Model Card for Model ID
This is official model checkpoint for Asclepius-7B (arxiv) This model is the first publicly shareable clinical LLM, trained with synthetic data.
Model Details
Model Description
- Model type: Clinical LLM (Large Language Model)
- Language(s) (NLP): English
- License: CC-BY-NC-SA 4.0
- Finetuned from model [optional]: LLaMA-7B
Model Sources [optional]
- Repository: https://github.com/starmpcc/Asclepius
- Paper: https://arxiv.org/abs/2309.00237
- Data: https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
Uses
This model can perform below 8 clinical NLP tasks, with clincal notes.
- Named Entity Recognition
- Abbreviation Expansion
- Relation Extraction
- Temporal Information Extraction
- Coreference Resolution
- Paraphrasing
- Summarization
- Question Answering
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
ONLY USE THIS MODEL FOR RESEARCH PURPOSE!!
How to Get Started with the Model
prompt = """You are an intelligent clinical languge model.
Below is a snippet of patient's discharge summary and a following instruction from healthcare professional.
Write a response that appropriately completes the instruction.
The response should provide the accurate answer to the instruction, while being concise.
[Discharge Summary Begin]
{note}
[Discharge Summary End]
[Instruction Begin]
{question}
[Instruction End]
"""
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-7B")
model = AutoModel.from_pretrained("starmpcc/Asclepius-7B")
note = "This is a sample note"
question = "What is the diagnosis?"
model_input = prompt.format(note=note, question=question)
input_ids = tokenizer(model_input, return_tensors="pt").input_ids
output = model.generate(input_ids)
print(tokenizer.decode(output[0]))
Training Details
Training Data
https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
Training Procedure
- Initial training was conducted using causal language modeling on synthetic clinical notes.
- It was then fine-tuned with clinical instruction-response pairs.
- For a comprehensive overview of our methods, our upcoming paper will serve as a resource.
Training Hyperparameters
- We followed config used in Stanford Alpaca
Speeds, Sizes, Times
- Pre-Training (1 epoch): 1h 33m with 8x A100 80G
- Instruction Fine-Tuning (3 epoch): 7h 26m with 8x A100 80G
Citation
BibTeX:
@misc{kweon2023publicly,
title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes},
author={Sunjun Kweon and Junu Kim and Jiyoun Kim and Sujeong Im and Eunbyeol Cho and Seongsu Bae and Jungwoo Oh and Gyubok Lee and Jong Hak Moon and Seng Chan You and Seungjin Baek and Chang Hoon Han and Yoon Bin Jung and Yohan Jo and Edward Choi},
year={2023},
eprint={2309.00237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}