Update README.md

README.md

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 ---
-license: other
+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.