源。 283 | 284 | #### 疾病评测 285 | 286 | 评测标准：随机选择`100`种疾病数据，按照“哪些药物能治疗{疾病}？”、“{疾病}需要做哪些检查？”、“{疾病}的临床表现有哪些？”组成“治疗药物”、“检查检验”、“临床表现”指令，分别让ChatGPT（gpt3.5）、ChatGLM、QiZhen0做出回答，然后请专业的医学人员对三个`模型的答案`与`启真医学知识库疾病知识`进行比对评分，以下是三个评分标准： 287 | 288 | - 标准1：模型答案命中一个“治疗药物”（“检查检验”、“临床表现”）则回答正确； 289 | 290 | - 标准2：模型答案命中的“治疗药物”（“检查检验”、“临床表现”）数目大于等于药品说明书适应症数目的1/2则回答正确； 291 | 292 | - 标准3：模型答案命中的“治疗药物”（“检查检验”、“临床表现”）数目大于等于药品说明书适应症数目的2/3则回答正确； 293 | 294 | | 模型 | 临床表现标准1 | 临床表现标准2 | 临床表现标准3 | 检查检验标准1 | 检查检验标准2 | 检查检验标准3 | 治疗药物标准1 | 治疗药物标准2 | 治疗药物标准3 | 295 | | :----------------------------------: | :-----------: | :-----------: | :-----------: | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 296 | | chatglm | 90.00% | 6.00% | 3.00% | 93.00% | 11.00% | 6.00% | 60.00% | 10.00% | 5.00% | 297 | | chatgpt | 94.00% | 11.00% | 4.00% | **97.00%** | 8.00% | 5.00% | 62.00% | 11.00% | 4.00% | 298 | | **QiZhen-CaMA-13B-Checkpoint-12400** | **95.00%** | **15.00%** | **7.00%** | **97.00%** | **20.00%** | **7.00%** | **75.00%** | **36.00%** | **23.00%** | 299 | 300 | 301 | 302 | 303 | ## 致谢 304 | 305 | 此外，本项目基于以下开源项目二次开发，在此对相关项目和研究开发人员表示感谢。 306 | 307 | - [LLaMA](https://github.com/facebookresearch/llama) 308 | - [Standford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) 309 | - [CaMA](https://github.com/zjunlp/CaMA) 310 | - [中文LLaMA & Alpaca大模型](https://github.com/ymcui/Chinese-LLaMA-Alpaca) 311 | 312 | 313 | 314 | ## License及免责声明 315 | 316 | ### License 317 | 318 | 详见[LICENSE](./LICENSE) 319 | 320 | ### 免责声明 321 | 322 | **本项目相关资源仅供学术研究之用，严禁用于商业用途。** 使用涉及第三方代码的部分时，请严格遵循相应的开源协议。模型生成的内容受模型计算、随机性和量化精度损失等因素影响，本项目不对其准确性作出保证。对于模型输出的任何内容，本项目不承担任何法律责任，亦不对因使用相关资源和输出结果而可能产生的任何损失承担责任。 323 | 324 | 325 | 326 | ## 引用说明 327 | 328 | 329 | Technical paper is coming soon. 330 | -------------------------------------------------------------------------------- /data/eval/药品适应症评测数据集.csv: -------------------------------------------------------------------------------- 1 | 药品,药品适应症,医学专家标注的适应病症,instruction 2 | 地屈孕酮片,

本品用于治疗内源性孕酮不足引起的疾病，如痛经、子宫内膜异位症、继发性闭经、月经周期不规则、功能失调性子宫出血、经前期综合征、孕激素缺乏所致先兆性流产或习惯性流产、黄体不足所致不孕症。

,内源性孕酮不足|痛经|子宫内膜异位症|继发性闭经|月经周期不规则|功能失调性子宫出血|经前期综合征,地屈孕酮片的适应病症 3 | 替格瑞洛片,

本品适用于急性冠脉综合征（不稳定性心绞痛、非ST段抬高心肌梗死或ST段抬高心肌梗死）患者，包括接受药物治疗和经皮冠状动脉介入（PCI）治疗的患者，降低血栓性心血管事件的发生率。与氯吡格雷相比，本品可以降低心血管死亡、心肌梗死或卒中复合终点的发生率，两治疗组之间的差异来源于心血管死亡和心肌梗死，而在卒中方面无差异。在ACS患者中，对本品与阿司匹林联合用药进行了研究。结果发现，阿司匹林维持剂量大于1mg会降低替格瑞洛减少复合终点事件的临床疗效，因此，阿司匹林的维持剂量不能超过每日1mg。

,急性冠脉综合征|不稳定性心绞痛|非ST段抬高心肌梗死|ST段抬高心肌梗死,替格瑞洛片的适应病症 4 | 劳拉西泮片,

本品用于焦虑障碍的治疗或用于缓解焦虑症状，以及与抑郁症状相关的焦虑的短期治疗。与日常生活压力相关的焦虑或紧张，通常不需要抗焦虑药的治疗。劳拉西泮长期应用的效果，即应用4个月以上的效果还未经系统的临床研究评估。医师应定期重新评估该药对个体患者的有效性。

,焦虑障碍|焦虑,劳拉西泮片的适应病症 5 | 依托考昔片,

本品用于治疗骨关节炎急性期和慢性期的症状和体征，亦可治疗急性痛风性关节炎。

,骨关节炎急性期|骨关节炎慢性期|急性痛风性关节炎,依托考昔片的适应病症 6 | 左甲状腺素钠片,

本品用于先天性甲状腺功能减退症（克汀病）与儿童及成人的各种原因引起的甲状腺功能减退症的长期替代治疗，也可用于单纯性甲状腺肿，慢性淋巴性甲状腺炎，甲状腺癌手术后的抑制（及替代）治疗，也可用于诊断甲状腺功能亢进的抑制试验。

,先天性甲状腺功能减退症|克汀病|单纯性甲状腺肿|慢性淋巴性甲状腺炎|甲状腺癌手术后,左甲状腺素钠片的适应病症 7 | 盐酸普拉克索片,

本品用于治疗特发性帕金森病的体征和症状，单独（无左旋多巴）或与左旋多巴联用。例如，在疾病后期左旋多巴的疗效逐渐减弱或者出现变化和波动时（剂末现象或“开关”波动），需要应用本品。

,特发性帕金森病,盐酸普拉克索片的适应病症 8 | 莨菪浸膏片,

本品适用于胃十二指肠溃疡和胆、肾、肠等绞痛。

,胃溃疡|十二指肠溃疡|胆绞痛|肾绞痛|肠绞痛,莨菪浸膏片的适应病症 9 | 格隆溴铵片,

本品用于胃、十二指肠溃疡及慢性胃炎等。

,胃溃疡|十二指肠溃疡|慢性胃炎,格隆溴铵片的适应病症 10 | 氨甲环酸片,

本品用于急性或慢性、局限性或全身性原发性纤维蛋白溶解亢进所致的各种出血。弥散性血管内凝血所致的继发性高纤溶状态，在未肝素化前，慎用本品。本品还适用于：

1、前列腺、尿道、肺、脑、子宫、肾上腺、甲状腺、肝等富有纤溶酶原激活物脏器的外伤或手术出血。

2、用作组织型纤溶酶原激活物（t-PA）、链激酶及尿激酶的拮抗物。

3、人工流产、胎盘早期剥落、死胎和羊水栓塞引起的纤溶性出血。

4、局部纤溶性增高的月经过多，眼前房出血及严重鼻出血。

5、用于防止或减轻因子Ⅷ因子Ⅸ缺乏的血友病患者拔牙或口腔手术后的出血。

6、中枢动脉瘤破裂所致的轻度出血，如蛛网膜下腔出血和颅内动脉瘤出血；应用本品止血优于其他抗纤溶药，但必须注意并发脑水肿或脑梗塞的危险性，至于重症有手术指征患者，本品仅可作辅助用药。

7、用于治疗遗传性血管神经性水肿。可减少其发作次数和严重度。

8、血友病患者发生活动性出血，可联合应用本药。

9、可治疗溶栓过量所致的严重出血。

,前列腺外伤|尿道外伤|肺外伤|脑外伤|子宫外伤|肾上腺外伤|甲状腺外伤|肝外伤|手术出血|眼前房出血|严重鼻出血|蛛网膜下腔出血|颅内动脉瘤|遗传性血管神经性水肿,氨甲环酸片的适应病症 11 | 盐酸阿罗洛尔片,

本品用于原发性高血压（轻度-中度）、心绞痛、心动过速性心律失常、原发性震颤。

,轻度原发性高血压|中度原发性高血压|心绞痛|心动过速性心律失常|原发性震颤,盐酸阿罗洛尔片的适应病症 12 | 盐酸达泊西汀片,

本品用于治疗符合下列所有条件的18至64岁男性早泄（PE）患者：

1、阴茎在插入阴道之前、过程当中或者插入后不久，以及未获性满足之前仅仅由于极小的性刺激即发生持续的或反复的射精。

2、因早泄（PE）而导致的显著性个人苦恼或人际交往障碍。

3、射精控制能力不佳。

,早泄,盐酸达泊西汀片的适应病症 13 | 他达拉非片,

本品用于治疗男性勃起功能障碍。需要性刺激以使本品生效。他达拉非不能用于女性。

,男性勃起功能障碍,他达拉非片的适应病症 14 | 枸橼酸氯米芬片,

本品用于诱导下述情况妇女的排卵：下丘脑-垂体机能障碍，包括多囊性卵巢综合征（PCOS）；诱导接受辅助受孕技术如体外受精（IVF）而行超数排卵妇女的多卵泡发育。从事不育症治疗时应按照临床医师指导用药。

,下丘脑-垂体机能障碍|多囊性卵巢综合征|辅助受孕|体外受精|不育症,枸橼酸氯米芬片的适应病症 15 | 依非韦伦片,

本品用于与其他抗病毒药物联合治疗HIV-1感染的成人、青少年及儿童。

,HIV-1感染,依非韦伦片的适应病症 16 | 拉替拉韦钾片,

1、本品适用于与其他抗反转录病毒药物联合使用，用于治疗人免疫缺陷病毒（HIV-1）。

2、本品适应症的确立是基于对三个临床对照研究48周时的血浆HIV-1RNA水平进行的分析。这些研究中的两个是在接受过三种抗反转录病毒抑制剂治疗（非核苷类反转录酶抑制剂、核苷类反转录酶抑制剂、蛋白酶抑制剂）并有疾病临床进展的成年患者中进行的。另一个是在既往未治疗的患者中进行的。

3、本品与其他活性药物联合使用时产生治疗应答的可能性更大。

4、在儿童患者中尚无本品的安全性和有效性数据。

,HIV-1感染,拉替拉韦钾片的适应病症 17 | 盐酸沙格雷酯片,

本品适用于改善慢性动脉闭塞症所引起的溃疡、疼痛以及冷感等缺血性诸症状。

,慢性动脉闭塞症,盐酸沙格雷酯片的适应病症 18 | 尼可地尔片,

本品用于冠心病，心绞痛的治疗。

,冠心病|心绞痛,尼可地尔片的适应病症 19 | 盐酸厄洛替尼片,

1、厄洛替尼单药适用于表皮生长因子受体（EGFR）基因具有敏感突变的局部晚期或转移性非小细胞肺癌（NSCLC）患者的治疗，包括一线治疗、维持治疗，或既往接受过至少一次化疗进展后的二线及以上治疗。

2、两项多中心、随机、安慰剂对照的Ⅲ期试验结果显示，厄洛替尼联合含铂化疗方案（卡铂+紫杉醇；或者吉西他滨+顺铂）作为局部晚期或转移的NSCLC患者一线治疗，相对单用含铂化疗未增加临床获益，因此不推荐用于上述情况的一线治疗。

,局部晚期非小细胞肺癌|转移性非小细胞肺癌,盐酸厄洛替尼片的适应病症 20 | 甲磺酸溴隐亭片,

1、内分泌系统疾病：泌乳素依赖性月经周期紊乱和不育症（伴随高或正常泌乳素血症）、闭经（伴有或不伴有泌乳）、月经过少、黄体功能不足和药物诱导的高泌乳激素症（抗精神病药物和高血压治疗药物）。

2、非催乳素依赖性不育症：多囊性卵巢综合症、与抗雌激素联合运用（如：氯底酚胺）治疗无排卵症。

3、高泌乳素瘤：垂体小腺瘤的保守治疗，在手术前抑制腺瘤生长或减少腺瘤的体积，使切除容易进行；术后可用于降低仍然较高的泌乳素水平。

4、肢端肥大症：单独应用或联合放疗、手术等可降低生长激素的血浆水平。

5、抑制生理性泌乳：分娩或流产后通过抑制泌乳来抑制乳腺充血、肿胀，从而可预防产后乳腺炎。

6、良性乳腺疾病：缓和或减轻经前综合症及乳腺结节（或囊性）乳腺疾病相关性乳腺疼痛。

7、神经系统疾病：用于各期自发性和脑炎后所致帕金森病的单独治疗，或与其他抗帕金森病药物联合使用。

,泌乳素依赖性月经周期紊乱|泌乳素依赖性不育症|闭经|月经过少|黄体功能不足|药源性高泌乳激素症|非催乳素依赖性不育症|多囊性卵巢综合征|高泌乳素瘤|垂体小腺瘤|肢端肥大症|良性乳腺疾病|经前综合症|乳腺结节|神经系统疾病|自发性帕金森病,甲磺酸溴隐亭片的适应病症 21 | 维格列汀片,

本品适用于治疗2型糖尿病，当二甲双胍作为单药治疗用至最大耐受剂量仍不能有效控制血糖时，本品可与二甲双胍联合使用。

,2型糖尿病,维格列汀片的适应病症 22 | 恩他卡朋片,

本品可作为标准药物左旋多巴/苄丝肼或左旋多巴/卡比多巴的辅助用药，用于治疗以上药物不能控制的帕金森病及剂末现象（症状波动）。

,剂末现象,恩他卡朋片的适应病症 23 | 盐酸氟西汀分散片,

本品适用于抑郁症、强迫症、神经性贪食症，作为心理治疗的辅助用药，以减少贪食和导泻行为。

,抑郁症|强迫症|神经性贪食症|贪食,盐酸氟西汀分散片的适应病症 24 | 恩曲他滨替诺福韦片,

本品用于与其他抗逆转录病毒药物联用，治疗成人和12岁（含）以上儿童的HIV-1感染。当开始使用本品治疗HIV-1感染时，应考虑下列因素：建议本品不要作为三联核苷治疗方案的一个组分使用；本品不应与恩曲他滨、替诺福韦二吡呋酯、拉米夫定或含有三者的固定剂量复方合并使用；接受过治疗的患者，本品的使用应按照实验室检查结果和患者治疗史进行。

,HIV-1感染,恩曲他滨替诺福韦片的适应病症 25 | 甲泼尼龙片,

糖皮质激素只能作为对症治疗，只有在某些内分泌失调的情况下，才能作为替代药品。甲泼尼龙片可用于非内分泌失调症。

1、风湿性疾病：作为辅助疗法短期使用（帮助患者度过急性期或危重期），用于：银屑病性关节炎、类风湿性关节炎，包括青少年类风湿性关节炎（有些患者可能需要低剂量维持治疗）、强直性脊柱炎急性或亚急性滑囊炎、急性非特异性踺鞘炎、急性痛风性关节炎、创伤后骨关节炎、骨关节炎引发的滑膜炎上踝炎。

2、胶原疾病：用于疾病危重期或作为下列疾病的维持治疗：系统性红斑狼疮、全身性皮肌炎（多肌炎）、风湿性多肌痛、巨细胞关节炎、急性风湿性心肌炎。

3、皮肤疾病：天疱疮、大疱疱疹性皮炎、严重的各类红斑病（Steven-Johnson综合症）、剥脱性皮炎、覃样真菌病、严重的银屑病、严重的脂溢性皮炎。

4、过敏状态：用于控制如下以常规疗法难以处理的严重或损伤机能的过敏性疾病：季节性或全年性过敏性鼻炎、血清病、支气管哮喘、药物过敏反应接触性皮炎、异位性皮炎。

5、眼部疾病：眼部及其附件严重的急慢性过敏和炎症反应，例如：过敏性角膜边缘溃疡眼部带状疱疹、前视网膜炎扩散性后房色素层炎和脉络膜炎交感性眼炎、过敏性结膜炎、角膜炎、脉络膜视网膜炎、视神经炎、虹膜炎、虹膜睫状体炎。

6、呼吸道疾病：有症状的肺部肉牙肿其它方法不能控制的吕弗勒氏综合征（Loeffler’sSyndrome）、铍中毒与适当的抗结核化疗法合用于暴发性或扩散性肺结核吸入性肺炎、血液病、特发性血小板减少性紫癜、继发性血小板减少症、获得性（自身免疫性）溶血性贫血、成红细胞减少症（RBC贫血）、先天性（红细胞）低增生性贫血。

7、肿瘤用于下列疾病的缓解治疗，白血病和淋巴瘤儿童急性白血病。

8、水肿状态用于无尿毒症的自发性或狼疮性性肾病综合征的利尿及缓解蛋白尿。

9、胃肠道疾病帮助患者度过以下疾病的危重期：溃疡性结肠炎、局限性回肠炎。

10、神经系统：各类硬化症的急性恶化脑部肿瘤引起的水肿。

11、其它与适当的抗结核化疗法合用，用于伴有蛛网膜下腔阻塞或趋于阻塞的结核性脑膜炎累及神经或心肌的旋毛虫病。

12、器官移植内分泌失调疾病原发或继发性肾上腺皮质不全（氢化可的松和可的松为首选药物，如有需要，合成的糖皮质激素可与盐皮质激素合用，在婴儿期，盐皮质激素的供给尤为重要）先天性肾上腺增生非化脓性甲状腺炎癌症引起的高钙血症。

,非内分泌失调症|银屑病性关节炎|类风湿性关节炎|青少年类风湿性关节炎|强直性脊柱炎|急性滑囊炎|亚急性滑囊炎|急性非特异性踺鞘炎|急性痛风性关节炎|创伤后骨关节炎|系统性红斑狼疮|全身性皮肌炎|风湿性多肌痛|巨细胞关节炎|急性风湿性心肌炎|天疱疮|大疱疱疹性皮炎|严重红斑病|Steven-Johnson综合症|剥脱性皮炎|覃样真菌病|严重银屑病|严重脂溢性皮炎|季节性过敏性鼻炎|全年性过敏性鼻炎|血清病|支气管哮喘|药物过敏反应|接触性皮炎|异位性皮炎|过敏性角膜边缘溃疡|眼部带状疱疹|前视网膜炎|扩散性后房色素层炎|脉络膜炎|交感性眼炎|过敏性结膜炎|角膜炎|脉络膜视网膜炎|视神经炎|虹膜炎|虹膜睫状体炎|吕弗勒氏综合征|铍中毒|暴发性肺结核|扩散性肺结核|吸入性肺炎|血液病|特发性血小板减少性紫癜|继发性血小板减少症|获得性溶血性贫血|成红细胞减少症|先天性低增生性贫血|白血病|淋巴瘤|儿童急性白血病|溃疡性结肠炎危重期|局限性回肠炎危重期|器官移植内分泌失调疾病|肾上腺皮质不全|先天性肾上腺增生|非化脓性甲状腺炎,甲泼尼龙片的适应病症 26 | 硝呋太尔阴道片,

本品用于细菌性阴道病、滴虫性阴道炎、外阴阴道念珠菌病、阴道混合感染。

,细菌性阴道病|滴虫性阴道炎|外阴阴道念珠菌病|阴道混合感染,硝呋太尔阴道片的适应病症 27 | 酒石酸伐尼克兰片,

本品适用于成人戒烟。

,烟草成瘾,酒石酸伐尼克兰片的适应病症 28 | 酒石酸唑吡坦片,

本品限用于下列情况严重睡眠障碍的治疗：偶发性失眠症、暂时性失眠症。

,偶发性失眠症|暂时性失眠症,酒石酸唑吡坦片的适应病症 29 | 依折麦布片,

1、原发性高胆固醇血症：本品作为饮食控制以外的辅助治疗，可单独或与HMG-CoA还原酶抑制剂（他汀类）联合应用于治疗原发性（杂合子家族性或非家族性）高胆固醇血症，可降低总胆固醇（TC）、低密度脂蛋白胆固醇（LDL-C）、载脂蛋白B（ApoB）。

2、纯合子家族性高胆固醇血症（HoFH）：本品与他汀类联合应用，可作为其他降脂治疗的辅助疗法（如LDL-C血浆分离置换法），或在其他降脂治疗无效时用于降低HoFH患者的TC和LDL-C水平。

3、纯合子谷甾醇血症（或植物甾醇血症）：本品作为饮食控制以外的辅助治疗，用于降低纯合子家族性谷甾醇血症患者的谷甾醇和植物甾醇水平。

,原发性高胆固醇血症|纯合子家族性高胆固醇血症|纯合子谷甾醇血症,依折麦布片的适应病症 30 | 吡罗昔康贴片,

本品用于缓解骨关节炎、腱鞘炎、肌痛、骨关节痛、外伤后及骨折愈合后引起的疼痛。

,骨关节炎|腱鞘炎|肌痛|骨关节痛,吡罗昔康贴片的适应病症 31 | 匹维溴铵片,

本品用于对症治疗与肠道功能紊乱有关的疼痛、排便异常和胃肠不适；对症治疗与胆道功能紊乱有关的疼痛；为钡灌肠做准备。

,肠道功能紊乱|胆道功能紊乱,匹维溴铵片的适应病症 32 | 马来酸氟伏沙明片,

本品用于抑郁症及相关症状的治疗，强迫症症状治疗。

,抑郁症|强迫症,马来酸氟伏沙明片的适应病症 33 | 氯诺昔康片,

本品适用于手术后急性疼痛、外伤引起的中-重度疼痛、急性坐骨神经痛和腰痛、晚期癌痛，亦可用于慢性腰痛、骨关节炎、类风湿性关节炎和强直性脊柱炎的治疗。

,急性坐骨神经痛|腰痛|晚期癌痛|慢性腰痛|骨关节炎|类风湿性关节炎|强直性脊柱炎,氯诺昔康片的适应病症 34 | 盐酸班布特罗片,

本品用于支气管哮喘、慢性喘息性支气管炎、阻塞性肺气肿和其它伴有支气管痉挛的肺部疾病。

,支气管哮喘|慢性喘息性支气管炎|阻塞性肺气肿|支气管痉挛,盐酸班布特罗片的适应病症 35 | 达格列净片,

1、在饮食和运动基础上，本品可作为单药治疗用于2型糖尿病成人患者改善血糖控制。

2、重要的使用限制：本品不适用于治疗1型糖尿病或糖尿病酮症酸中毒。

,2型糖尿病,达格列净片的适应病症 36 | 比卡鲁胺片,

1、50mg每日：与促黄体生成素释放激素（LHRH）类似物或外科睾丸切除术联合应用于晚期前列腺癌的治疗。

2、150mg每日：用于治疗局部晚期、无远处转移的前列腺癌患者，这些患者不适宜或不愿接受外科去势术或其他内科治疗。

,晚期前列腺癌,比卡鲁胺片的适应病症 37 | 硫酸氨基葡萄糖钾片,

本品适用于全身各关节部位的骨性关节炎，如膝关节、髋关节、脊椎、肩、手、腕关节和踝关节等。

,骨性关节炎|膝关节炎|髋关节炎|脊柱炎|肩关节炎|手关节炎|腕关节炎|踝关节炎,硫酸氨基葡萄糖钾片的适应病症 38 | 琥珀酸索利那新片,

本品适用于膀胱过度活动症患者伴有的尿失禁和/或尿频、尿急症状的治疗。

,膀胱过度活动症,琥珀酸索利那新片的适应病症 39 | 盐酸普萘洛尔片,

1、本品用于作为二级预防，降低心肌梗死死亡率。

2、高血压（单独或与其它抗高血压药合用）。

3、劳力型心绞痛。

4、控制室上性快速心律失常、室性心律失常，特别是与儿茶酚胺有关或洋地黄引起心律失常。可用于洋地黄疗效不佳的房扑、房颤心室率的控制，也可用于顽固性期前收缩，改善患者的症状。

5、减低肥厚型心肌病流出道压差，减轻心绞痛、心悸与昏厥等症状。

6、配合α受体阻滞剂用于嗜铬细胞瘤病人控制心动过速。

7、用于控制甲状腺机能亢进症的心率过快，也可用于治疗甲状腺危象。

,高血压|劳力型心绞痛|室上性快速心律失常|室性心律失常|顽固性期前收缩|心绞痛|心悸|昏厥|甲状腺危象,盐酸普萘洛尔片的适应病症 40 | 恩替卡韦片,

本品适用于病毒复制活跃、血清丙氨酸氨基转移酶(ALT)持续升高或肝脏组织学显示有活动性病变的慢性成人乙型肝炎的治疗。

,慢性乙型肝炎病毒复制活跃期,恩替卡韦片的适应病症 41 | 甲苯磺酸索拉非尼片,

用于治疗不能手术或远处转移的肝细胞癌；治疗不能手术的晚期肾细胞癌。

,肝细胞癌远处转移|晚期肾细胞癌,甲苯磺酸索拉非尼片的适应病症 42 | 利伐沙班片,

1、用于择期髋关节或膝关节置换手术成年患者，以预防静脉血栓形成(VTE)。

2、用于治疗成人深静脉血栓形成（DVT），降低急性DVT后DVT复发和肺栓塞（PE）的风险。

3、用于具有一种或多种危险因素（例如：充血性心力衰竭、高血压、年龄≥75岁、糖尿病、卒中或短暂性脑缺血发作病史）的非瓣膜性房颤成年患者，以降低卒中和全身性栓塞的风险。

,择期髋关节置换手术|择期膝关节置换手术|深静脉血栓形成,利伐沙班片的适应病症 43 | 盐酸莫西沙星片,

本品用于治疗成人（≥18岁）敏感细菌所引起的下列感染：

1、急性细菌性鼻窦炎：由肺炎链球菌、流感嗜血杆菌或卡他莫拉菌引起。由于使用氟喹诺酮类药物（包括盐酸莫西沙星）已有报道发生严重不良反应，且对于一些患者，急性细菌性鼻窦炎有自限性，应在没有其它药物治疗时方可使用盐酸莫西沙星。

2、慢性支气管炎急性发作：由肺炎链球菌、流感嗜血杆菌、副流感嗜血杆菌、肺炎克雷伯菌、甲氧西林敏感的金黄色葡萄球菌或卡他莫拉菌引起。由于使用氟喹诺酮类药物（包括盐酸莫西沙星）已有报道发生严重不良反应，且对于一些患者，慢性支气管炎急性发作有自限性，应在没有其它药物治疗时方可使用盐酸莫西沙星。

3、社区获得性肺炎：由肺炎链球菌（包括多药耐药株）、流感嗜血杆菌、卡他莫拉菌、甲氧西林敏感的金黄色葡萄球菌、肺炎克雷伯菌、肺炎支原体或肺炎衣原体引起。

4、非复杂性皮肤和皮肤组织感染：由甲氧西林敏感的金黄色葡萄球菌或化脓性链球菌引起。

5、复杂性皮肤和皮肤组织感染：由甲氧西林敏感的金黄色葡萄球菌、大肠埃希氏菌、肺炎克雷伯氏菌或阴沟肠杆菌引起。

6、复杂性腹腔内感染：由大肠杆菌、脆弱类杆菌、咽峡炎链球菌、星座链球菌、粪肠球菌、变形杆菌、产气荚膜梭菌、多形类杆菌或消化链球菌属等引起，包括腹腔脓肿。

7、鼠疫，包括成人因鼠疫耶尔森氏杆菌（Y.pestis）引起的肺鼠疫和败血性鼠疫，也可预防鼠疫。由于可行性原因，无法对人类进行有效性临床研究，因此，该适应症仅是基于动物有效性研究数据所确定。

8、不伴有输卵管-卵巢或盆腔脓肿的轻至中度盆腔炎性疾病（即：女性上生殖道感染，包括输卵管炎和子宫内膜炎）。由于淋病奈瑟氏菌对莫西沙星的耐药性增加，不建议本品用于单药治疗轻至中度盆腔炎性疾病，而是应当与另一种合适的抗菌药物（例如：头孢菌素）联合用药，除非能够排除对莫西沙星耐药的淋病奈瑟氏菌。

,急性肺炎链球菌性鼻窦炎|急性流感嗜血杆菌性鼻窦炎|急性卡他莫拉菌性鼻窦炎|慢性支气管炎急性发作|社区获得性肺炎|非复杂性皮肤组织化脓性链球菌感染|复杂性皮肤组织金黄色葡萄球菌感染|复杂性腹腔内大肠杆菌感染|鼠疫|女性上生殖道感染,盐酸莫西沙星片的适应病症 44 | 戊酸雌二醇片,

本品用于治疗主要与自然或人工绝经相关的雌激素缺乏：血管舒缩性疾病（潮热），生殖泌尿道营养性疾病（外阴阴道萎缩，性交困难，尿失禁）以及精神性疾病（睡眠障碍，衰弱）；也用于预防原发性或继发性雌激素缺乏所造成的骨质丢失。

,血管舒缩性疾病|潮热|生殖泌尿道营养性疾病|外阴阴道萎缩|性交困难|尿失禁|睡眠障碍|衰弱,戊酸雌二醇片的适应病症 45 | 克霉唑阴道片,

本品用于念珠菌性外阴阴道炎。

,念珠菌性外阴阴道炎,克霉唑阴道片的适应病症 46 | 颠茄磺苄啶片,

本品用于痢疾杆菌引起的慢性菌痢和其他敏感致病菌引起的肠炎等。

,慢性菌痢|肠炎,颠茄磺苄啶片的适应病症 47 | 马来酸多潘立酮片,"

本品用于治疗下列疾病或药物引起的消化功能异常（恶心、呕吐、食欲不振、腹胀、上腹部不适、腹痛、胃烧灼、嗳气等）：

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1、成人：慢性胃炎，胃下垂症，反流性食道炎，胃切除症候群，使用抗恶性肿瘤药或左旋多巴等引起的上述诸症状。

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2、儿童：周期性呕吐症，上呼吸道感染症，使用抗恶性肿瘤药等引起的消化功能异常。

",慢性胃炎|胃下垂|反流性食道炎|胃切除症候群|儿童周期性呕吐症|儿童上呼吸道感染症,马来酸多潘立酮片的适应病症 50 | 富马酸比索洛尔片,

本品用于治疗高血压、冠心病（心绞痛）、伴有心室收缩功能减退的中度至重度慢性稳定性心力衰竭。在使用本品前，需要遵医嘱接受ACE抑制剂、利尿剂和选择性使用强心苷类药物治疗。

,高血压|冠心病|心绞痛,富马酸比索洛尔片的适应病症 51 | 阿托伐他汀钙片,

1、高胆固醇血症：原发性高胆固醇血症患者，包括家族性高胆固醇血症（杂合子型）或混合性高脂血症（相当于Fredrickson分类法的IIa和IIb型）患者，如果饮食治疗和其它非药物治疗疗效不满意，应用本品可治疗其总胆固醇（TC）升高、低密度脂蛋白胆固醇（LDL-C）升高、载脂蛋白B（ApoB）升高和甘油三酯（TG）升高。在纯合子家族性高胆固醇血症患者，阿托伐他汀钙可与其它降脂疗法（如低密度脂蛋白血浆透析法）合用或单独使用（当无其它治疗手段时），以降低总胆固醇（TC）和低密度脂蛋白胆固醇（LDL-C）。

2、冠心病：冠心病或冠心病等危症（如糖尿病，症状性动脉粥样硬化性疾病等）合并高胆固醇血症或混合型血脂异常的患者，本品适用于：降低非致死性心肌梗死的风险、降低致死性和非致死性卒中的风险、降低血管重建术的风险、降低因充血性心力衰竭而住院的风险、降低心绞痛的风险。

,高胆固醇血症|冠心病,阿托伐他汀钙片的适应病症 52 | 卡维地洛片,

1、本品用于原发性高血压：可单独使用或与其他抗高血压药特别是噻嗪类利尿剂联合使用。

2、治疗有症状的充血性心力衰竭：卡维地洛用于治疗有症状的充血性心力衰竭可降低死亡率以及心血管事件的住院率，改善病人的一般情况并减慢疾病进展。

3、卡维地洛可作为标准治疗的附加治疗，也可用于不耐受ACEI或没有使用洋地黄、肼苯哒嗪、硝酸盐类药物治疗的病人。

,原发性高血压,卡维地洛片的适应病症 53 | 盐酸依匹斯汀片,

本品用于成人所患的过敏性鼻炎、荨麻疹、湿疹、皮炎、皮肤瘙痒症、痒疹、伴有瘙痒的寻常性银屑病及过敏性支气管哮喘的防治。

,过敏性鼻炎|荨麻疹|湿疹|皮炎|皮肤瘙痒症|痒疹|寻常性银屑病|过敏性支气管哮喘,盐酸依匹斯汀片的适应病症 54 | 替比夫定片,

1、替比夫定用于有病毒复制证据以及有血清转氨酶（ALT或AST）持续升高或肝组织活动性病变证据的慢性乙型肝炎成人患者。

2、本适应症基于核苷类似物初治的、HBeAg阳性和HBeAg阴性的、肝功能代偿的，慢性乙型肝炎成年患者的病毒学、血清学、生化学和组织学应答结果。未在合并HIV、HCV或HDV感染的乙型肝炎患者中评估过替比夫定的作用。未在肝移植患者或失代偿肝病患者中评估过替比夫定的作用。对核苷类似物逆转录酶抑制剂耐药的慢性乙型肝炎患者，尚无应用替比夫定的设计良好的对照研究，但估计其可能与拉米夫定存在交叉耐药。

,慢性乙型肝炎病毒复制活跃期,替比夫定片的适应病症 55 | 环酯红霉素片,

本品用于由敏感菌引起的感染，如扁桃体炎、咽炎、细菌性肺炎、支原体肺炎、口腔炎、军团病、白喉、百日咳、猩红热、红癣、类丹毒、淋病、早期梅毒、软下疳、尿道炎、弯曲菌肠炎、阿米巴肠炎等。

,扁桃体炎|咽炎|细菌性肺炎|支原体肺炎|口腔炎|军团病|白喉|百日咳|猩红热|红癣|类丹毒|淋病|早期梅毒|软下疳|尿道炎|弯曲菌肠炎|阿米巴肠炎,环酯红霉素片的适应病症 56 | 盐酸曲唑酮片,

本品用于治疗各种类型的抑郁症和伴有抑郁症状的焦虑症以及药物依赖者戒断后的情绪障碍。

,抑郁症|焦虑症,盐酸曲唑酮片的适应病症 57 | 盐酸苯海索片,

本品用于帕金森病、帕金森综合征。也可用于药物引起的锥体外系疾患。

,帕金森病|帕金森综合征|药源性锥体外系疾患,盐酸苯海索片的适应病症 58 | 盐酸异丙嗪片,"

1、皮肤粘膜的过敏：本品适用于长期的、季节性的过敏性鼻炎，血管舒缩性鼻炎，接触过敏源或食物而致的过敏性结膜炎，荨麻疹，血管神经性水肿，对血液或血浆制品的过敏反应，皮肤划痕症。必要时可与肾上腺素合用，作为本药的辅助剂。

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2、晕动病：防治晕车、晕船、晕飞机。

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3、镇静、催眠：适用于术前、术后和产科。此外，也可用于减轻成人及儿童的恐惧感，呈浅睡眠状态。

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4、恶心、呕吐的治疗：适用于一些麻醉和手术后的恶心、呕吐，也用于防治放射病性或药源性恶心、呕吐。

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5、术后疼痛；可与止痛药合用，作为辅助用药。

",过敏性鼻炎|血管舒缩性鼻炎|过敏性结膜炎|荨麻疹|血管神经性水肿|皮肤划痕症|晕动病|晕车|晕船|晕飞机|放射病性恶心|药源性恶心|药源性呕吐|术后疼痛,盐酸异丙嗪片的适应病症 63 | 盐酸可乐定片,

本品适用于高血压（不作为第一线用药）；高血压急症；偏头痛、绝经期潮热、痛经，以及戒绝阿片瘾毒症状。

,高血压|高血压急症|偏头痛|绝经期潮热|痛经，阿片瘾毒戒绝症状,盐酸可乐定片的适应病症 64 | 熊去氧胆酸片,

本品用于胆固醇型胆结石，形成及胆汁缺乏性脂肪泻，也可用于预防药物性结石形成及治疗脂肪痢（回肠切除术后）。

,胆固醇型胆结石|胆汁缺乏性脂肪泻|药物性结石形成|回肠切除术后脂肪痢,熊去氧胆酸片的适应病症 65 | 复方卡托普利片,

1、高血压，可单独应用或与其他降压药合用。

2、心力衰竭，可单独应用或与强心利尿药合用。

,高血压|心力衰竭,复方卡托普利片的适应病症 66 | 卡托普利片,

本品适用于高血压，心力衰竭。

,高血压|心力衰竭,卡托普利片的适应病症 67 | 黄豆苷元片,

本品适用于高血压病及症状性高血压、冠心病、脑血栓、眩晕症、突发性耳聋的辅助治疗。也可用于妇女更年期综合症。

,高血压|冠心病|脑血栓|眩晕症|突发性耳聋|妇女更年期综合征,黄豆苷元片的适应病症 68 | 阿魏酸钠片,

本品用于缺血性心脑血管病的辅助治疗。

,缺血性心脑血管病,阿魏酸钠片的适应病症 69 | 盐酸舍曲林片,

1、本品用于治疗抑郁症的相关症状，包括伴随焦虑、有或无躁狂史的抑郁症。疗效满意后，继续服用舍曲林可有效地防止抑郁症的复发和再发。

2、本品也用于治疗强迫症。疗效满意后，舍曲林在治疗强迫症两年的时间内，仍保持它的有效性、安全性和耐受性。

,抑郁症|强迫症,盐酸舍曲林片的适应病症 70 | 伏立康唑片,

本品用于治疗侵袭性曲霉病。治疗对氟康唑耐药的念珠菌引起的严重侵袭性感染（包括克柔念珠菌）。治疗由足放线病菌属和镰刀菌属引起的严重感染。本品应主要用于治疗免疫缺陷患者中进行性的、可能威胁生命的感染。

,侵袭性曲霉病|放线病菌属严重感染|镰刀菌属严重感染,伏立康唑片的适应病症 71 | 肌苷片,

本品用于急、慢性肝炎的辅助治疗。

,急性肝炎|慢性肝炎,肌苷片的适应病症 72 | 利血平片,

本品适用于治疗高血压（不推荐为一线用药）。

,高血压,利血平片的适应病症 73 | 氧氟沙星片,

本品用于敏感菌引起的：

1、泌尿生殖系统感染，包括单纯性、复杂性尿路感染、细菌性前列腺炎、淋病奈瑟菌尿道炎或宫颈炎（包括产酶株所致者）。

2、呼吸道感染，包括敏感革兰阴性杆菌所致支气管感染急性发作及肺部感染。

3、胃肠道感染，由志贺菌属、沙门菌属、产肠毒素大肠杆菌、亲水气单胞菌、副溶血弧菌等所致。

4、伤寒。

5、骨和关节感染。

6、皮肤软组织感染。

7、败血症等全身感染。

,泌尿生殖系统感染|尿路感染|细菌性前列腺炎|淋病奈瑟菌尿道炎|宫颈炎|呼吸道感染|支气管感染急性发作|肺部感染|胃肠道感染|伤寒|骨关节感染|皮肤软组织感染|败血症|全身感染,氧氟沙星片的适应病症 74 | 丙戊酸钠片,

本品用于单纯或复杂失神发作、肌阵挛发作，大发作的单药或合并用药治疗，有时对复杂部分性发作也有一定疗效。

,失神发作|肌阵挛发作,丙戊酸钠片的适应病症 75 | 西沙必利片,

本品用于对其它治疗不耐受或疗效不佳的严重胃肠道动力性疾病，如慢性特发性或糖尿病性胃轻瘫、慢性假性肠梗阻、胃食管反流病。

,严重胃肠道动力性疾病|慢性特发性胃轻瘫|糖尿病性胃轻瘫|慢性假性肠梗阻|胃食管反流病,西沙必利片的适应病症 76 | 舒林酸片,

本品用于骨关节炎、类风湿性关节炎、关节强硬性脊椎炎、幼年类风湿性关节炎、关节周的疾病如急性疼痛（急性肩峰下粘液囊炎/冈上肌腱炎）和腱鞘炎、急性痛风性关节炎、疼痛的低背综合症（低背痛，通常通归于腰痛）、上呼吸道感染体征和症状。

,骨关节炎|类风湿性关节炎|关节强硬性脊椎炎|幼年类风湿性关节炎|关节周的疾病|急性疼痛|急性肩峰下粘液囊炎|冈上肌腱炎|腱鞘炎|急性痛风性关节炎|疼痛的低背综合症|呼吸道感染,舒林酸片的适应病症 77 | 联磺甲氧苄啶片,

本品主要用于对本品敏感的细菌所致的尿路感染、肠道感染、成人慢性支气管炎急性发作、急性中耳炎等。

,尿路感染|肠道感染|慢性支气管炎急性发作|急性中耳炎,联磺甲氧苄啶片的适应病症 78 | 碳酸锂缓释片,

本品用于治疗躁狂症，对躁狂和抑郁交替发作的双相情感性精神障碍有很好的治疗和预防复发作用，对反复发作的抑郁症也有预防发作作用。也用于治疗分裂-情感性精神病。

,躁狂症|躁郁症|抑郁症|分裂-情感性精神病,碳酸锂缓释片的适应病症 79 | 曲尼司特片,

本品用于预防和治疗支气管哮喘及过敏性鼻炎。

,支气管哮喘|过敏性鼻炎,曲尼司特片的适应病症 80 | 卡比多巴片,

本品与左旋多巴联合应用，用于帕金森病和帕金森综合征。

,帕金森病|帕金森综合征,卡比多巴片的适应病症 81 | 拉米夫定片,

本品用于伴有丙氨酸氨基转氨酶升高和病毒活动复制的、肝功能代偿的成年慢性乙型肝炎病人的治疗。

,慢性乙型肝炎病毒复制活跃期|慢性乙型肝炎肝功能代偿期,拉米夫定片的适应病症 82 | 复方氯唑沙宗片,

本品用于各种急性骨骼肌损伤。

,急性骨骼肌损伤,复方氯唑沙宗片的适应病症 83 | 白消安片,

本品用于慢性粒细胞白血病的慢性期，对缺乏费城染色体Ph1病人效果不佳。也可用于治疗原发性血小板增多症，真性红细胞增多症等慢性骨髓增殖性疾病。

,慢性粒细胞白血病慢性期|原发性血小板增多症|真性红细胞增多症|慢性骨髓增殖性疾病,白消安片的适应病症 84 | 盐酸土霉素片,

1、本品可作为下列疾病的选用药物：

（1）立克次体病，包括流行性斑疹伤寒、地方性斑疹伤寒、洛矶山热、恙虫病和Q热。

（2）支原体属感染。

（3）衣原体属感染，包括鹦鹉热、性病、淋巴肉芽肿、非特异性尿道炎、输卵管炎、宫颈炎及沙眼。

（4）回归热。

（5）布鲁菌病。

（6）霍乱。

（7）兔热病。

（8）鼠疫。

（9）软下疳。

治疗布鲁菌病和鼠疫时需与氨基糖苷类联合应用。

2、由于目前常见致病菌对本品耐药现象严重，仅在病原菌对本品敏感时，可作为选用药物；对本品敏感的大肠埃希菌、产气肠杆菌、洛菲不动杆菌、志贺菌属、流感嗜血杆菌（仅限于呼吸道感染）和克雷伯菌属（限于呼吸道和泌尿道感染）等革兰阴性杆菌感染。本品不宜用于任何类型的葡萄球菌或溶血性链球菌感染。

3、本品可用于对青霉素类过敏的破伤风、气性坏疽、雅司、梅毒、淋病和钩端螺旋体病以及放线菌属、李斯特菌感染的患者。

4、可用于急性肠道阿米巴病和中、重度痤疮患者作为辅助治疗。

,立克次体病|流行性斑疹伤寒|地方性斑疹伤寒|洛矶山热|恙虫病|Q热|支原体属感染|衣原体属感染|鹦鹉热|性病性淋巴肉芽肿|非特异性尿道炎|输卵管炎|宫颈炎|沙眼|回归热|布鲁菌病|霍乱|兔热病|鼠疫|软下疳|急性肠道阿米巴病|中度痤疮|重度痤疮,盐酸土霉素片的适应病症 85 | 特非那定片,

用于治疗季节性过敏性鼻炎，常年性过敏性鼻炎，急、慢性荨麻疹等。

,季节性过敏性鼻炎|常年性过敏性鼻|急性荨麻疹|慢性荨麻疹,特非那定片的适应病症 86 | 米索前列醇片,

本品用于治疗十二指肠溃疡和胃溃疡，包括关节炎患者由于服用非甾体类抗炎药NSAID所引起的十二指肠溃疡和胃溃疡，保障其仍可继续使用NSAID治疗。本品还可用于预防使用NSAID所引起的溃疡。

,十二指肠溃疡|胃溃疡,米索前列醇片的适应病症 87 | 硝酸甘油片,

本品用于冠心病心绞痛的治疗及预防，也可用于降低血压或治疗充血性心力衰竭。

,冠心病|心绞痛|充血性心力衰竭|血压升高,硝酸甘油片的适应病症 88 | 氯噻酮片,

1、水肿性疾病，排泄体内过多的钠和水，减少细胞外液容量，消除水肿。常见的包括充血性心力衰竭、肝硬化腹水、肾病综合征、急慢性肾炎水肿、慢性肾功能衰竭早期、肾上腺皮质激素和雌激素治疗所致的钠、水潴留。

2、高血压，可单独或与其他降压药联合应用，主要用于治疗原发性高血压。

3、中枢性或肾性尿崩症。

4、肾石症，主要用于预防含钙盐成分形成的结石。

,水肿|充血性心力衰竭|肝硬化腹水|肾病综合征|急慢性肾炎水肿|慢性肾功能衰竭早期|高血压|原发性高血压|中枢性尿崩症|肾性尿崩症|肾石症,氯噻酮片的适应病症 89 | 蛋氨酸片,

本品用于脂肪肝，以及酒精和磺胺等药物引起的肝损害。

,脂肪肝|药物性肝损害,蛋氨酸片的适应病症 90 | 盐酸林可霉素片,

本品适用于敏感葡萄球菌属、链球菌属、肺炎链球菌及厌氧菌所致的呼吸道感染、皮肤软组织感染、女性生殖道感染和盆腔感染及腹腔感染等，后两种病种可根据情况单用本品或与其他抗菌药联合应用。此外有应用青霉素指征的患者，如患者对青霉素过敏或不宜用青霉素者本品可用作替代药物。

,敏感葡萄球菌属感染|链球菌属感染|肺炎链球菌感染|厌氧菌感染,盐酸林可霉素片的适应病症 91 | 硫酸特布他林片,

本品用于支气管哮喘，慢性支气管炎、肺气肿和其它伴有支气管痉挛的肺部疾病。

,支气管哮喘|慢性支气管炎|肺气肿|支气管痉挛,硫酸特布他林片的适应病症 92 | 福尔可定片,

本品用于剧烈干咳和中等度疼痛。

,剧烈干咳|中等度疼痛,福尔可定片的适应病症 93 | 益多酯片,

本品用于治疗高胆固醇血症。

,高胆固醇血症,益多酯片的适应病症 94 | 碘化钾片,

本品用于地方性甲状腺肿的预防与治疗，甲状腺功能亢进症手术前准备及甲状腺亢进危象。

,地方性甲状腺肿|甲状腺功能亢进症手术前准备|甲状腺亢进危象,碘化钾片的适应病症 95 | 那可丁片,

本品用于干咳。

,干咳,那可丁片的适应病症 96 | 保泰松片,

本品​用于治疗风湿性关节炎、类风湿性关节炎、强直性脊柱炎。本药大剂量可减少肾小管对尿酸盐的再吸收，促进尿酸盐排泄，故可用于治疗急性痛风。

,风湿性关节炎|类风湿性关节炎|强直性脊柱炎|急性痛风,保泰松片的适应病症 97 | 贝诺酯片,

本品适用于治疗普通感冒或流行性感冒引起的发热，也用于缓解轻至中度疼痛如头痛、关节痛、偏头痛、牙痛、肌肉痛、神经痛、痛经。

,头痛|关节痛|偏头痛|牙痛|肌肉痛|神经痛|痛经,贝诺酯片的适应病症 98 | 瑞格列奈片,

本品适用于饮食控制、减轻体重及运动锻炼不能有效控制其高血糖的2型糖尿病(非胰岛素依赖型)患者。当单独使用二甲双胍不能有效控制其高血糖时，瑞格列奈可与二甲双胍合用。治疗应从饮食控制和运动锻炼降低餐时血糖的辅助治疗开始。

,非胰岛素依赖型2型糖尿病,瑞格列奈片的适应病症 99 | 三磷酸腺苷二钠片,

本品适用于进行性肌萎缩、脑出血后遗症、心功能不全、心肌疾患及肝炎等的辅助治疗。

,进行性肌萎缩|脑出血后遗症|心功能不全|心肌疾患|肝炎,三磷酸腺苷二钠片的适应病症 100 | 沙利度胺片,

本品适用于控制瘤型麻风反应症。

,瘤型麻风反应症,沙利度胺片的适应病症 101 | 石杉碱甲片,

本品适用于良性记忆障碍，可提高患者指向记忆、联想学习、图像回忆、无意义图形再认及人像回忆等能力，对正常人的学习与记忆也有增强作用，对痴呆患者和脑器质性病变引起的记忆障碍亦有改善作用。

,良性记忆障碍|痴呆,石杉碱甲片的适应病症 102 | -------------------------------------------------------------------------------- /gradio_cama-demo.py: -------------------------------------------------------------------------------- 1 | from peft import PeftModel 2 | from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig 3 | import torch 4 | import sys 5 | import gradio as gr 6 | import argparse 7 | import os 8 | import mdtex2html 9 | from scripts.callbacks import Iteratorize, Stream 10 | # from examples.prompter import Prompter 11 | import transformers 12 | import os 13 | os.environ["CUDA_VISIBLE_DEVICES"] = '7' 14 | 15 | 16 | 17 | def postprocess(self, y): 18 | if y is None: 19 | return [] 20 | for i, (message, response) in enumerate(y): 21 | y[i] = ( 22 | None if message is None else mdtex2html.convert((message)), 23 | None if response is None else mdtex2html.convert(response), 24 | ) 25 | return y 26 | 27 | 28 | def reset_user_input(): 29 | return gr.update(value='') 30 | 31 | 32 | def reset_state(): 33 | return [], [] 34 | 35 | def generate_prompt(instruction): 36 | return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. 37 | 38 | ### Instruction: 39 | {instruction} 40 | 41 | ### Response: """ 42 | 43 | 44 | gr.Chatbot.postprocess = postprocess 45 | 46 | if torch.cuda.is_available(): 47 | device = "cuda" 48 | else: 49 | device = "cpu" 50 | 51 | try: 52 | if torch.backends.mps.is_available(): 53 | device = "mps" 54 | except: # noqa: E722 55 | pass 56 | 57 | base_model: str = "cama-path" 58 | lora_weights: str = "lora-path" 59 | load_8bit = False 60 | 61 | # prompter = Prompter(prompt_template) 62 | tokenizer = LlamaTokenizer.from_pretrained(base_model) 63 | if device == "cuda": 64 | model = LlamaForCausalLM.from_pretrained( 65 | base_model, 66 | load_in_8bit=load_8bit, 67 | torch_dtype=torch.float16, 68 | device_map="auto", 69 | ) 70 | model = PeftModel.from_pretrained( 71 | model, 72 | lora_weights, 73 | torch_dtype=torch.float16, 74 | ) 75 | elif device == "mps": 76 | model = LlamaForCausalLM.from_pretrained( 77 | base_model, 78 | device_map={"": device}, 79 | torch_dtype=torch.float16, 80 | ) 81 | model = PeftModel.from_pretrained( 82 | model, 83 | lora_weights, 84 | device_map={"": device}, 85 | torch_dtype=torch.float16, 86 | ) 87 | else: 88 | model = LlamaForCausalLM.from_pretrained( 89 | base_model, device_map={"": device}, low_cpu_mem_usage=True 90 | ) 91 | model = PeftModel.from_pretrained( 92 | model, 93 | lora_weights, 94 | device_map={"": device}, 95 | ) 96 | 97 | # unwind broken decapoda-research config 98 | model.config.pad_token_id = tokenizer.pad_token_id = 0 # pad 99 | model.config.bos_token_id = tokenizer.pad_token_id = 1 100 | model.config.eos_token_id = tokenizer.pad_token_id = 2 101 | 102 | if not load_8bit: 103 | model.half() # seems to fix bugs for some users. 104 | 105 | model.eval() 106 | if torch.__version__ >= "2" and sys.platform != "win32": 107 | model = torch.compile(model) 108 | 109 | 110 | max_memory=512 111 | 112 | def evaluate( 113 | chatbot, 114 | instruction, 115 | # input=None, 116 | temperature=0.4, 117 | top_p=0.75, 118 | top_k=40, 119 | num_beams=2, 120 | max_new_tokens=512, 121 | repetition_penalty=1.3, 122 | stream_output=False, 123 | history=None, 124 | **kwargs, 125 | ): 126 | 127 | now_input = instruction 128 | chatbot.append((instruction, "")) 129 | history = history or [] 130 | if len(history) != 0: 131 | instruction = "".join(["### Instruction:\n" + i[0] +"\n\n" + "### Response: " + i[1] + "\n\n" for i in history]) + \ 132 | "### Instruction:\n" + instruction 133 | instruction = instruction[len("### Instruction:\n"):] 134 | if len(instruction) > max_memory: 135 | instruction = instruction[-max_memory:] 136 | 137 | prompt = generate_prompt(instruction) 138 | inputs = tokenizer(prompt, return_tensors="pt") 139 | input_ids = inputs["input_ids"].to(device) 140 | generation_config = GenerationConfig( 141 | temperature=temperature, 142 | top_p=top_p, 143 | top_k=top_k, 144 | num_beams=num_beams, 145 | repetition_penalty=repetition_penalty, 146 | **kwargs, 147 | ) 148 | 149 | 150 | # Without streaming 151 | with torch.no_grad(): 152 | generation_output = model.generate( 153 | input_ids=input_ids, 154 | generation_config=generation_config, 155 | return_dict_in_generate=True, 156 | output_scores=True, 157 | max_new_tokens=max_new_tokens, 158 | ) 159 | s = generation_output.sequences[0] 160 | output = tokenizer.decode(s) 161 | # output = prompter.get_response(output) 162 | output = output.split("### Response:")[-1].strip() 163 | history.append((now_input, output)) 164 | chatbot[-1] = (now_input, output) 165 | # return chatbot, history 166 | # yield prompter.get_response(output) 167 | yield chatbot, history 168 | 169 | 170 | with gr.Blocks() as demo: 171 | gr.HTML("""

CaMA0601 - training - 3600steps

""") 172 | current_file_path = os.path.abspath(os.path.dirname(__file__)) 173 | # gr.Image(f'{current_file_path}/../pics/banner.png', label = 'Chinese LLaMA & Alpaca LLM') 174 | gr.Markdown("> 启真医学大模型") 175 | chatbot = gr.Chatbot() 176 | with gr.Row(): 177 | with gr.Column(scale=4): 178 | with gr.Column(scale=12): 179 | user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style( 180 | container=False) 181 | with gr.Column(min_width=32, scale=1): 182 | submitBtn = gr.Button("Submit", variant="primary") 183 | with gr.Column(scale=1): 184 | emptyBtn = gr.Button("Clear History") 185 | temperature = gr.components.Slider( 186 | minimum=0, maximum=1, value=0.4, label="Temperature" 187 | ) 188 | top_p = gr.components.Slider( 189 | minimum=0, maximum=1, value=0.75, label="Top p" 190 | ) 191 | top_k = gr.components.Slider( 192 | minimum=0, maximum=100, step=1, value=40, label="Top k" 193 | ) 194 | num_beams = gr.components.Slider( 195 | minimum=1, maximum=4, step=1, value=2, label="Beams" 196 | ) 197 | max_new_tokens = gr.components.Slider( 198 | minimum=1, maximum=2000, step=1, value=512, label="Max tokens" 199 | ) 200 | repetition_penalty = gr.components.Slider( 201 | minimum=1, maximum=2, step=0.1, value=1.3, label="Repetition Penalty" 202 | ) 203 | stream_output = gr.components.Checkbox(label="Stream output") 204 | 205 | history = gr.State([]) # (message, bot_message) 206 | 207 | submitBtn.click(evaluate, [chatbot, user_input, temperature, top_p, top_k, num_beams, max_new_tokens, repetition_penalty, stream_output, history],[chatbot, history], 208 | show_progress=True) 209 | # submitBtn.click(predict, [user_input, chatbot, history, max_length, top_p, temperature], [chatbot, history], 210 | # show_progress=True) 211 | submitBtn.click(reset_user_input, [], [user_input]) 212 | 213 | emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True) 214 | 215 | demo.queue().launch(share=False, inbrowser=True, 216 | server_name='0.0.0.0', server_port=16666) 217 | -------------------------------------------------------------------------------- /gradio_chatglm_demo.py: -------------------------------------------------------------------------------- 1 | from transformers import AutoModel, AutoTokenizer 2 | import gradio as gr 3 | import mdtex2html 4 | from peft import PeftModel 5 | 6 | 7 | base_model_path = 'chatglm6b-path/' 8 | lora_model_path = 'lora-path/' 9 | 10 | tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True) 11 | base_model = AutoModel.from_pretrained(base_model_path, trust_remote_code=True).half().cuda() 12 | if lora_model_path is not None: 13 | print("loading peft model") 14 | model = PeftModel.from_pretrained(base_model, lora_model_path, torch_dtype='fp16', device_map='auto') 15 | else: 16 | model = base_model 17 | model = model.eval() 18 | 19 | """Override Chatbot.postprocess""" 20 | 21 | 22 | def postprocess(self, y): 23 | if y is None: 24 | return [] 25 | for i, (message, response) in enumerate(y): 26 | y[i] = ( 27 | None if message is None else mdtex2html.convert((message)), 28 | None if response is None else mdtex2html.convert(response), 29 | ) 30 | return y 31 | 32 | 33 | gr.Chatbot.postprocess = postprocess 34 | 35 | 36 | def parse_text(text): 37 | """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" 38 | lines = text.split("\n") 39 | lines = [line for line in lines if line != ""] 40 | count = 0 41 | for i, line in enumerate(lines): 42 | if "```" in line: 43 | count += 1 44 | items = line.split('`') 45 | if count % 2 == 1: 46 | lines[i] = f'
'
 47 |             else:
 48 |                 lines[i] = f'
' 49 | else: 50 | if i > 0: 51 | if count % 2 == 1: 52 | line = line.replace("`", "\`") 53 | line = line.replace("<", "<") 54 | line = line.replace(">", ">") 55 | line = line.replace(" ", " ") 56 | line = line.replace("*", "*") 57 | line = line.replace("_", "_") 58 | line = line.replace("-", "-") 59 | line = line.replace(".", ".") 60 | line = line.replace("!", "!") 61 | line = line.replace("(", "(") 62 | line = line.replace(")", ")") 63 | line = line.replace("$", "$") 64 | lines[i] = "
"+line 65 | text = "".join(lines) 66 | return text 67 | 68 | 69 | def predict(input, chatbot, max_length, top_p, temperature, history): 70 | chatbot.append((parse_text(input), "")) 71 | for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p, 72 | temperature=temperature,):#no_repeat_ngram_size=7 73 | chatbot[-1] = (parse_text(input), parse_text(response)) 74 | 75 | yield chatbot, history 76 | 77 | 78 | def reset_user_input(): 79 | return gr.update(value='') 80 | 81 | 82 | def reset_state(): 83 | return [], [] 84 | 85 | 86 | with gr.Blocks() as demo: 87 | gr.HTML("""

QiZhen-ChatGLM-6b

""") 88 | 89 | chatbot = gr.Chatbot() 90 | with gr.Row(): 91 | with gr.Column(scale=4): 92 | with gr.Column(scale=40): 93 | user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style( 94 | container=False) 95 | with gr.Column(min_width=32, scale=1): 96 | submitBtn = gr.Button("Submit", variant="primary") 97 | with gr.Column(scale=1): 98 | emptyBtn = gr.Button("Clear History") 99 | max_length = gr.Slider(0, 4096, value=512, step=1.0, label="Maximum length", interactive=True) 100 | top_p = gr.Slider(0, 1, value=0.85, step=0.01, label="Top P", interactive=True) 101 | temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True) 102 | 103 | history = gr.State([]) 104 | 105 | submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history], 106 | show_progress=True) 107 | submitBtn.click(reset_user_input, [], [user_input]) 108 | 109 | emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True) 110 | 111 | demo.queue().launch(share=False, inbrowser=True, server_name = '0.0.0.0', server_port=16669) 112 | 113 | # nohup python web_demo.py > serve.out 2>&1 & 114 | -------------------------------------------------------------------------------- /gradio_chinese-llama_demo.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import gradio as gr 3 | import argparse 4 | import os 5 | import mdtex2html 6 | 7 | parser = argparse.ArgumentParser() 8 | parser.add_argument('--base_model', default="qizhen_model/", type=str) 9 | parser.add_argument('--tokenizer_path',default="qizhen_model/",type=str) 10 | parser.add_argument('--gpus', default="0", type=str) 11 | parser.add_argument('--only_cpu',action='store_true',help='only use CPU for inference') 12 | args = parser.parse_args() 13 | if args.only_cpu is True: 14 | args.gpus = "" 15 | os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus 16 | 17 | import torch 18 | from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig 19 | from peft import PeftModel 20 | 21 | def postprocess(self, y): 22 | if y is None: 23 | return [] 24 | for i, (message, response) in enumerate(y): 25 | y[i] = ( 26 | None if message is None else mdtex2html.convert((message)), 27 | None if response is None else mdtex2html.convert(response), 28 | ) 29 | return y 30 | 31 | 32 | gr.Chatbot.postprocess = postprocess 33 | 34 | 35 | load_type = torch.float16 36 | if torch.cuda.is_available(): 37 | device = torch.device(0) 38 | else: 39 | device = torch.device('cpu') 40 | if args.tokenizer_path is None: 41 | args.tokenizer_path = args.lora_model 42 | if args.lora_model is None: 43 | args.tokenizer_path = args.base_model 44 | tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path) 45 | 46 | base_model = LlamaForCausalLM.from_pretrained( 47 | args.base_model, 48 | load_in_8bit=False, 49 | torch_dtype=load_type, 50 | low_cpu_mem_usage=True, 51 | device_map='auto', 52 | ) 53 | 54 | model_vocab_size = base_model.get_input_embeddings().weight.size(0) 55 | tokenzier_vocab_size = len(tokenizer) 56 | print(f"Vocab of the base model: {model_vocab_size}") 57 | print(f"Vocab of the tokenizer: {tokenzier_vocab_size}") 58 | if model_vocab_size!=tokenzier_vocab_size: 59 | assert tokenzier_vocab_size > model_vocab_size 60 | print("Resize model embeddings to fit tokenizer") 61 | base_model.resize_token_embeddings(tokenzier_vocab_size) 62 | 63 | model = base_model 64 | 65 | if device==torch.device('cpu'): 66 | model.float() 67 | 68 | model.eval() 69 | 70 | def reset_user_input(): 71 | return gr.update(value='') 72 | 73 | def reset_state(): 74 | return [], [] 75 | 76 | def generate_prompt(instruction): 77 | return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. 78 | 79 | ### Instruction: 80 | {instruction} 81 | 82 | ### Response: """ 83 | 84 | if torch.__version__ >= "2" and sys.platform != "win32": 85 | model = torch.compile(model) 86 | 87 | def predict( 88 | input, 89 | chatbot, 90 | history, 91 | max_new_tokens=512, 92 | top_p=0.9, 93 | temperature=0.1, 94 | top_k=40, 95 | num_beams=4, 96 | repetition_penalty=1.0, 97 | max_memory=256, 98 | **kwargs, 99 | ): 100 | now_input = input 101 | chatbot.append((input, "")) 102 | history = history or [] 103 | if len(history) != 0: 104 | input = "".join(["### Instruction:\n" + i[0] +"\n\n" + "### Response: " + i[1] + "\n\n" for i in history]) + \ 105 | "### Instruction:\n" + input 106 | input = input[len("### Instruction:\n"):] 107 | if len(input) > max_memory: 108 | input = input[-max_memory:] 109 | prompt = generate_prompt(input) 110 | inputs = tokenizer(prompt, return_tensors="pt") 111 | input_ids = inputs["input_ids"].to(device) 112 | generation_config = GenerationConfig( 113 | temperature=temperature, 114 | top_p=top_p, 115 | top_k=top_k, 116 | num_beams=num_beams, 117 | **kwargs, 118 | ) 119 | with torch.no_grad(): 120 | generation_output = model.generate( 121 | input_ids=input_ids, 122 | generation_config=generation_config, 123 | return_dict_in_generate=True, 124 | output_scores=False, 125 | max_new_tokens=max_new_tokens, 126 | repetition_penalty=float(repetition_penalty), 127 | ) 128 | s = generation_output.sequences[0] 129 | output = tokenizer.decode(s, skip_special_tokens=True) 130 | output = output.split("### Response:")[-1].strip() 131 | history.append((now_input, output)) 132 | chatbot[-1] = (now_input, output) 133 | return chatbot, history 134 | 135 | with gr.Blocks() as demo: 136 | gr.HTML("""

启真医学大模型

""") 137 | current_file_path = os.path.abspath(os.path.dirname(__file__)) 138 | chatbot = gr.Chatbot() 139 | with gr.Row(): 140 | with gr.Column(scale=4): 141 | with gr.Column(scale=12): 142 | user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style( 143 | container=False) 144 | with gr.Column(min_width=32, scale=1): 145 | submitBtn = gr.Button("Submit", variant="primary") 146 | with gr.Column(scale=1): 147 | emptyBtn = gr.Button("Clear History") 148 | max_length = gr.Slider( 149 | 0, 4096, value=512, step=1.0, label="Maximum length", interactive=True) 150 | top_p = gr.Slider(0, 1, value=0.9, step=0.01, 151 | label="Top P", interactive=True) 152 | temperature = gr.Slider( 153 | 0, 1, value=0.1, step=0.01, label="Temperature", interactive=True) 154 | 155 | history = gr.State([]) # (message, bot_message) 156 | 157 | submitBtn.click(predict, [user_input, chatbot, history, max_length, top_p, temperature], [chatbot, history], 158 | show_progress=True) 159 | submitBtn.click(reset_user_input, [], [user_input]) 160 | 161 | emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True) 162 | demo.queue().launch(share=False, inbrowser=True, server_name = '0.0.0.0', server_port=16667) -------------------------------------------------------------------------------- /lora/lora_weights: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CMKRG/QiZhenGPT/cf5be4d33d7fda3813e0e19623f81056d07fce2f/lora/lora_weights -------------------------------------------------------------------------------- /pics/llm_kg1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CMKRG/QiZhenGPT/cf5be4d33d7fda3813e0e19623f81056d07fce2f/pics/llm_kg1.jpg -------------------------------------------------------------------------------- /pics/llm_kg2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CMKRG/QiZhenGPT/cf5be4d33d7fda3813e0e19623f81056d07fce2f/pics/llm_kg2.jpg -------------------------------------------------------------------------------- /pics/medcopilot1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CMKRG/QiZhenGPT/cf5be4d33d7fda3813e0e19623f81056d07fce2f/pics/medcopilot1.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate==0.19.0 2 | aiofiles==23.1.0 3 | aiohttp==3.8.4 4 | aiosignal==1.3.1 5 | altair==5.0.0 6 | anyio==3.6.2 7 | async-timeout==4.0.2 8 | attrs==23.1.0 9 | brotlipy==0.7.0 10 | certifi==2023.5.7 11 | cffi==1.15.1 12 | charset-normalizer==2.0.4 13 | click==8.1.3 14 | contourpy==1.0.7 15 | cryptography==39.0.1 16 | cycler==0.11.0 17 | datasets==2.12.0 18 | deepspeed==0.9.2 19 | dill==0.3.6 20 | fastapi==0.95.2 21 | ffmpy==0.3.0 22 | filelock==3.12.0 23 | fonttools==4.39.4 24 | frozenlist==1.3.3 25 | fsspec==2023.5.0 26 | gmpy2==2.1.2 27 | gradio==3.31.0 28 | gradio_client==0.2.5 29 | h11==0.14.0 30 | hjson==3.1.0 31 | httpcore==0.17.1 32 | httpx==0.24.1 33 | huggingface-hub==0.14.1 34 | idna==3.4 35 | Jinja2==3.1.2 36 | jsonschema==4.17.3 37 | kiwisolver==1.4.4 38 | latex2mathml==3.75.5 39 | linkify-it-py==2.0.2 40 | Markdown==3.4.3 41 | markdown-it-py==2.2.0 42 | MarkupSafe==2.1.1 43 | matplotlib==3.7.1 44 | mdit-py-plugins==0.3.3 45 | mdtex2html==1.2.0 46 | mdurl==0.1.2 47 | mkl-fft==1.3.6 48 | mkl-random==1.2.2 49 | mkl-service==2.4.0 50 | mpmath==1.2.1 51 | multidict==6.0.4 52 | multiprocess==0.70.14 53 | networkx==2.8.4 54 | ninja==1.11.1 55 | numpy==1.24.3 56 | orjson==3.8.12 57 | packaging==23.1 58 | pandas==2.0.1 59 | peft==0.3.0.dev0 60 | Pillow==9.4.0 61 | pip==23.0.1 62 | psutil==5.9.5 63 | py-cpuinfo==9.0.0 64 | pyarrow==12.0.0 65 | pycparser==2.21 66 | pydantic==1.10.7 67 | pydub==0.25.1 68 | Pygments==2.15.1 69 | pyOpenSSL==23.0.0 70 | pyparsing==3.0.9 71 | pyrsistent==0.19.3 72 | PySocks==1.7.1 73 | python-dateutil==2.8.2 74 | python-multipart==0.0.6 75 | pytz==2023.3 76 | PyYAML==6.0 77 | regex==2023.5.5 78 | requests==2.29.0 79 | responses==0.18.0 80 | semantic-version==2.10.0 81 | sentencepiece==0.1.97 82 | setuptools==66.0.0 83 | six==1.16.0 84 | sniffio==1.3.0 85 | starlette==0.27.0 86 | sympy==1.11.1 87 | tokenizers==0.13.3 88 | toolz==0.12.0 89 | torch==1.13.1 90 | torchaudio==0.13.1 91 | torchvision==0.14.1 92 | tqdm==4.65.0 93 | transformers==4.28.1 94 | triton==2.0.0 95 | typing_extensions==4.5.0 96 | tzdata==2023.3 97 | uc-micro-py==1.0.2 98 | urllib3==1.26.15 99 | uvicorn==0.22.0 100 | websockets==11.0.3 101 | wheel==0.38.4 102 | xxhash==3.2.0 103 | yarl==1.9.2 104 | -------------------------------------------------------------------------------- /scripts/callbacks.py: -------------------------------------------------------------------------------- 1 | """ 2 | Helpers to support streaming generate output. 3 | Borrowed from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/callbacks.py 4 | """ 5 | 6 | import gc 7 | import traceback 8 | from queue import Queue 9 | from threading import Thread 10 | 11 | import torch 12 | import transformers 13 | 14 | 15 | class Stream(transformers.StoppingCriteria): 16 | def __init__(self, callback_func=None): 17 | self.callback_func = callback_func 18 | 19 | def __call__(self, input_ids, scores) -> bool: 20 | if self.callback_func is not None: 21 | self.callback_func(input_ids[0]) 22 | return False 23 | 24 | 25 | class Iteratorize: 26 | 27 | """ 28 | Transforms a function that takes a callback 29 | into a lazy iterator (generator). 30 | """ 31 | 32 | def __init__(self, func, kwargs={}, callback=None): 33 | self.mfunc = func 34 | self.c_callback = callback 35 | self.q = Queue() 36 | self.sentinel = object() 37 | self.kwargs = kwargs 38 | self.stop_now = False 39 | 40 | def _callback(val): 41 | if self.stop_now: 42 | raise ValueError 43 | self.q.put(val) 44 | 45 | def gentask(): 46 | try: 47 | ret = self.mfunc(callback=_callback, **self.kwargs) 48 | except ValueError: 49 | pass 50 | except: 51 | traceback.print_exc() 52 | pass 53 | 54 | self.q.put(self.sentinel) 55 | if self.c_callback: 56 | self.c_callback(ret) 57 | 58 | self.thread = Thread(target=gentask) 59 | self.thread.start() 60 | 61 | def __iter__(self): 62 | return self 63 | 64 | def __next__(self): 65 | obj = self.q.get(True, None) 66 | if obj is self.sentinel: 67 | raise StopIteration 68 | else: 69 | return obj 70 | 71 | def __enter__(self): 72 | return self 73 | 74 | def __exit__(self, exc_type, exc_val, exc_tb): 75 | self.stop_now = True 76 | -------------------------------------------------------------------------------- /scripts/merge_llama_plus.sh: -------------------------------------------------------------------------------- 1 | python scripts/merge_llama_with_chinese_lora.py \ 2 | --base_model Chinese-LLaMA-Plus-path \ 3 | --lora_model lora/checkpoint-3500 \ 4 | --output_type huggingface \ 5 | --output_dir qizhen_model/ -------------------------------------------------------------------------------- /scripts/merge_llama_with_chinese_lora.py: -------------------------------------------------------------------------------- 1 | """ 2 | Usage: 3 | python merge_llama_with_chinese_lora.py \ 4 | --base_model path/to/llama/model \ 5 | --lora_model path/to/first/lora/model [path/to/second/lora/model] \ 6 | --output_type [pth|huggingface] \ 7 | --output_dir path/to/output/dir 8 | """ 9 | import argparse 10 | import json 11 | import os 12 | import gc 13 | import torch 14 | import peft 15 | from peft import PeftModel 16 | from transformers import LlamaForCausalLM, LlamaTokenizer 17 | from huggingface_hub import hf_hub_download 18 | 19 | parser = argparse.ArgumentParser() 20 | parser.add_argument('--base_model', default=None, required=True, 21 | type=str, help="Please specify a base_model") 22 | parser.add_argument('--lora_model', default=None, required=True, 23 | type=str, help="Please specify LoRA models to be merged (ordered); use commas to separate multiple LoRA models.") 24 | parser.add_argument('--offload_dir', default=None, type=str, 25 | help="(Optional) Please specify a temp folder for offloading (useful for low-RAM machines). Default None (disable offload).") 26 | parser.add_argument('--output_type', default='pth',choices=['pth','huggingface'], type=str, 27 | help="save the merged model in pth or huggingface format.") 28 | parser.add_argument('--output_dir', default='./', type=str) 29 | 30 | 31 | emb_to_model_size = { 32 | 4096 : '7B', 33 | 5120 : '13B', 34 | 6656 : '30B', 35 | 8192 : '65B', 36 | } 37 | num_shards_of_models = {'7B': 1, '13B': 2} 38 | params_of_models = { 39 | '7B': 40 | { 41 | "dim": 4096, 42 | "multiple_of": 256, 43 | "n_heads": 32, 44 | "n_layers": 32, 45 | "norm_eps": 1e-06, 46 | "vocab_size": -1, 47 | }, 48 | '13B': 49 | { 50 | "dim": 5120, 51 | "multiple_of": 256, 52 | "n_heads": 40, 53 | "n_layers": 40, 54 | "norm_eps": 1e-06, 55 | "vocab_size": -1, 56 | }, 57 | } 58 | 59 | def transpose(weight, fan_in_fan_out): 60 | return weight.T if fan_in_fan_out else weight 61 | 62 | # Borrowed and modified from https://github.com/tloen/alpaca-lora 63 | def translate_state_dict_key(k): 64 | k = k.replace("base_model.model.", "") 65 | if k == "model.embed_tokens.weight": 66 | return "tok_embeddings.weight" 67 | elif k == "model.norm.weight": 68 | return "norm.weight" 69 | elif k == "lm_head.weight": 70 | return "output.weight" 71 | elif k.startswith("model.layers."): 72 | layer = k.split(".")[2] 73 | if k.endswith(".self_attn.q_proj.weight"): 74 | return f"layers.{layer}.attention.wq.weight" 75 | elif k.endswith(".self_attn.k_proj.weight"): 76 | return f"layers.{layer}.attention.wk.weight" 77 | elif k.endswith(".self_attn.v_proj.weight"): 78 | return f"layers.{layer}.attention.wv.weight" 79 | elif k.endswith(".self_attn.o_proj.weight"): 80 | return f"layers.{layer}.attention.wo.weight" 81 | elif k.endswith(".mlp.gate_proj.weight"): 82 | return f"layers.{layer}.feed_forward.w1.weight" 83 | elif k.endswith(".mlp.down_proj.weight"): 84 | return f"layers.{layer}.feed_forward.w2.weight" 85 | elif k.endswith(".mlp.up_proj.weight"): 86 | return f"layers.{layer}.feed_forward.w3.weight" 87 | elif k.endswith(".input_layernorm.weight"): 88 | return f"layers.{layer}.attention_norm.weight" 89 | elif k.endswith(".post_attention_layernorm.weight"): 90 | return f"layers.{layer}.ffn_norm.weight" 91 | elif k.endswith("rotary_emb.inv_freq") or "lora" in k: 92 | return None 93 | else: 94 | print(layer, k) 95 | raise NotImplementedError 96 | else: 97 | print(k) 98 | raise NotImplementedError 99 | 100 | 101 | def unpermute(w): 102 | return ( 103 | w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim) 104 | ) 105 | 106 | 107 | def save_shards(model_sd, num_shards: int): 108 | # Add the no_grad context manager 109 | with torch.no_grad(): 110 | if num_shards == 1: 111 | new_state_dict = {} 112 | for k, v in model_sd.items(): 113 | new_k = translate_state_dict_key(k) 114 | if new_k is not None: 115 | if "wq" in new_k or "wk" in new_k: 116 | new_state_dict[new_k] = unpermute(v) 117 | else: 118 | new_state_dict[new_k] = v 119 | 120 | os.makedirs(output_dir, exist_ok=True) 121 | print(f"Saving shard 1 of {num_shards} into {output_dir}/consolidated.00.pth") 122 | torch.save(new_state_dict, output_dir + "/consolidated.00.pth") 123 | with open(output_dir + "/params.json", "w") as f: 124 | json.dump(params, f) 125 | else: 126 | new_state_dicts = [dict() for _ in range(num_shards)] 127 | for k in list(model_sd.keys()): 128 | v = model_sd[k] 129 | new_k = translate_state_dict_key(k) 130 | if new_k is not None: 131 | if new_k=='tok_embeddings.weight': 132 | print(f"Processing {new_k}") 133 | assert v.size(1)%num_shards==0 134 | splits = v.split(v.size(1)//num_shards,dim=1) 135 | elif new_k=='output.weight': 136 | print(f"Processing {new_k}") 137 | if v.size(0)%num_shards==0: 138 | splits = v.split(v.size(0)//num_shards,dim=0) 139 | else: 140 | size_list = [v.size(0)//num_shards] * num_shards 141 | size_list[-1] += v.size(0)%num_shards 142 | splits = v.split(size_list, dim=0) # 13B: size_list == [24976,24977] 143 | elif new_k=='norm.weight': 144 | print(f"Processing {new_k}") 145 | splits = [v] * num_shards 146 | elif 'ffn_norm.weight' in new_k: 147 | print(f"Processing {new_k}") 148 | splits = [v] * num_shards 149 | elif 'attention_norm.weight' in new_k: 150 | print(f"Processing {new_k}") 151 | splits = [v] * num_shards 152 | 153 | 154 | elif 'w1.weight' in new_k: 155 | print(f"Processing {new_k}") 156 | splits = v.split(v.size(0)//num_shards,dim=0) 157 | elif 'w2.weight' in new_k: 158 | print(f"Processing {new_k}") 159 | splits = v.split(v.size(1)//num_shards,dim=1) 160 | elif 'w3.weight' in new_k: 161 | print(f"Processing {new_k}") 162 | splits = v.split(v.size(0)//num_shards,dim=0) 163 | 164 | 165 | elif 'wo.weight' in new_k: 166 | print(f"Processing {new_k}") 167 | splits = v.split(v.size(1)//num_shards,dim=1) 168 | 169 | elif 'wv.weight' in new_k: 170 | print(f"Processing {new_k}") 171 | splits = v.split(v.size(0)//num_shards,dim=0) 172 | 173 | elif "wq.weight" in new_k or "wk.weight" in new_k: 174 | print(f"Processing {new_k}") 175 | v = unpermute(v) 176 | splits = v.split(v.size(0)//num_shards,dim=0) 177 | else: 178 | print(f"Unexpected key {new_k}") 179 | raise ValueError 180 | for sd,split in zip(new_state_dicts,splits): 181 | sd[new_k] = split.clone() 182 | del split 183 | del splits 184 | del model_sd[k],v 185 | gc.collect() # Effectively enforce garbage collection 186 | 187 | os.makedirs(output_dir, exist_ok=True) 188 | for i,new_state_dict in enumerate(new_state_dicts): 189 | print(f"Saving shard {i+1} of {num_shards} into {output_dir}/consolidated.0{i}.pth") 190 | torch.save(new_state_dict, output_dir + f"/consolidated.0{i}.pth") 191 | with open(output_dir + "/params.json", "w") as f: 192 | print(f"Saving params.json into {output_dir}/params.json") 193 | json.dump(params, f) 194 | 195 | 196 | if __name__=='__main__': 197 | 198 | args = parser.parse_args() 199 | base_model_path = args.base_model 200 | lora_model_paths = [s.strip() for s in args.lora_model.split(',') if len(s.strip())!=0] 201 | output_dir = args.output_dir 202 | output_type = args.output_type 203 | offload_dir = args.offload_dir 204 | 205 | print(f"Base model: {base_model_path}") 206 | print(f"LoRA model(s) {lora_model_paths}:") 207 | 208 | if offload_dir is not None: 209 | # Load with offloading, which is useful for low-RAM machines. 210 | # Note that if you have enough RAM, please use original method instead, as it is faster. 211 | base_model = LlamaForCausalLM.from_pretrained( 212 | base_model_path, 213 | load_in_8bit=False, 214 | torch_dtype=torch.float16, 215 | offload_folder=offload_dir, 216 | offload_state_dict=True, 217 | low_cpu_mem_usage=True, 218 | device_map={"": "cpu"}, 219 | ) 220 | else: 221 | # Original method without offloading 222 | base_model = LlamaForCausalLM.from_pretrained( 223 | base_model_path, 224 | load_in_8bit=False, 225 | torch_dtype=torch.float16, 226 | device_map={"": "cpu"}, 227 | ) 228 | 229 | ## infer the model size from the checkpoint 230 | embedding_size = base_model.get_input_embeddings().weight.size(1) 231 | model_size = emb_to_model_size[embedding_size] 232 | print(f"Peft version: {peft.__version__}") 233 | print(f"Loading LoRA for {model_size} model") 234 | 235 | lora_model = None 236 | lora_model_sd = None 237 | for lora_index, lora_model_path in enumerate(lora_model_paths): 238 | print(f"Loading LoRA {lora_model_path}") 239 | tokenizer = LlamaTokenizer.from_pretrained(lora_model_path) 240 | if base_model.get_input_embeddings().weight.size(0) != len(tokenizer): 241 | base_model.resize_token_embeddings(len(tokenizer)) 242 | print(f"Extended vocabulary size to {len(tokenizer)}") 243 | 244 | first_weight = base_model.model.layers[0].self_attn.q_proj.weight 245 | first_weight_old = first_weight.clone() 246 | 247 | if hasattr(peft.LoraModel,'merge_and_unload'): 248 | lora_model = PeftModel.from_pretrained( 249 | base_model, 250 | lora_model_path, 251 | device_map={"": "cpu"}, 252 | torch_dtype=torch.float16, 253 | ) 254 | assert torch.allclose(first_weight_old, first_weight) 255 | print(f"Merging with merge_and_unload...") 256 | base_model = lora_model.merge_and_unload() 257 | else: 258 | base_model_sd = base_model.state_dict() 259 | try: 260 | lora_model_sd = torch.load(os.path.join(lora_model_path,'adapter_model.bin'),map_location='cpu') 261 | except FileNotFoundError: 262 | print("Cannot find lora model on the disk. Downloading lora model from hub...") 263 | filename = hf_hub_download(repo_id=lora_model_path,filename='adapter_model.bin') 264 | lora_model_sd = torch.load(filename,map_location='cpu') 265 | 266 | lora_config = peft.LoraConfig.from_pretrained(lora_model_path) 267 | lora_scaling = lora_config.lora_alpha / lora_config.r 268 | fan_in_fan_out = lora_config.fan_in_fan_out 269 | lora_keys = [k for k in lora_model_sd if 'lora_A' in k] 270 | non_lora_keys = [k for k in lora_model_sd if not 'lora_' in k] 271 | 272 | for k in non_lora_keys: 273 | print(f"merging {k}") 274 | original_k = k.replace('base_model.model.','') 275 | base_model_sd[original_k].copy_(lora_model_sd[k]) 276 | 277 | for k in lora_keys: 278 | print(f"merging {k}") 279 | original_key = k.replace('.lora_A','').replace('base_model.model.','') 280 | assert original_key in base_model_sd 281 | lora_a_key = k 282 | lora_b_key = k.replace('lora_A','lora_B') 283 | base_model_sd[original_key] += ( 284 | transpose(lora_model_sd[lora_b_key].float() @ lora_model_sd[lora_a_key].float(),fan_in_fan_out) * lora_scaling 285 | ) 286 | assert base_model_sd[original_key].dtype == torch.float16 287 | 288 | # did we do anything? 289 | assert not torch.allclose(first_weight_old, first_weight) 290 | 291 | tokenizer.save_pretrained(output_dir) 292 | 293 | if output_type=='huggingface': 294 | print("Saving to Hugging Face format...") 295 | LlamaForCausalLM.save_pretrained(base_model, output_dir) #, state_dict=deloreanized_sd) 296 | else: # output_type=='pth 297 | print("Saving to pth format...") 298 | 299 | base_model_sd = base_model.state_dict() 300 | del lora_model, base_model, lora_model_sd 301 | 302 | params = params_of_models[model_size] 303 | num_shards = num_shards_of_models[model_size] 304 | n_layers = params["n_layers"] 305 | n_heads = params["n_heads"] 306 | dim = params["dim"] 307 | dims_per_head = dim // n_heads 308 | base = 10000.0 309 | inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)) 310 | 311 | save_shards(model_sd=base_model_sd, num_shards=num_shards) 312 | --------------------------------------------------------------------------------