Please note that due to the ongoing development of the project, the model weights in this repository may differ from those in our currently deployed demo.

Check DISC-MedLLM for more information.

DISC-MedLLM

Demo | Tech Report

This is the repo of DISC-MedLLM, a medical domain-specific LLM designed for conversational healthcare scenarios by Fudan-DISC lab.

The following resources have been released:

DISC-Med-SFT Dataset (with out behavioral preference dataset)
Model weights of DISC-MedLLM

You can check this link to try our online demo.

Overview

The DISC-MedLLM is a large-scale domain-specific model designed for conversational healthcare scenarios. It can address a variety of your needs, including medical consultations and treatment inquiries, offering you high-quality health support services.

The DISC-MedLLM effectively bridges the gap between general language models and real-world medical consultations, as evidenced by experimental results.

Owing to our goal-oriented strategy and the framework that integrates both LLM and Human in the loop based on real-world doctor-patient dialogues and knowledge graphs, DISC-MedLLM boasts several features:

Knowledge-intensive and reliable
Ability of multi-turn inquiry
Alignment with human preferences

Dataset

To train DISC-MedLLM, we construct a high-quality dataset called DISC-Med-SFT consisting of over 470k distinct examples derived from existing medical datasets. We adopt a goal-oriented strategy by selectively reconstructing the dataset using a few deliberately chosen sources. These data sources serve the purpose of assisting LLMs in acquiring medical domain knowledge, aligning behavioral patterns with human preferences, and capturing real-world online medical dialogue distributions.

Dateset	Original Source	Size
Re-constructed AI Doctor-Patient Dialogue	MedDialog	400k
cMedQA2	20k
Knowledge Graph QA pairs	CMeKG	50k
Behavior Preference Dataset	Manual selection	2k
Others	MedMCQA	8k
MOSS-SFT	33k
Alpaca-GPT4-zh	1k

Deploy

The current version of DISC-MedLLM is derived from the Baichuan-13B-Base. You can directly download our model weights from the HuggingFace repository, or automatically obtain them through the demo code.

Using through hugging face transformers

python

>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> from transformers.generation.utils import GenerationConfig
>>> tokenizer = AutoTokenizer.from_pretrained("Flmc/DISC-MedLLM", use_fast=False, trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("Flmc/DISC-MedLLM", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
>>> model.generation_config = GenerationConfig.from_pretrained("Flmc/DISC-MedLLM")
>>> messages = []
>>> messages.append({"role": "user", "content": "我感觉自己颈椎非常不舒服，每天睡醒都会头痛"})
>>> response = model.chat(tokenizer, messages)
>>> print(response)

Additionally, since the current version uses Baichuan as the base model, you can refer to its repo for deploying with int8, int4 quantized inference. However, using quantized deployment will result in performance degradation.

Training

You can fine-tuning our model using the data same as our data schema. Our train code is derived from Firefly with the different data schema and dialogue format. We jsut provide the code of Full Params Fine-tuning:

deepspeed --num_gpus={num_gpus} ./train/train.py --train_args_file ./train/train_args/sft.json

Please check the setup of sft.json before you attempt to start training.

If you want to fine-tuning our model with other training code, please use the following dialogue format.

<\b><$user_token>content<$assistant_token>content<\s><$user_token>content ...

The user_token and assistant_token we used are 195 and 196, respectly. Which is same as Baichuan-13b-Chat.

Delcaration

Due to the inherent limitations of language models, we cannot assure the accuracy or reliability of information generated by this model. This model is designed exclusively for research and testing by individuals and academic groups. We urge users to critically assess any information or medical advice obtained through the model's output. Blindly trusting or following such information is strongly discouraged. We disclaim responsibility for any issues, risks, or adverse consequences resulting from the model's use.

Licenses

The use of the source code in this repository complies with the Apache 2.0 License.

Citation

angular2

@misc{bao2023discmedllm,
      title={DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation}, 
      author={Zhijie Bao and Wei Chen and Shengze Xiao and Kuang Ren and Jiaao Wu and Cheng Zhong and Jiajie Peng and Xuanjing Huang and Zhongyu Wei},
      year={2023},
      eprint={2308.14346},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

jiey2/DISC-MedLLM