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README.md

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+---
+license: cc
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- medical
+inference: false
+---
+<!-- header start -->
+<div style="width: 100%;">
+    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
+    </div>
+    <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<!-- header end -->
+
+# medalpaca-13B-GGML
+
+This is GGML format quantised 4-bit, 5-bit and 8-bit GGML models of [Medalpaca 13B](https://huggingface.co/medalpaca/medalpaca-13b).
+
+This repo is the result of quantising to 4-bit, 5-bit and 8-bit GGML for CPU (+CUDA) inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).
+
+## Repositories available
+
+* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/medalpaca-13B-GPTQ-4bit).
+* [4-bit, 5-bit 8-bit GGML models for llama.cpp CPU (+CUDA) inference](https://huggingface.co/TheBloke/medalpaca-13B-GGML).
+* [medalpaca's float32 HF format repo for GPU inference and further conversions](https://huggingface.co/medalpaca/medalpaca-13b).
+
+## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!
+
+llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508
+
+I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them.
+
+For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`.
+
+## Provided files
+| Name | Quant method | Bits | Size | RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+`medalpaca-13B.ggmlv3.q4_0.bin` | q4_0 | 4bit | 8.14GB | 10.5GB | 4-bit. |
+`medalpaca-13B.ggmlv3.q4_1.bin` | q4_1 | 4bit | 8.14GB | 10.5GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
+`medalpaca-13B.ggmlv3.q5_0.bin` | q5_0 | 5bit | 8.95GB | 11.0GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
+`medalpaca-13B.ggmlv3.q5_1.bin` | q5_1 | 5bit | 9.76GB | 12.25GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. |
+`medalpaca-13B.ggmlv3.q8_0.bin` | q8_0 | 8bit | 14.6GB | 17GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. |
+
+## How to run in `llama.cpp`
+
+I use the following command line; adjust for your tastes and needs:
+
+```
+./main -t 8 -m medalpaca-13B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: write a story about llamas ### Response:"
+```
+
+Change `-t 8` to the number of physical CPU cores you have.
+
+## How to run in `text-generation-webui`
+
+GGML models can be loaded into text-generation-webui by installing the llama.cpp module, then placing the ggml model file in a model folder as usual.
+
+Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
+
+Note: at this time text-generation-webui may not support the new May 19th llama.cpp quantisation methods for q4_0, q4_1 and q8_0 files.
+
+
+<!-- footer start -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
+
+## Thanks, and how to contribute.
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
+
+If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
+
+Thank you to all my generous patrons and donaters!
+<!-- footer end -->
+# 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.