---
license: cc
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- medical
---

# medalpaca-13B GPTQ 4bit

This is a [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) 4bit quantisation of [medalpaca-13b](https://huggingface.co/medalpaca/medalpaca-13b).

## GIBBERISH OUTPUT IN `text-generation-webui`?

Please read the Provided Files section below. You should use `medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors` unless you are able to update GPTQ-for-LLaMa.

## Provided files

Two files are provided. 

**The second file will not work unless you use recent GPTQ-for-LLaMa code**

Specifically, the file that uses act-order it will not work with oobabooga's fork of GPTQ-for-LLaMa and therefore it will not work with `text-generation-webui` one-click installers.

Unless you are able to use the latest GPTQ-for-LLaMa code, please use `medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors`

* `medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors`
  * Created with the latest GPTQ-for-LLaMa code
  * Parameters: Groupsize = 128g. No act-order.
  * Command: `CUDA_VISIBLE_DEVICES=0 python3 llama.py medalpaca-13b c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors
* `medalpaca-13B-GPTQ-4bit-128g.safetensors`
  * Created with the latest GPTQ-for-LLaMa code
  * Parameters: Groupsize = 128g. act-order.
  * Offers highest quality quantisation, but requires recent GPTQ-for-LLaMa code
  * Command: `CUDA_VISIBLE_DEVICES=0 python3 llama.py medalpaca-13b c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors medalpaca-13B-GPTQ-4bit-128g.safetensors

## How to run in `text-generation-webui`

File `medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors` can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui).

[Instructions on using GPTQ 4bit files in text-generation-webui are here](https://github.com/oobabooga/text-generation-webui/wiki/GPTQ-models-\(4-bit-mode\)).

The other `safetensors` model file was created with the latest GPTQ code, and uses `--act-order` to give the maximum possible quantisation quality, but this means it requires that the latest GPTQ-for-LLaMa is used inside the UI.

If you want to use the `safetensors` file and need to update GPTQ-for-LLaMa, here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI:
```
# We need to clone GPTQ-for-LLaMa as of April 13th, due to breaking changes in more recent commits
git clone -n  https://github.com/qwopqwop200/GPTQ-for-LLaMa gptq-safe
cd gptq-safe && git checkout 58c8ab4c7aaccc50f507fd08cce941976affe5e0

# Now clone text-generation-webui, if you don't already have it
git clone https://github.com/oobabooga/text-generation-webui
# And link GPTQ-for-Llama into text-generation-webui
mkdir -p text-generation-webui/repositories
ln -s gptq-safe text-generation-webui/repositories/GPTQ-for-LLaMa
```

Then install this model into `text-generation-webui/models` and launch the UI as follows:
```
cd text-generation-webui
python server.py --model vicuna-13B-1.1-GPTQ-4bit-128g --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want
```

The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information.

If you are on Windows, or cannot use the Triton branch of GPTQ for any other reason, you can try the CUDA branch instead:
```
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa -b cuda
cd GPTQ-for-LLaMa
python setup_cuda.py install
```
Then link that into `text-generation-webui/repositories` as described above.

However I have heard reports that the CUDA code may run quite slow.

Or just use `medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors` as mentioned above, which should work without any upgrades to text-generation-webui.

# Original model card: MedAlpaca 13b


## Table of Contents

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

## Model Description
### Architecture
`medalpaca-13b` is a large language model specifically fine-tuned for medical domain tasks. 
It is based on LLaMA (Large Language Model Meta AI) and contains 13 billion parameters. 
The primary goal of this model is to improve question-answering and medical dialogue tasks.

### 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   |

## Model Usage
To evaluate the performance of the model on a specific dataset, you can use the Hugging Face Transformers library's built-in evaluation scripts. Please refer to the evaluation guide for more information.
Inference

You can use the model for inference tasks like question-answering and medical dialogues using the Hugging Face Transformers library. Here's an example of how to use the model for a question-answering task:

```python

from transformers import pipeline

qa_pipeline = pipeline("question-answering", model="medalpaca/medalpaca-7b", tokenizer="medalpaca/medalpaca-7b")
question = "What are the symptoms of diabetes?"
context = "Diabetes is a metabolic disease that causes high blood sugar. The symptoms include increased thirst, frequent urination, and unexplained weight loss."
answer = qa_pipeline({"question": question, "context": context})
print(answer)
```

## 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.