license: cc
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- medical
medalpaca-13B GPTQ 4bit
This is a GPTQ-for-LLaMa 4bit quantisation of 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.
Instructions on using GPTQ 4bit files in text-generation-webui are here.
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
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. 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 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:
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.