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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 use the latest Triton branch of GPTQ-for-LLaMa.

How to easily download and use this model in text-generation-webui

Open the text-generation-webui UI as normal.

Provided files

Two files are provided. The second file will not work unless you use a recent version of the Triton branch of GPTQ-for-LLaMa

Specifically, the second file uses --act-order for maximum quantisation quality and will not work with oobabooga's fork of GPTQ-for-LLaMa. Therefore at this time it will also not work with the CUDA branch of GPTQ-for-LLaMa, or 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
  • Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
  • Works with text-generation-webui one-click-installers
  • Works on Windows
  • Parameters: Groupsize = 128g. No act-order.
  • Command used to create the GPTQ:

    CUDA_VISIBLE_DEVICES=0 python3 llama.py medalpaca-13b c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors
    

• medalpaca-13B-GPTQ-4bit-128g.safetensors
  • Only works with the latest Triton branch of GPTQ-for-LLaMa
  • Does not work with text-generation-webui one-click-installers
  • Does not work on Windows
  • Parameters: Groupsize = 128g. act-order.
  • Offers highest quality quantisation, but requires recent GPTQ-for-LLaMa code
  • Command used to create the GPTQ:

    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 act-order safetensors file and need to update the Triton branch of 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:

# Clone text-generation-webui, if you don't already have it
git clone https://github.com/oobabooga/text-generation-webui
# Make a repositories directory
mkdir text-generation-webui/repositories
cd text-generation-webui/repositories
# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui
git clone https://github.com/qwopqwop200/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 medalpaca-13B-GPTQ-4bit --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 can't update GPTQ-for-LLaMa to the latest Triton branch, or don't want to, you can use medalpaca-13B-GPTQ-4bit-128g.no-act-order.safetensors as mentioned above, which should work without any upgrades to text-generation-webui.

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Thanks, and how to contribute.

Thanks to the chirper.ai team!

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Original model card: MedAlpaca 13b

Table of Contents

Model Description

• Architecture
• Training Data Model Usage 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 . 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 .

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.