Release a talk slide

README.md

@@ -20,6 +20,8 @@
 
 
 ## News
+**[2023/09/24]发布[《面向智慧医疗的大语言模型微调技术》](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/tree/main/doc/Tuning_Methods_for_LLMs_towards_Health_Intelligence.pdf)**
+
 **[2023/09/12]在arxiv发布[《探索大模型从医学文献中交互式知识的获取》](https://arxiv.org/pdf/2309.04198.pdf)**
 
 **[2023/09/08]在arxiv发布[《基于知识微调的大语言模型可靠中文医学回复生成方法》](https://arxiv.org/pdf/2309.04175.pdf)**  

README_EN.md

@@ -1,18 +1,37 @@
 [**中文**](./README.md) | [**English**](./README_EN.md)
 <p align="center" width="100%">
-<a href="https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/" target="_blank"><img src="assets/logo/logo.png" alt="SCIR-HI-HuaTuo" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
+<a href="https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/" target="_blank"><img src="assets/logo/logo_new.png" alt="SCIR-HI-HuaTuo" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
-# BenTsao (original name: HuaTuo): Tuning LLaMA Model With Chinese Medical Instructions
+# BenTsao (original name: HuaTuo): Instruction-tuning Large Language Models With Chinese Medical Knowledge
 
 [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/blob/main/LICENSE) [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/)
 
-This repo open-sources the Instruct-tuned LLaMA-7B model that has been fine-tuned with Chinese medical instructions. We constructed a Chinese medical instruct-tuning dataset using medical knowledge graphs and the GPT3.5 API, and performed instruction-tuning on LLaMA based on this dataset, improving its question-answering performance in the medical field.
+This repo open-sources a series of instruction-tuned large language models with Chinese medical instruction datasets, including LLaMA、Alpaca-Chinese、Bloom、Huozi. 
+
+We constructed a Chinese medical instruct-tuning dataset using medical knowledge graphs, medical literatures and the GPT3.5 API, and performed instruction-tuning on various base models with this dataset, improving its question-answering performance in the medical field.
+
+
+## News
+**[2023/08/07] 🔥🔥 Released a model instruction-tuned based on [Huozi](https://github.com/HIT-SCIR/huozi), resulting in a significant improvement in model performance. 🔥🔥**
+
+[2023/08/05] The "Bentsao" model was presented as a poster at the CCL 2023 Demo Track.
+
+[2023/08/03] SCIR Lab open-sourced the [Huozi](https://github.com/HIT-SCIR/huozi) general question-answering model. Everyone is welcome to check it out! 🎉🎉
+
+[2023/07/19] Released a model instruction-tuned based on [Bloom](https://huggingface.co/bigscience/bloom-7b1).
+
+[2023/05/12] The model was renamed from "Huatuo" to "Bentsao".
+
+[2023/04/28] Released a model instruction-tuned based on the [Chinese-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca).
+
+[2023/04/24] Released a model instruction-tuned based on LLaMA with medical literature.
+
+[2023/03/31] Released a model instruction-tuned based on LLaMA with a medical knowledge base.
+
+
 
-In addition, we tried to use the GPT3.5 API to integrate [conclusions] in the medical literature as external information into multiple rounds of dialogue, and based on this, we fine-tuned LLaMA. At present, we only open model parameters trained for the single disease "liver cancer". In the future, we plan to release a medical dialogue dataset incorporating medical literature conclusions, and plan to train models for 16 diseases related to "liver, gallbladder and pancreas".
 
-We also trained a medical version of ChatGLM: [ChatGLM-6B-Med](https://github.com/SCIR-HI/Med-ChatGLM) based on the same data.
 
-We are about to release our new model [扁鹊（PienChueh）](https://github.com/SCIR-HI/Bian-Que_Pien-Chueh). 
 
 
 ## A Quick Start
@@ -21,27 +40,42 @@ Firstly, install the required packages. It is recommended to use Python 3.9 or a
 ```
 pip install -r requirements.txt
 ```
-### Model download
+
+For all base models, we adopted the semi-precision base model LoRA fine-tuning method for instruction fine-tuning training, in order to strike a balance between computational resources and model performance.
+
+### Base models
+ - [Huozi1.0](https://github.com/HIT-SCIR/huozi), Bloom-7B-based Chinese QA model
+ - [Bloom-7B](https://huggingface.co/bigscience/bloomz-7b1)
+ - [Alpaca-Chinese-7B](https://github.com/ymcui/Chinese-LLaMA-Alpaca), LLaMA-based Chinese QA model
+ - [LLaMA-7B](https://huggingface.co/decapoda-research/llama-7b-hf)
+
+
+### LoRA weight download
 LORA weights can be downloaded through Baidu Netdisk or Huggingface.
 
--Based on medical knowledge base. [Baidu Netdisk](https://pan.baidu.com/s/1jih-pEr6jzEa6n2u6sUMOg?pwd=jjpf) or [HuggingFace](https://huggingface.co/thinksoso/lora-llama-med).
+1. 🔥LoRA for Huozi 1.0
--Based on medical literature. [Baidu Netdisk](https://pan.baidu.com/s/1jADypClR2bLyXItuFfSjPA?pwd=odsk)
+  - with the medical knowledge base and medical QA dataset [BaiduDisk](https://pan.baidu.com/s/1BPnDNb1wQZTWy_Be6MfcnA?pwd=m21s)
+2. LoRA for Bloom
+ - with the medical knowledge base and medical QA dataset [BaiduDisk](https://pan.baidu.com/s/1jPcuEOhesFGYpzJ7U52Fag?pwd=scir) and [Hugging Face](https://huggingface.co/lovepon/lora-bloom-med-bloom)
+3. LoRA for Chinese Alpaca
+ - with the medical knowledge base [BaiduDisk](https://pan.baidu.com/s/16oxcjzXnXjDpL8SKihgNxw?pwd=scir) and [Hugging Face](https://huggingface.co/lovepon/lora-alpaca-med)
+ - with the medical knowledge base and medical literature [BaiduDisk](https://pan.baidu.com/s/1HDdK84ASHmzOFlkmypBIJw?pwd=scir) and [Hugging Face](https://huggingface.co/lovepon/lora-alpaca-med-alldata)
+4. LoRA for LLaMA
+ - with the medical knowledge base [BaiduDisk](https://pan.baidu.com/s/1jih-pEr6jzEa6n2u6sUMOg?pwd=jjpf) and [Hugging Face](https://huggingface.co/thinksoso/lora-llama-med)
+ - with medical literature [BaiduDisk](https://pan.baidu.com/s/1jADypClR2bLyXItuFfSjPA?pwd=odsk) and [Hugging Face](https://huggingface.co/lovepon/lora-llama-literature)
+
 
 Download the LORA weight file and extract it. The format of the extracted file should be as follows:
 
 ```
-#Based on medical knowledge base
+**lora-folder-name**/
-lora-llama-med/
+  - adapter_config.json   # LoRA configuration
-  - adapter_config.json		# LoRA weight configuration file
+  - adapter_model.bin   # LoRA weight
-  - adapter_model.bin		# LoRA weights
-
-#Based on medical literature
-lora-llama-med-literature/
-  - adapter_config.json   # LoRA weight configuration file
-  - adapter_model.bin   # LoRA weights
-
 ```
 
+We also trained a medical version of ChatGLM: [ChatGLM-6B-Med](https://github.com/SCIR-HI/Med-ChatGLM) based on the same data.
+
+
 ### Infer
 We provided some test cases in `./data/infer.json`, which can be replaced with other datasets. Please make sure to keep the format consistent.
 
@@ -59,7 +93,32 @@ bash ./scripts/infer-literature-single.sh
 bash ./scripts/infer-literature-multi.sh
 ```
 
+The code for the infer.sh script is as follows. Please replace the base model base_model, the LoRA weights lora_weights, and the test dataset path instruct_dir before running
+
+	python infer.py \
+			    --base_model 'BASE_MODEL_PATH' \
+			    --lora_weights 'LORA_WEIGHTS_PATH' \
+			    --use_lora True \
+			    --instruct_dir 'INFER_DATA_PATH' \
+			    --prompt_template 'TEMPLATE_PATH'
+		    
+
+The prompt template is relevant to the model as follows: 
+
+| Huozi&Bloom                      | LLaMA&Alpaca                                                                          |                                       
+|:------------------------------|:--------------------------------------------------------------------------------------|
+| `templates/bloom_deploy.json` | with the medical knowledge base`templates/med_template.json` <br>  with the medical literature`templates/literature_template.json` |
+
+
+
+other reference in `./scripts/test.sh`
+
+	
+## Methodology
+The base model has limited effectiveness in medical question-answering scenarios. Instruction-tuning is an efficient method to give the base model the ability to answer human questions
+	    
 ### Dataset construction
+#### Medical knowledge base
 We used both publicly available and self-built Chinese medical knowledge bases, with a primary reference to [cMeKG](https://github.com/king-yyf/CMeKG_tools). The medical knowledge base is built around diseases, drugs, and diagnostic indicators, with fields including complications, risk factors, histological examinations, clinical symptoms, drug treatments, and adjuvant therapies. An example of the knowledge base is shown below:
 
 
@@ -80,6 +139,7 @@ We provided a training dataset for the model, consisting of more than eight thou
 
 The quality of the dataset for instruct-tuning is still limited. We will continue to iterate and improve it. Meanwhile, the medical knowledge base and dataset construction code are still being organized and will be released once completed.
 
+#### Medical literature
 In addition, we collected Chinese medical literature on liver cancer in 2023, and used the GPT3.5 interface to collect multiple rounds of question-and-answer data around the medical literature. We provide 1k training examples in `./data_literature/liver_cancer.json`. At present, the quality of training samples is still limited. In the future, we will further iterate the data and release it in the form of `public dataset`. An example of a training sample is as follows:
 
 <p align="center" width="100%">
@@ -88,6 +148,7 @@ In addition, we collected Chinese medical literature on liver cancer in 2023, an
 
 </p>
 
+At present, we only open model parameters trained for the single disease "liver cancer". In the future, we plan to release a medical dialogue dataset incorporating medical literature conclusions, and plan to train models for 16 diseases related to "liver, gallbladder and pancreas".
 
 ### Finetune
 To fine-tune LLaMA with your own dataset, please construct your dataset following the format of `./data/llama_data.json` and run the finetune script.
@@ -109,15 +170,45 @@ https://wandb.ai/thinksoso/llama_med/runs/a5wgcnzt/overview?workspace=user-think
 
 ## Comparison of models
 
+Note: test in March 2023.
+
 | Input入 | Llama output  | Alpaca output | HuaTuo output |
 |:------------- |:---------------| :-------------|:-------------|
 |小张最近感觉身体不适，出现心悸、气促等症状。体检发现心脏扩大、搏动减弱。|问题：小张最近感觉身体不适，出现心悸、气促等症状。体检发现心脏扩大、搏动减弱。| 最近，小张一直感觉身体不舒服，有心烦意乱、呼吸急促等症状。体格检查显示心脏增大，肌肉无力。|**小张可能患有心肌炎，建议尽快到医院进行检查和治疗。治疗方案包括药物治疗和手术治疗。**|
 |麻风病和儿童哮喘的病因是否一致？|儿童哮喘和麻风病的病因是一致，但是儿童哮喘和麻风病的病因不一致|是的，哮喘和儿童喘息有相似的原因。| **不一致，麻风病的病因是麻风杆菌感染，而儿童哮喘的病因是喂养不良、吸烟等因素。**|
 |一位患者出现了持续性的胀痛和黄疸，经影像学检查后确诊为肝胆管结石病，应该采取哪些治疗方案？|肝胆管结石病患者应该采取哪些治疗方案？|患者应接受手术和化疗相结合的治疗。| **针对肝胆管结石病，治疗方案包括手术治疗、药物治疗和辅助治疗等。具体治疗方案需要根据患者的具体情况而定。**|
 
+##FAQs
+
+1. 	Q: Why was it renamed to "Bentsao"?
+	
+	A: For naming consistency with the SCIR large language model series, the name of the Chinese medical model has been renamed to "Bentsao".
+
+2. 	Q: Why "Huatuo(华驼)" instead of "Huatuo(华佗)"?
+	
+	A: We chose the name "Huatuo(华驼)" because our base model, LLaMA(美洲驼), and Alpaca(羊驼). Inspired by their names and a pun on the name "tuo驼" & "tuo佗", we named our model "Huatuo(华驼)".
+
+3. 	Q: Does it use Chinese traditional medicine theories or data?
+	
+	A: Not at the moment.
+
+4.	Q: The results from the model vary and are limited in effectiveness.
+
+	A: Due to the diversity considerations of the generative model, results may vary with multiple runs. The current open-source model, owing to the limited Chinese corpus in LLaMA and Alpaca, and a rather coarse way of knowledge integration, may yield inconsistent results. Please try the bloom-based and character-based models.
+5. Q: The model cannot run or the inferred content is completely unacceptable.
+	
+	A: Please ensure that you've installed the dependencies from the requirements, set up the CUDA environment and added the environment variables, and correctly input the downloaded model and Lora's storage location. Any repetition or partial mistakes in the inferred content are occasional issues with the llama-based model and relate to LLaMA's ability in Chinese, training data scale, and hyperparameter settings. Please try the new character-based model. If there are severe issues, please describe in detail the filename, model name, Lora configuration, etc., in an issue. Thank you all.
+Q: Among the released models, which one is the best?
+A: Based on our experience, the character-based model seems to perform relatively better.
+
+
+
+
+
+
 ## Contributors
 
-This project was founded by the Health Intelligence Group of the Research Center for Social Computing and Information Retrieval at Harbin Institute of Technology, including [Haochun Wang](https://github.com/s65b40), [Yanrui Du](https://github.com/DYR1), [Chi Liu](https://github.com/thinksoso), [Rui Bai](https://github.com/RuiBai1999), [Nuwa Xi](https://github.com/rootnx), [Yuhan Chen](https://github.com/Imsovegetable), [Zewen Qiang](https://github.com/1278882181), [Jianyu Chen](https://github.com/JianyuChen01), [Zijian Li](https://github.com/FlowolfzzZ) supervised by Associate Professor Sendong Zhao, Professor Bing Qin, and Professor Ting Liu.
+This project was founded by the Health Intelligence Group of the Research Center for Social Computing and Information Retrieval at Harbin Institute of Technology, including [Haochun Wang](https://github.com/s65b40), [Yanrui Du](https://github.com/DYR1), [Chi Liu](https://github.com/thinksoso), [Rui Bai](https://github.com/RuiBai1999), [Nuwa Xi](https://github.com/rootnx), [Yuhan Chen](https://github.com/Imsovegetable), [Zewen Qiang](https://github.com/1278882181), [Jianyu Chen](https://github.com/JianyuChen01), [Zijian Li](https://github.com/FlowolfzzZ) supervised by Associate Professor [Sendong Zhao](http://homepage.hit.edu.cn/stanzhao?lang=zh), Professor Bing Qin, and Professor Ting Liu.
 
 
 ## Acknowledgements
@@ -146,4 +237,7 @@ If you use the data or code from this project, please declare the reference:
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```
+```
+
+
+