Asclepius Llama3 8B

🚀 Asclepius-Llama3-8B Model

This is an official model checkpoint for Asclepius-Llama3-8B, an enhanced version of Asclepius-7B, which can perform 8 clinical NLP tasks.

🚀 Quick Start

To get started with the Asclepius-Llama3-8B model, you can use the following Python code:

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, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-Llama3-8B", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("starmpcc/Asclepius-Llama3-8B")

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]))

✨ Features

This model can perform the following 8 clinical NLP tasks with clinical notes:

• Named Entity Recognition
• Abbreviation Expansion
• Relation Extraction
• Temporal Information Extraction
• Coreference Resolution
• Paraphrasing
• Summarization
• Question Answering

📚 Documentation

Model Details

Model Description

Model Sources

• Repository: https://github.com/starmpcc/Asclepius
• Paper: https://arxiv.org/abs/2309.00237
• Data: https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

⚠️ Important Note

ONLY USE THIS MODEL FOR RESEARCH PURPOSE!!

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 the config used in Stanford Alpaca

Speeds, Sizes, Times

• Pre-Training (1 epoch): 2h 59m with 4x A100 80G
• Instruction Fine-Tuning (3 epoch): 30h 41m with 4x A100 80G

📄 License

This model is licensed under CC-BY-NC-SA 4.0.

📚 Citation

BibTeX:

@article{kweon2023publicly,
  title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes},
  author={Kweon, Sunjun and Kim, Junu and Kim, Jiyoun and Im, Sujeong and Cho, Eunbyeol and Bae, Seongsu and Oh, Jungwoo and Lee, Gyubok and Moon, Jong Hak and You, Seng Chan and others},
  journal={arXiv preprint arXiv:2309.00237},
  year={2023}
}

🔍 UPDATE

2024.01.10

• Asclepius-R, the variant of Asclepius that trained on MIMIC-III discharge summaries, is now available on Physionet!