Upload folder using huggingface_hub

.chainlit/config.toml

@@ -0,0 +1,39 @@
+[project]
+# If true (default), the app will be available to anonymous users.
+# If false, users will need to authenticate and be part of the project to use the app.
+public = true
+
+# The project ID (found on https://cloud.chainlit.io).
+# The project ID is required when public is set to false or when using the cloud database.
+#id = ""
+
+# Uncomment if you want to persist the chats.
+# local will create a database in your .chainlit directory (requires node.js installed).
+# cloud will use the Chainlit cloud database.
+# custom will load use your custom client.
+# database = "local"
+
+# Whether to enable telemetry (default: true). No personal data is collected.
+enable_telemetry = true
+
+# List of environment variables to be provided by each user to use the app.
+user_env = []
+
+[UI]
+# Name of the app and chatbot.
+name = "Chatbot"
+
+# Description of the app and chatbot. This is used for HTML tags.
+# description = ""
+
+# The default value for the expand messages settings.
+default_expand_messages = false
+
+# Hide the chain of thought details from the user in the UI.
+hide_cot = false
+
+# Link to your github repo. This will add a github button in the UI's header.
+# github = ""
+
+[meta]
+generated_by = "0.5.1"

.gitattributes

@@ -33,3 +33,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data_clean/questions/Mainland/chinese_qbank.jsonl filter=lfs diff=lfs merge=lfs -text
+data_clean/questions/Mainland/train.jsonl filter=lfs diff=lfs merge=lfs -text
+data_clean/questions/US/4_options/phrases_no_exclude_train.jsonl filter=lfs diff=lfs merge=lfs -text
+data_clean/questions/US/US_qbank.jsonl filter=lfs diff=lfs merge=lfs -text
+data_clean/questions/US/metamap_extracted_phrases/train/phrases_train.jsonl filter=lfs diff=lfs merge=lfs -text
+data_clean/textbooks/en/InternalMed_Harrison.txt filter=lfs diff=lfs merge=lfs -text
+data_clean/textbooks/en/Surgery_Schwartz.txt filter=lfs diff=lfs merge=lfs -text
+data_clean/textbooks/zh_paragraph/all_books.txt filter=lfs diff=lfs merge=lfs -text
+data_clean/textbooks/zh_sentence/all_books.txt filter=lfs diff=lfs merge=lfs -text
+db/test/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# experimental notebooks
+*.ipynb 
+
+# large files
+data/
+models/
+scheduler/
+eval/outputs
+lora*
+wandb
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+download.txt
+
+# Pyre type checker
+.pyre/
+.DS_Store
+.vscode/

DATA_DESCRIPTION.md

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+# Medical Meadow
+
+## Overview
+*Medical Meadow* is a collection of medical datasets, some were explicitly created by us for this project, some are established medical datasets. If you use the *Medicl Meadow* for your research, please cite all datasets accordingly.
+Currently the following datasets are part of our meadow.
+
+### Self Created Datasets
+These datasets were created by us for this project. Please cite our paper if you should use them. 
+
+```Citation
+Citation TBA
+```
+
+| Dataset                       | n       |
+|-------------------------------|---------|
+| Anki Flashcards               | 33,955  |
+| Stack Exchange Academia       | 39,633  |
+| Stack Exchange Biology        | 7,482   |
+| Stack Exchange Fitness        | 3,026   |
+| Stack Exchange Health         | 1,428   |
+| Stack Exchange Bioinformatics | 906     |
+| Wikidoc Living Textbook       | 67,704  |
+| Wikidoc Patient Information   | 5,942   |
+| USMLE Self Assessment Step 1  | 119     |
+| USMLE Self Assessment Step 2  | 120     |
+| USMLE Self Assessment Step 3  | 135     |
+
+#### Anki Flashcards
+Medicine as a whole encompasses a wide range of subjects that medical students and graduates must master in order to practice effectively. This includes a deep understanding of basic medical sciences, clinical knowledge, and clinical skills. The Anki Medical Curriculum flashcards are created and updated by medical students and cover the entirety of this curriculum, addressing subjects such as anatomy, physiology, pathology, pharmacology, and more. These flashcards frequently feature succinct summaries and mnemonics to aid in learning and retention of vital medical concepts.
+
+In our study, we employed the flashcards as a resource for generating question-answer pairs for training purposes. After removing cards that contained images, we utilized OpenAI's GPT-3.5-turbo to rephrase the cards into coherent, contextually relevant question-answer pairs. In general the questions and answers are short and focused, as the flashcards do not allow to add much information.
+
+#### StackExchange 
+Our dataset consists of 52,475 question-answer pairs obtained from five Stack Exchange forums related to biomedical sciences and related fields. Specifically, we gathered the data from answers that head at least five upvotes in the forums concering Academia, Bioinformatics, Biology, Fitness, and Health, and matched them to the corresponding question. You can find the full Stack Exchange dataset, where the five different categories can be extraxted from, [here](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences).
+
+#### WikiDoc
+We incorporated medical question-answer pairs extracted from [WikiDoc](https://www.wikidoc.org/index.php/Main_Page), a collaborative platform for medical professionals to share and contribute to up-to-date medical knowledge. The platform has to main subsites, the "Living Textbook" and "Patient Information". The "Living Textbook" contains chapters for various medical specialties, which we crawled. We then used GTP-3.5-Turbo to rephrase the paragraph heading to a question and used the paragraph as answer.  Patient Information is structured differently, in that each section subheading is already a question, making rephrasing them obsolete.  
+**Note:** This dataset is still a WIP. While the Q/A pairs from the patient information seems to be mostly correct, the conversion using GPT-3.5-Turbo yielded some unsatisfactory results in  approximately 30% of cases. We are in the process of cleaning this dataset. 
+
+#### USMLE Self assessment
+The USMLE (United States Medical Licensing Examination) provides a set of self assessment questions for medical students to evaluate, if they are likels to pass the Exam. For each of the three steps, they provide about 120 questions, which is equal to approximately half the amount of questions a student needs to answer during the real exam. As these questions provide a realsitic knowledge benchmark, covering all relevant medical areas, we use them explicitly for testing our models. 
+
+#### Wikipedia
+We have crawled all medical articles on the German and English Wikipedia and are currently in the process of converting them to Q/A pairs. 
+
+
+### External Datasets
+This is a collection of open medical datasets. Please cite the respective source, if you use them in your reserach. 
+
+| Dataset              | n       | link/citation                                                                     |
+|----------------------|---------|-----------------------------------------------------------------------------------|
+| MEDIQA               | 2208    | https://osf.io/fyg46/?view_only=                                                  |
+| CORD-19              | 1056660 | https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge |
+| MMMLU                | 3787    | https://github.com/hendrycks/test                                                 |
+| MedQA                | 10178   | https://github.com/jind11/MedQA                                                   |
+| Pubmed Health Advice | 8676    | https://aclanthology.org/D19-1473/                                                |
+| Pubmed Causal        | 2446    | https://aclanthology.org/2020.coling-main.427/                                    |
+| ChatDoctor           | 215000  | https://github.com/Kent0n-Li/ChatDoctor                                           |
+
+
+#### MEDIQA
+MEDIQA is a dataset of manually generated, question-driven summaries of multi and single document answers to consumer health questions. 
+It is available [here](https://osf.io/fyg46/?view_only=) or, if you want to use our slightly processed version [here](https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa) 
+**Citation:**
+```
+@article{savery2020question,
+  title={Question-driven summarization of answers to consumer health questions},
+  author={Savery, Max and Abacha, Asma Ben and Gayen, Soumya and Demner-Fushman, Dina},
+  journal={Scientific Data},
+  volume={7},
+  number={1},
+  pages={322},
+  year={2020},
+  publisher={Nature Publishing Group UK London}
+}
+
+```
+
+#### COVID-19 Open Research Dataset Challenge (CORD-19)
+In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 1,000,000 scholarly articles, including over 400,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. 
+The dataset is available [here](https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge) or, if you want to use our slightly processed version [here](https://huggingface.co/datasets/medalpaca/medical_meadow_cord19). 
+**Citation:**
+```
+@inproceedings{wang-etal-2020-cord,
+    title = "{CORD-19}: The {COVID-19} Open Research Dataset",
+    author = "Wang, Lucy Lu  and Lo, Kyle  and Chandrasekhar, Yoganand  and Reas, Russell  and Yang, Jiangjiang  and Burdick, Doug  and Eide, Darrin  and Funk, Kathryn  and Katsis, Yannis  and Kinney, Rodney Michael  and Li, Yunyao  and Liu, Ziyang  and Merrill, William  and Mooney, Paul  and Murdick, Dewey A.  and Rishi, Devvret  and Sheehan, Jerry  and Shen, Zhihong  and Stilson, Brandon  and Wade, Alex D.  and Wang, Kuansan  and Wang, Nancy Xin Ru  and Wilhelm, Christopher  and Xie, Boya  and Raymond, Douglas M.  and Weld, Daniel S.  and Etzioni, Oren  and Kohlmeier, Sebastian",
+    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1"
+}
+```
+
+#### Measuring Massive Multitask Language Understanding - Test Set
+This is the test set for the Measuring Massive Multitask Language Understanding, which can be downloaded [here](https://people.eecs.berkeley.edu/~hendrycks/data.tar). We also provide a prepared version of this data set [here](https://huggingface.co/datasets/medalpaca/medical_meadow_mmmlu). 
+
+**Citation:**
+```
+@article{hendryckstest2021,
+  title={Measuring Massive Multitask Language Understanding},
+  author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
+  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
+  year={2021}
+}
+
+@article{hendrycks2021ethics,
+  title={Aligning AI With Shared Human Values},
+  author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
+  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
+  year={2021}
+}
+```
+#### MedQA
+This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams." 
+
+From https://github.com/jind11/MedQA:
+>The data that contains both the QAs and textbooks can be downloaded from [this google drive folder](https://drive.google.com/file/d/1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw/view?usp=sharing). A bit of details of data are explained as below:
+>
+> For QAs, we have three sources: US, Mainland of China, and Taiwan District, which are put in folders, respectively. All files for QAs are in jsonl file format, where each line is a data sample as a dict. The "XX_qbank.jsonl" files contain all data samples while we also provide an official random split into train, dev, and test sets. Those files in the "metamap" folders are extracted medical related phrases using the Metamap tool.
+>
+> For QAs, we also include the "4_options" version in for US and Mainland of China since we reported results for 4 options in the paper.
+>
+> For textbooks, we have two languages: English and simplified Chinese. For simplified Chinese, we provide two kinds of sentence spliting: one is split by sentences, and the other is split by paragraphs.
+
+If you would like to use the data, please cite the paper. The prepared dataset is available [here](https://huggingface.co/datasets/medalpaca/medical_meadow_medqa)
+
+**Citation:**
+```
+@article{jin2020disease,
+  title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
+  author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
+  journal={arXiv preprint arXiv:2009.13081},
+  year={2020}
+}
+```
+
+####  PubMed Health Advice
+This is the dataset use in the paper: Detecting Causal Language Use in Science Findings. 
+The prepared dataset is available [here](https://huggingface.co/datasets/medalpaca/medical_meadow_health_advice)
+
+**Citation:**
+
+```
+@inproceedings{yu-etal-2019-detecting,
+    title = "Detecting Causal Language Use in Science Findings",
+    author = "Yu, Bei  and
+      Li, Yingya  and
+      Wang, Jun",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D19-1473",
+    doi = "10.18653/v1/D19-1473",
+    pages = "4664--4674",
+}
+```
+#### Pubmed Causal
+This is the dataset used in the paper: Detecting Causal Language Use in Science Findings.
+The prepared dataset is available [here](https://huggingface.co/datasets/medalpaca/medical_meadow_pubmed_causal)
+
+**Citation:**
+```
+@inproceedings{yu-etal-2019-detecting,
+    title = "Detecting Causal Language Use in Science Findings",
+    author = "Yu, Bei  and
+      Li, Yingya  and
+      Wang, Jun",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D19-1473",
+    doi = "10.18653/v1/D19-1473",
+    pages = "4664--4674",
+}
+
+```
+#### ChatDoctor 
+Dataset used in the Paper: "ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge". All data is available [here](https://github.com/Kent0n-Li/ChatDoctor). As it is already in the right format, we provide no prepared version.
+
+Please cite the paper, if you use this dataset. 
+
+**Citation:**
+
+```
+@misc{yunxiang2023chatdoctor,
+      title={ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge}, 
+      author={Li, Yunxiang and Li, Zihan and Zhang, Kai and Dan, Ruilong and Zhang, You},
+      year={2023},
+      eprint={2303.14070},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+#### OpenAssistant Datset
+Although, this is not a medical dataste per se, it is a high quality instruction dataset covering multiple languages and was thus also included into our dataset. At this timepoint, the dataset is still in gated access, so we cannot link it directly. Please refer to https://huggingface.co/OpenAssistant, where it will likely be published, once final. 
+
+## Acessing the data
+We have parsed all dataset to a uniform format and uploaded them to the hugginface repository. With them you can curate your very own version of the **Medical Meadow** tailored to your specific needs. Check out the data at https://huggingface.co/medalpaca

LICENSE

@@ -0,0 +1,674 @@
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README.md

@@ -0,0 +1,156 @@
+![medalpaca](https://user-images.githubusercontent.com/37253540/228315829-b22f793c-2dcd-4c03-a32d-43720085a7de.png)
+
+# medAlpaca: Finetuned Large Language Models for Medical Question Answering
+
+## Project Overview
+MedAlpaca expands upon both [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and 
+[AlpacaLoRA](https://github.com/tloen/alpaca-lora) to offer an advanced suite of large language 
+models specifically fine-tuned for medical question-answering and dialogue applications. 
+Our primary objective is to deliver an array of open-source language models, paving the way for 
+seamless development of medical chatbot solutions.
+
+These models have been trained using a variety of medical texts, encompassing resources such as 
+medical flashcards, wikis, and dialogue datasets. For more details on the data utilized, please consult the data section. 
+
+## Getting Started
+Create a new virtual environment, e.g. with conda
+
+```bash
+conda create -n medalpaca python>=3.9
+```
+
+Install the required packages:
+```bash
+pip install -r requirements.txt
+```
+
+## Training of medAlpaca
+<img width="256" alt="training your alpaca" src="https://user-images.githubusercontent.com/37253540/229250535-98f28e1c-0a8e-46e7-9e61-aeb98ef115cc.png">
+
+### Memory Requirements
+We have benchmarked the needed GPU memory as well as the approximate duration per epoch 
+for finetuning LLaMA 7b on the Medical Meadow small dataset (~6000 Q/A pairs) on a single GPU:
+
+
+| Model    | 8bit trainig | LoRA  | fp16  | bf16  | VRAM Used | Gradient cktp | Duration/epoch |
+|----------|--------------|-------|-------|-------|-----------|---------------|----------------|
+| LLaMA 7b | True         | True  | True  | False | 8.9 GB    | False         | 77:30          |
+| LLaMA 7b | False        | True  | True  | False | 18.8 GB   | False         | 14:30          |
+| LLaMA 7b | False        | False | True  | False | OOM       | False         | -              | 
+| LLaMA 7b | False        | False | False | True  | 79.5 GB   | True          | 35:30          |
+| LLaMA 7b | False        | False | False | False | OOM       | True          | -              |
+
+### Train medAlpaca based on LLaMA 
+If you have access to the [LLaMA](https://arxiv.org/abs/2302.13971) or [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) 
+weights you can finetune the model with the following command. 
+Just replace `<PATH_TO_LLAMA_WEIGHTS>` with the folder containing you LLaMA or Alpaca weights. 
+
+```bash
+python medalpaca/train.py \
+    --model PATH_TO_LLAMA_WEIGHTS \
+    --data_path medical_meadow_small.json \
+    --output_dir 'output' \
+    --train_in_8bit True \  
+    --bf16 True \
+    --tf32 False \
+    --fp16 False \
+    --global_batch_size 128 \
+    --per_device_batch_size 8 \
+```
+Per default the script performs mixed precision training.  
+You can toggle 8bit training with the `train_in_8bit` flag. 
+While 8 bit training currently only works with `use_lora True`, however you can use
+LoRA without 8 bit training. 
+It is also able to train other models such as `facebook/opt-6.7` with the above script. 
+
+## Data
+<img width="256" alt="Screenshot 2023-03-31 at 09 37 41" src="https://user-images.githubusercontent.com/37253540/229244284-72b00e82-0da1-4218-b08e-63864306631e.png">
+
+To ensure your cherished llamas and alpacas are well-fed and thriving, 
+we have diligently gathered high-quality biomedical open-source datasets 
+and transformed them into instruction tuning formats. 
+We have dubbed this endeavor **Medical Meadow**. 
+Medical Meadow currently encompasses roughly 1.5 million data points across a diverse range of tasks, 
+including openly curated medical data transformed into Q/A pairs with OpenAI's `gpt-3.5-turbo`
+and a collection of established NLP tasks in the medical domain. 
+Please note, that not all data is of the same quantitiy and quality and you may need tp subsample 
+the data for training your own model. 
+We will persistently update and refine the dataset, and we welcome everyone to contribute more 'grass' to Medical Meadow!
+
+### Data Overview
+
+| Name                 |  Source                                                                 |  n       |  n included in training |
+|----------------------|-------------------------------------------------------------------------|----------|-------------------------|
+| Medical Flashcards   |  [medalpaca/medical_meadow_medical_flashcards](https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards)  |  33955  |  33955                 |
+| Wikidoc              |  [medalpaca/medical_meadow_wikidoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)    |  67704  |  10000                 |
+| Wikidoc Patient Information | [medalpaca/medical_meadow_wikidoc_patient_information](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information)    |  5942 |  5942 |
+| Stackexchange academia |  [medalpaca/medical_meadow_stack_exchange](https://huggingface.co/medalpaca/datasets/medalpaca/medical_meadow_stackexchange)    |  40865  |  40865                 |
+| Stackexchange biology |  [medalpaca/medical_meadow_stack_exchange](https://huggingface.co/medalpaca/datasets/medalpaca/medical_meadow_stackexchange)    |  27887  |  27887                 |
+| Stackexchange fitness |  [medalpaca/medical_meadow_stack_exchange](https://huggingface.co/medalpaca/datasets/medalpaca/medical_meadow_stackexchange)    |  9833  | 9833                 |
+| Stackexchange health |  [medalpaca/medical_meadow_stack_exchange](https://huggingface.co/medalpaca/datasets/medalpaca/medical_meadow_stackexchange)    |  7721  |  7721                 |
+| Stackexchange bioinformatics |  [medalpaca/medical_meadow_stack_exchange](https://huggingface.co/datasets/medalpaca/medical_meadow_stackexchange)    |  5407  |  5407                |
+| USMLE Self Assessment Step 1 |  [medalpaca/medical_meadow_usmle_self](https://huggingface.co/datasets/medalpaca/medical_meadow_usmle_self_assessment)    |  119  |  92 (test only)              |
+| USMLE Self Assessment Step 2 |  [medalpaca/medical_meadow_usmle_self](https://huggingface.co/datasets/medalpaca/medical_meadow_usmle_self_assessment)    |  120  |  110  (test only)              |
+| USMLE Self Assessment Step 3 |  [medalpaca/medical_meadow_usmle_self](https://huggingface.co/datasets/medalpaca/medical_meadow_usmle_self_assessment)    |  135  |  122  (test only)             |
+| MEDIQA               | [original](https://osf.io/fyg46/?view_only=), [preprocessed](https://huggingface.co/datasets/medalpaca/medical_meadow_mediqa) |  2208    |  2208 |
+| CORD-19              | [original](https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge ), [preprocessed](https://huggingface.co/datasets/medalpaca/medical_meadow_cord19) |  1056660    |  50000 |
+| MMMLU               | [original](https://github.com/hendrycks/test), [preprocessed](https://huggingface.co/datasets/medalpaca/medical_meadow_mmmlu) |  3787    |  3787 |
+| Pubmed Health Advice | [original](https://aclanthology.org/D19-1473/), [preprocessed](vhuggingface.co/datasets/medalpaca/health_advice) |  10178    |  10178 |
+| Pubmed Causal               | [original](https://aclanthology.org/2020.coling-main.427/    ), [preprocessed](https://huggingface.co/datasets/medalpaca/medical_meadow_pubmed_causal) |  2446    |  2446 |
+| ChatDoctor               | [original](https://github.com/Kent0n-Li/ChatDoctor  ) |  215000    |  10000 |
+| OpenAssistant | [original](https://huggingface.co/OpenAssistant) |  9209   | 9209     |
+
+
+### Data description
+please refer to [DATA_DESCRIPTION.md](DATA_DESCRIPTION.md)
+
+
+## Benchmarks
+<img width="256" alt="benchmarks" src="https://user-images.githubusercontent.com/37253540/229249302-20ff8a88-95b4-42a3-bdd8-96a9dce9a92b.png">
+
+We are benchmarking all models on the USMLE self assessment, which is available at this [link](https://www.usmle.org/prepare-your-exam).
+Note, that we removed all questions with images, as our models are not multimodal. 
+
+| **Model**                                                                                  | **Step1**         | **Step2**        | **Step3**        |
+|--------------------------------------------------------------------------------------------|-------------------|------------------|------------------|
+| [LLaMA 7b](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)               | 0.198             | 0.202            | 0.203            |
+| [Alpaca 7b naive](https://github.com/tatsu-lab/stanford_alpaca) ([weights](https://huggingface.co/chavinlo/alpaca-native)) | 0.275             | 0.266            | 0.293            |
+| [Alpaca 7b LoRA](https://github.com/tloen/alpaca-lora)                                     | 0.220             | 0.138            | 0.252            |
+| [MedAlpaca 7b](https://huggingface.co/medalpaca/medalpaca-7b)                              | 0.297             | 0.312            | 0.398            |
+| [MedAlpaca 7b LoRA](https://huggingface.co/medalpaca/medalpaca/medalpaca-lora-7b-16bit)    | 0.231             | 0.202            | 0.179            |
+| [MedAlpaca 7b LoRA 8bit](https://huggingface.co/medalpaca/medalpaca-lora-7b-8bit)          | 0.231             | 0.241            | 0.211            |
+| [ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor) (7b)                                 | 0.187             | 0.185            | 0.148            |
+| [LLaMA 13b](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)              | 0.222             | 0.248            | 0.276            |
+| [Alpaca 13b naive](https://huggingface.co/chavinlo/alpaca-13b)                             | 0.319             | 0.312            | 0.301            |
+| [MedAlpaca 13b](https://huggingface.co/medalpaca/medalpaca-13b)                            | ***0.473***       | ***0.477***      | ***0.602***      |  
+| [MedAlpaca 13b LoRA](https://huggingface.co/medalpaca/medalpaca/medalpaca-lora-13b-16bit)  | 0.250             | 0.255            | 0.255            |
+| [MedAlpaca 13b LoRA 8bit](https://huggingface.co/medalpaca/medalpaca-lora-13b-8bit)        | 0.189             | 0.303            | 0.289            |
+| [MedAlpaca 30b](https://huggingface.co/medalpaca/medalpaca-30b) (still training)           | TBA               | TBA              | TBA              |  
+| [MedAlpaca 30b LoRA 8bit](https://huggingface.co/medalpaca/medalpaca-lora-30b-8bit)        | 0.315             | 0.327            | 0.361            |+
+
+We are continuously working on improving the training as well as our evaluation prompts. 
+Expect this table to change quite a bit. 
+
+
+## Access the models
+Visit the zoo and have a look at our alpacas here: https://huggingface.co/medalpaca
+
+It should be obvious, but the models provided on this platform are shared for research purposes 
+only and should not be used in any healthcare applications or settings. 
+While we are excited to showcase our experimental models, please be aware that they have not undergone 
+extensive testing or validation, and their reliability cannot be guaranteed. 
+We kindly ask you to exercise caution when using these models, 
+and we appreciate your understanding as we continue to explore and develop this innovative technology.
+
+
+## Paper
+<img width="256" alt="chat-lama" src="https://user-images.githubusercontent.com/37253540/229261366-5cce9a60-176a-471b-80fd-ba390539da72.png">
+
+```
+@article{han2023medalpaca,
+  title={MedAlpaca--An Open-Source Collection of Medical Conversational AI Models and Training Data},
+  author={Han, Tianyu and Adams, Lisa C and Papaioannou, Jens-Michalis and Grundmann, Paul and Oberhauser, Tom and L{\"o}ser, Alexander and Truhn, Daniel and Bressem, Keno K},
+  journal={arXiv preprint arXiv:2304.08247},
+  year={2023}
+}
+```