medAlpaca: Finetuned Large Language Models for Medical Question Answering

Project Overview

MedAlpaca expands upon both Stanford Alpaca and AlpacaLoRA to offer an advanced suite of large language models specifically fine-tuned for medical question-answering and dialogue applications. Our primary objective is to deliver an array of open-source language models, paving the way for seamless development of medical chatbot solutions.

These models have been trained using a variety of medical texts, encompassing resources such as medical flashcards, wikis, and dialogue datasets. For more details on the data utilized, please consult the data section.

Getting Started

Create a new virtual environment, e.g. with conda

conda create -n medalpaca python>=3.9

Install the required packages:

pip install -r requirements.txt

Training of medAlpaca

Memory Requirements

We have benchmarked the needed GPU memory as well as the approximate duration per epoch for finetuning LLaMA 7b on the Medical Meadow small dataset (~6000 Q/A pairs) on a single GPU:

Model	8bit trainig	LoRA	fp16	bf16	VRAM Used	Gradient cktp	Duration/epoch
LLaMA 7b	True	True	True	False	8.9 GB	False	77:30
LLaMA 7b	False	True	True	False	18.8 GB	False	14:30
LLaMA 7b	False	False	True	False	OOM	False	-
LLaMA 7b	False	False	False	True	79.5 GB	True	35:30
LLaMA 7b	False	False	False	False	OOM	True	-

Train medAlpaca based on LLaMA

If you have access to the LLaMA or Alpaca weights you can finetune the model with the following command. Just replace <PATH_TO_LLAMA_WEIGHTS> with the folder containing you LLaMA or Alpaca weights.

python medalpaca/train.py \
    --model PATH_TO_LLAMA_WEIGHTS \
    --data_path medical_meadow_small.json \
    --output_dir 'output' \
    --train_in_8bit True \  
    --use_lora True \
    --bf16 True \
    --tf32 False \
    --fp16 False \
    --global_batch_size 128 \
    --per_device_batch_size 8 \

Per default the script performs mixed precision training.
You can toggle 8bit training with the train_in_8bit flag. While 8 bit training currently only works with use_lora True, however you can use LoRA without 8 bit training. It is also able to train other models such as facebook/opt-6.7 with the above script.

Data

To ensure your cherished llamas and alpacas are well-fed and thriving, we have diligently gathered high-quality biomedical open-source datasets and transformed them into instruction tuning formats. We have dubbed this endeavor Medical Meadow. Medical Meadow currently encompasses roughly 1.5 million data points across a diverse range of tasks, including openly curated medical data transformed into Q/A pairs with OpenAI's gpt-3.5-turbo and a collection of established NLP tasks in the medical domain. Please note, that not all data is of the same quantitiy and quality and you may need tp subsample the data for training your own model. We will persistently update and refine the dataset, and we welcome everyone to contribute more 'grass' to Medical Meadow!

Data Overview

Name	Source	n	n included in training
Medical Flashcards	medalpaca/medical_meadow_medical_flashcards	33955	33955
Wikidoc	medalpaca/medical_meadow_wikidoc	67704	10000
Wikidoc Patient Information	medalpaca/medical_meadow_wikidoc_patient_information	5942	5942
Stackexchange academia	medalpaca/medical_meadow_stack_exchange	40865	40865
Stackexchange biology	medalpaca/medical_meadow_stack_exchange	27887	27887
Stackexchange fitness	medalpaca/medical_meadow_stack_exchange	9833	9833
Stackexchange health	medalpaca/medical_meadow_stack_exchange	7721	7721
Stackexchange bioinformatics	medalpaca/medical_meadow_stack_exchange	5407	5407
USMLE Self Assessment Step 1	medalpaca/medical_meadow_usmle_self	119	92 (test only)
USMLE Self Assessment Step 2	medalpaca/medical_meadow_usmle_self	120	110 (test only)
USMLE Self Assessment Step 3	medalpaca/medical_meadow_usmle_self	135	122 (test only)
MEDIQA	original, preprocessed	2208	2208
CORD-19	original, preprocessed	1056660	50000
MMMLU	original, preprocessed	3787	3787
Pubmed Health Advice	original, preprocessed	10178	10178
Pubmed Causal	original, preprocessed	2446	2446
ChatDoctor	original	215000	10000
OpenAssistant	original	9209	9209

Data description

please refer to DATA_DESCRIPTION.md

Benchmarks

We are benchmarking all models on the USMLE self assessment, which is available at this link. Note, that we removed all questions with images, as our models are not multimodal.

Model	Step1	Step2	Step3
LLaMA 7b	0.198	0.202	0.203
Alpaca 7b naive (weights)	0.275	0.266	0.293
Alpaca 7b LoRA	0.220	0.138	0.252
MedAlpaca 7b	0.297	0.312	0.398
MedAlpaca 7b LoRA	0.231	0.202	0.179
MedAlpaca 7b LoRA 8bit	0.231	0.241	0.211
ChatDoctor (7b)	0.187	0.185	0.148
LLaMA 13b	0.222	0.248	0.276
Alpaca 13b naive	0.319	0.312	0.301
MedAlpaca 13b	0.473	0.477	0.602
MedAlpaca 13b LoRA	0.250	0.255	0.255
MedAlpaca 13b LoRA 8bit	0.189	0.303	0.289
MedAlpaca 30b (still training)	TBA	TBA	TBA
MedAlpaca 30b LoRA 8bit	0.315	0.327	0.361

We are continuously working on improving the training as well as our evaluation prompts. Expect this table to change quite a bit.

Access the models

Visit the zoo and have a look at our alpacas here: https://huggingface.co/medalpaca

It should be obvious, but the models provided on this platform are shared for research purposes only and should not be used in any healthcare applications or settings. While we are excited to showcase our experimental models, please be aware that they have not undergone extensive testing or validation, and their reliability cannot be guaranteed. We kindly ask you to exercise caution when using these models, and we appreciate your understanding as we continue to explore and develop this innovative technology.

Paper

@article{han2023medalpaca,
  title={MedAlpaca--An Open-Source Collection of Medical Conversational AI Models and Training Data},
  author={Han, Tianyu and Adams, Lisa C and Papaioannou, Jens-Michalis and Grundmann, Paul and Oberhauser, Tom and L{\"o}ser, Alexander and Truhn, Daniel and Bressem, Keno K},
  journal={arXiv preprint arXiv:2304.08247},
  year={2023}
}