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https://github.com/chaoyi-wu/PMC-LLaMA

The official codes for "PMC-LLaMA: Towards Building Open-source Language Models for Medicine"
https://github.com/chaoyi-wu/PMC-LLaMA

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The official codes for "PMC-LLaMA: Towards Building Open-source Language Models for Medicine"

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README 

        
# PMC-LLaMA



The official codes for "PMC-LLaMA: Towards Building Open-source Language Models for Medicine". 



* [Latest News](#latest-news)

* [Environment](#environment)

* [Quick Start](#quick-start)

* [Training](#training)

* [Results](#results)

    * [QA Benchmark](#qa-benchmark)

    * [Zero-shot Cases](#zero-shot-cases)

* [Acknowledge](#acknowledge)

* [Contact](#contact)



[**Arxiv Version**](https://arxiv.org/abs/2304.14454)



We prove that medical LLM should be first pretrained with domain corpus, and then tuned with instructions following dataset.



We have released The latest model **PMC_LLaMA_13B** finetuned on our instructions the following dataset.

It has shown a better ability to follow user instructions than MedLLaMA_13B.






Similarly, it can be easily loaded with:



```python

import transformers

import torch

tokenizer = transformers.LlamaTokenizer.from_pretrained('axiong/PMC_LLaMA_13B')

model = transformers.LlamaForCausalLM.from_pretrained('axiong/PMC_LLaMA_13B')

```

Hereby we present PMC_LLaMA's versions and briefs.



[MedLLaMA_13B](https://huggingface.co/chaoyi-wu/MedLLaMA_13B) is pretrained on medical corpus, and [PMC_LLaMA_13B](https://huggingface.co/axiong/PMC_LLaMA_13B) is further finetuned based on that.



| Version | Link | Brief | Release Date |

| --- | --- | --- | --- |

| MMedLM ![](./figures/new.gif) | https://github.com/MAGIC-AI4Med/MMedLM | Further Pretrained Multilingual LLM | 2024/02/21 |

| 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 |



## Latest News

We have released a new multilingual medical LLM **MMedLM**, you can find detailed information in [here](https://github.com/MAGIC-AI4Med/MMedLM). 



It is **better than PMC-LLaMA** even in the English domain while it has not passed instruction tuning, thus is more suitable for fine-tuning instead of zero-shot or few-shot prompting. 



## Environment

Simply set up the required environment as following:

```bash

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, datasets

```



## Quick Start

Check `simple_test.py` for quickly use PMC-LLaMA or you can follow this folowing simple sample.



```python

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])

```



## Training



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](https://github.com/allenai/s2orc). Only those papers with PubMed IDs are deemed as medical-related and used during pretraining.



The book is listed in this repo as [MedicalBook.xlsx](https://github.com/chaoyi-wu/PMC-LLaMA/blob/main/MedicalBook.xlsx), due to licenses, we cannot release raw content. For reproducing, pls buy and process the books.



More details about how to fine-tune LLaMA can refer to [Finetune_LLAMA](https://github.com/chaoyi-wu/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](https://huggingface.co/datasets/axiong/pmc_llama_instructions).



## Results



### QA Benchmark

| Method              | Model Size          | USMLE | MedMCQA | PubMedQA |

|---------------------|---------------------|------------------|--------------|------------------|

| 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 ![](./figures/new.gif) | 13B | **56.36**   | **56.04**  | **77.9**  |



Note that, the manual and zero-shot results with * are referred from [LMFLow](https://github.com/OptimalScale/LMFlow/tree/main/src/lmflow).



### Zero-shot Cases



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.



## Acknowledge

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



## Contact

If you have any question, please feel free to contact [email protected].