The official codes for "PMC-LLaMA: Continue Training LLaMA on Medical Papers"
We have release a new model MedLLaMA-13B finetuned with LLaMA-13B on some medical corpus, termed as MedLLaMA-13B. It have been proved to be more powerful than both LLaMA-13B and PMC-LLaMa, refering to our benchmark for detail comparison:
Similarly it can be easily loaded with:
import transformers
import torch
tokenizer = transformers.LlamaTokenizer.from_pretrained('chaoyi-wu/MedLLaMA_13B')
model = transformers.LlamaForCausalLM.from_pretrained('chaoyi-wu/MedLLaMA_13B')
We continue pre-training LLaMA on 4.8M PubmedCentral papers.
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,sentencepiece,datasets
Check 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('chaoyi-wu/PMC_LLAMA_7B')
model = transformers.LlamaForCausalLM.from_pretrained('chaoyi-wu/PMC_LLAMA_7B')
sentence = 'Hello, doctor'
batch = tokenizer(
sentence,
return_tensors="pt",
add_special_tokens=False
)
with torch.no_grad():
generated = model.generate(inputs = batch["input_ids"], max_length=200, do_sample=True, top_k=50)
print('model predict: ',tokenizer.decode(generated[0]))
The raw training data can be dowloaded from S2ORC, filter out the papers with PubmedCentral IDs, and you can get the training data we use.
We will also release a version of training data soon.
Check training.py and training.sh for re-produce our work.
More details about how to fine-tune LLaMA can refer to Finetune_LLAMA
| Method | Setting | USMLE(OOD/ID) | MedMCQA(ID) | PubMedQA(ID) |
|---|---|---|---|---|
| Human (pass) | Manual* | 50.0 | -- | 60.0 |
| Human (expert) | Manual* | 87.0 | 90.0 | 78.0 |
| InstructGPT-175B | Zero-shot* | 46.0 | 44.0 | 73.2 |
| ChatGPT | Zero-shot* | 57.0 | 44.7 | 63.9 |
| LLaMA-7B | Zero-shot* | 27.1 | 24.3 | 5.2 |
| LLaMA-33B | Zero-shot* | 43.4 | 30.3 | 1.8 |
| LLaMA-7B-Full | Full fine-tuning | 44.55/35.66 | 48.15 | 73.4 |
| PMC-LLaMA-7B-Full | Full fine-tuning | 44.70/40.61 | 50.54 | 69.5 |
| LLaMA-13B-Full | Full fine-tuning | 45.48/39.36 | 51.42 | 77.2 |
| MedLLaMA-13B-Full | Full fine-tuning | 48.15/43.52 | 54.15 | 77.7 |
| LLaMA-7B-PEFT | PEFT | 29.38/27.34 | 32.37 | 65.8 |
| PMC-LLaMA-7B$-PEFT | PEFT | 30.64/28.52 | 34.33 | 68.2 |
| LLaMA-13B-PEFT | PEFT | 38.73/38.73 | 39.56 | 65.4 |
| MedLLaMA-13B-Full | PEFT | 39.12/39.98 | 41.26 | 69.4 |
Note that, the manual and zero-shot results with * are referred from LMFLow.
Note that, due to train on the papers, PMC-LLaMA may generate some citation numbers (LLaMA somtimes will do this as well) and we dismiss them in the cases to show the main contents.
Minimal LLaMA -- https://github.com/zphang/minimal-llama
alpaca -- https://github.com/tatsu-lab/stanford_alpaca
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].