# MedAlpaca LoRA 30b 8bit


## Table of Contents

[Model Description](#model-description)  
- [Architecture](#architecture)    
- [Training Data](#trainig-data)  
[Model Usage](#model-usage)  
[Limitations](#limitations)  

## Model Description
### Architecture
`medalpaca-lora-30b-8bit` is a large language model specifically fine-tuned for medical domain tasks. 
It is based on LLaMA (Large Language Model Meta AI) and contains 7 billion parameters. 
The primary goal of this model is to improve question-answering and medical dialogue tasks.
It was trained using [LoRA](https://arxiv.org/abs/2106.09685) and quantized, to reduce memory footprint. 


### Training Data
The training data for this project was sourced from various resources. 
Firstly, we used Anki flashcards to automatically generate questions, 
from the front of the cards and anwers from the back of the card. 
Secondly, we generated medical question-answer pairs from [Wikidoc](https://www.wikidoc.org/index.php/Main_Page). 
We extracted paragraphs with relevant headings, and used Chat-GPT 3.5 
to generate questions from the headings and using the corresponding paragraphs 
as answers. This dataset is still under development and we believe 
that approximately 70% of these question answer pairs are factual correct. 
Thirdly, we used StackExchange to extract question-answer pairs, taking the 
top-rated question from five categories: Academia, Bioinformatics, Biology, 
Fitness, and Health. Additionally, we used a dataset from [ChatDoctor](https://arxiv.org/abs/2303.14070) 
consisting of 200,000 question-answer pairs, available at https://github.com/Kent0n-Li/ChatDoctor.

| Source                      | n items |
|------------------------------|--------|
| ChatDoc large                | 200000 |
| wikidoc                      | 67704  |
| Stackexchange academia       | 40865  |
| Anki flashcards              | 33955  |
| Stackexchange biology        | 27887  |
| Stackexchange fitness        | 9833   |
| Stackexchange health         | 7721   |
| Wikidoc patient information  | 5942   |
| Stackexchange bioinformatics | 5407   |


## Limitations
The model may not perform effectively outside the scope of the medical domain.
The training data primarily targets the knowledge level of medical students, 
which may result in limitations when addressing the needs of board-certified physicians.
The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown. 
It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.
{"language": ["en"], "license": "cc", "library_name": "transformers", "tags": ["medical"], "pipeline_tag": "text-generation"}
transformers medical text-generation en arxiv:2106.09685 arxiv:2303.14070 license:cc endpoints_compatible has_space region:us
TAGS
#transformers #medical #text-generation #en #arxiv-2106.09685 #arxiv-2303.14070 #license-cc #endpoints_compatible #has_space #region-us 

MedAlpaca LoRA 30b 8bit
=======================


Table of Contents
-----------------


Model Description


* Architecture
* Training Data  

Model Usage  

Limitations


Model Description
-----------------


### Architecture


'medalpaca-lora-30b-8bit' is a large language model specifically fine-tuned for medical domain tasks.
It is based on LLaMA (Large Language Model Meta AI) and contains 7 billion parameters.
The primary goal of this model is to improve question-answering and medical dialogue tasks.
It was trained using LoRA and quantized, to reduce memory footprint.


### Training Data


The training data for this project was sourced from various resources.
Firstly, we used Anki flashcards to automatically generate questions,
from the front of the cards and anwers from the back of the card.
Secondly, we generated medical question-answer pairs from Wikidoc.
We extracted paragraphs with relevant headings, and used Chat-GPT 3.5
to generate questions from the headings and using the corresponding paragraphs
as answers. This dataset is still under development and we believe
that approximately 70% of these question answer pairs are factual correct.
Thirdly, we used StackExchange to extract question-answer pairs, taking the
top-rated question from five categories: Academia, Bioinformatics, Biology,
Fitness, and Health. Additionally, we used a dataset from ChatDoctor
consisting of 200,000 question-answer pairs, available at URL



Limitations
-----------


The model may not perform effectively outside the scope of the medical domain.
The training data primarily targets the knowledge level of medical students,
which may result in limitations when addressing the needs of board-certified physicians.
The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown.
It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.



### Architecture


'medalpaca-lora-30b-8bit' is a large language model specifically fine-tuned for medical domain tasks.
It is based on LLaMA (Large Language Model Meta AI) and contains 7 billion parameters.
The primary goal of this model is to improve question-answering and medical dialogue tasks.
It was trained using LoRA and quantized, to reduce memory footprint. ### Training Data


The training data for this project was sourced from various resources.
Firstly, we used Anki flashcards to automatically generate questions,
from the front of the cards and anwers from the back of the card.
Secondly, we generated medical question-answer pairs from Wikidoc.
We extracted paragraphs with relevant headings, and used Chat-GPT 3.5
to generate questions from the headings and using the corresponding paragraphs
as answers. This dataset is still under development and we believe
that approximately 70% of these question answer pairs are factual correct.
Thirdly, we used StackExchange to extract question-answer pairs, taking the
top-rated question from five categories: Academia, Bioinformatics, Biology,
Fitness, and Health. Additionally, we used a dataset from ChatDoctor
consisting of 200,000 question-answer pairs, available at URL



Limitations
-----------


The model may not perform effectively outside the scope of the medical domain.
The training data primarily targets the knowledge level of medical students,
which may result in limitations when addressing the needs of board-certified physicians.
The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown.
It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.
TAGS
#transformers #medical #text-generation #en #arxiv-2106.09685 #arxiv-2303.14070 #license-cc #endpoints_compatible #has_space #region-us 
 ### Architecture


'medalpaca-lora-30b-8bit' is a large language model specifically fine-tuned for medical domain tasks.
It is based on LLaMA (Large Language Model Meta AI) and contains 7 billion parameters.
The primary goal of this model is to improve question-answering and medical dialogue tasks.
It was trained using LoRA and quantized, to reduce memory footprint. ### Training Data


The training data for this project was sourced from various resources.
Firstly, we used Anki flashcards to automatically generate questions,
from the front of the cards and anwers from the back of the card.
Secondly, we generated medical question-answer pairs from Wikidoc.
We extracted paragraphs with relevant headings, and used Chat-GPT 3.5
to generate questions from the headings and using the corresponding paragraphs
as answers. This dataset is still under development and we believe
that approximately 70% of these question answer pairs are factual correct.
Thirdly, we used StackExchange to extract question-answer pairs, taking the
top-rated question from five categories: Academia, Bioinformatics, Biology,
Fitness, and Health. Additionally, we used a dataset from ChatDoctor
consisting of 200,000 question-answer pairs, available at URL



Limitations
-----------


The model may not perform effectively outside the scope of the medical domain.
The training data primarily targets the knowledge level of medical students,
which may result in limitations when addressing the needs of board-certified physicians.
The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown.
It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.