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---
license: cc-by-nc-4.0
datasets:
- starmpcc/Asclepius-Synthetic-Clinical-Notes
language:
- en
pipeline_tag: text2text-generation
tags:
- medical
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is official model checkpoint for Asclepius-7B [arxiv](todo)
This model is the first publicly shareable clinical LLM, trained with synthetic data.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/starmpcc/Asclepius
- **Paper [optional]:** TODO Arxiv
- **Data:** https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
ONLY USE THIS MODEL FOR RESEARCH PURPOSE!!
## How to Get Started with the Model
```python
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
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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](https://github.com/tatsu-lab/stanford_alpaca)
-
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- Pre-Training (1 epoch): 1h 33m with 8x A100 80G
- Instruction Fine-Tuning (3 epoch): 7h 26m with 8x A100 80G
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
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