The official codes for "PMC-LLaMA: Towards Building Open-source Language Models for Medicine".
We prove that medical LLM should be first pretrained with domain corpus, and then tuned with instructions following dataset.
Hereby we present PMC_LLaMA's versions and briefs.
MedLLaMA_13B is pretrained on medical corpus, and PMC_LLaMA_13B is further finetuned based on that.
| Version | Link | Brief | Release Date |
|---|---|---|---|
| PMC_LLaMA_13B | https://huggingface.co/axiong/PMC_LLaMA_13B | Instruction Tuned | 2023/09/01 |
| MedLLaMA_13B | https://huggingface.co/chaoyi-wu/MedLLaMA_13B | Pre-training LLaMA on 4.8M PubmedCentral papers and Medical Books | 2023/05/01 |
| PMC_LLaMA_7B_10_epoch | https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B_10_epoch | Similar to PMC_LLaMA_7B but trained 10 epochs | 2023/05/01 |
| PMC_LLaMA_7B | https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B | LLaMA-7b finetuned with PMC papers for 5 epochs | 2023/04/25 |
We have release a new model PMC_LLaMA_13B finetuned on our instruction following dataset. It has shown better ability on following user instruction than MedLLaMA_13B.
Similarly it can be easily loaded with:
import transformers
import torch
tokenizer = transformers.LlamaTokenizer.from_pretrained('axiong/PMC_LLaMA_13B')
model = transformers.LlamaForCausalLM.from_pretrained('axiong/PMC_LLaMA_13B')Simply set up the required environment as following:
conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.6 -c pytorch -c nvidia
pip install transformers=4.28.1, sentencepiece, datasetsCheck simple_test.py for quickly use PMC-LLaMA or you can follow this folowing simple sample.
import transformers
import torch
tokenizer = transformers.LlamaTokenizer.from_pretrained('axiong/PMC_LLaMA_13B')
model = transformers.LlamaForCausalLM.from_pretrained('axiong/PMC_LLaMA_13B')
model.cuda() # move the model to GPU
prompt_input = (
'Below is an instruction that describes a task, paired with an input that provides further context.'
'Write a response that appropriately completes the request.\n\n'
'### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:'
)
example = {
"instruction": "You're a doctor, kindly address the medical queries according to the patient's account. Answer with the best option directly.",
"input": (
"###Question: A 23-year-old pregnant woman at 22 weeks gestation presents with burning upon urination. "
"She states it started 1 day ago and has been worsening despite drinking more water and taking cranberry extract. "
"She otherwise feels well and is followed by a doctor for her pregnancy. "
"Her temperature is 97.7°F (36.5°C), blood pressure is 122/77 mmHg, pulse is 80/min, respirations are 19/min, and oxygen saturation is 98% on room air."
"Physical exam is notable for an absence of costovertebral angle tenderness and a gravid uterus. "
"Which of the following is the best treatment for this patient?"
"###Options: A. Ampicillin B. Ceftriaxone C. Doxycycline D. Nitrofurantoin"
)
}
input_str = [prompt_input.format_map(example)]
model_inputs = tokenizer(
input_str,
return_tensors='pt',
padding=True,
)
print( f"\033[32mmodel_inputs\033[0m: { model_inputs }" )
topk_output = model.generate(
model_inputs.input_ids.cuda(),
max_new_tokens=1000,
top_k=50
)
output_str = tokenizer.batch_decode(topk_output)
print('model predict: ', output_str[0])The training process can be divided as two phases: pretrain and instruction-tuning.
Pre-training
The script for pretraining locates at Pretrain/training.sh.
Our pretraining dataset sources from S2ORC. Only those papers with PubMed IDs are deemed as medical-related and used during pretraining.
More details about how to fine-tune LLaMA can refer to Finetune_LLAMA
Instruction Tuning
We also provide instruction tuning script at SFT/train.py.
And you can find our instruction dataset at PMC LLaMA Instructions.
| Method | Model Size | USMLE(OOD/ID) | MedMCQA(ID) | PubMedQA(ID) |
|---|---|---|---|---|
| Human (pass) | - | 50.0 | -- | 60.0 |
| Human (expert) | - | 87.0 | 90.0 | 78.0 |
| ChatGPT | 175B | 57.0 | 44.7 | 63.9 |
| LLaMA-2 | 13B | 42.73 | 37.41 | 68.0 |
| LLaMA-2 | 70B | 43.68 | 35.02 | 74.3 |
| Med-Alpaca | 13B | 30.85 | 31.13 | 53.2 |
| Chat-Doctor | 7B | 33.93 | 31.10 | 54.3 |
PMC_LLaMA_13B  |
13B | 56.36 | 56.04 | 77.9 |
Note that, the manual and zero-shot results with * are referred from LMFLow.
We demonstrate PMC_LLaMA_13B's responses with out of domain queries.
Note that, due to train on the papers, MedLLaMA_13B may generate some citation numbers (LLaMA somtimes will do this as well) and we dismiss them in the cases to show the main contents. While for PMC_LLaMA_13B, it's much easier to extract the correct answer as the output result is structured.
Minimal LLaMA -- https://github.com/zphang/minimal-llama
alpaca -- https://github.com/tatsu-lab/stanford_alpaca
LMFLow -- https://github.com/OptimalScale/LMFlow/tree/main/src/lmflow
LLaMA: Open and Efficient Foundation Language Models -- https://arxiv.org/abs/2302.13971
If you have any question, please feel free to contact [email protected].