Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
📃 Paper • 🌐 Demo • 🤗 ApolloCorpus • 🤗 XMedBench • 🌐 ApolloMoE
中文 | English
- [2024.10.15] ApolloMoE repo released, covering 50 Languages.
- [2024.04.25] MedJamba released, train and evaluation code refer to repo.
- [2024.03.07] Paper released.
- [2024.02.12] ApolloCorpus and XMedBench is published!🎉
- [2024.01.23] Apollo repo is published!🎉
🤗 Apollo-0.5B • 🤗 Apollo-1.8B • 🤗 Apollo-2B • 🤗 Apollo-6B • 🤗 Apollo-7B • 🤗 Apollo-34B • 🤗 Apollo-72B
🤗 MedJamba
🤗 Apollo-0.5B-GGUF • 🤗 Apollo-2B-GGUF • 🤗 Apollo-6B-GGUF • 🤗 Apollo-7B-GGUF
- 0.5B, 1.8B, 2B, 6B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
- 34B, 72B: <|User|>:{query}\n<|Assistant|>:{response}<|endoftext|>
-
Dataset 🤗 ApolloCorpus
Click to expand
- Zip File
- Data category
- Pretrain:
- data item:
- json_name: {data_source}{language}{data_type}.json
- data_type: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki
- language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)
- data_type: qa(generated qa from text)
- data_type==text: list of string
[ "string1", "string2", ... ]
- data_type==qa: list of qa pairs(list of string)
[ [ "q1", "a1", "q2", "a2", ... ], ... ]
- data item:
- SFT:
- json_name: {data_source}_{language}.json
- data_type: code, general, math, medicalExam, medicalPatient
- data item: list of qa pairs(list of string)
[ [ "q1", "a1", "q2", "a2", ... ], ... ]
- Pretrain:
-
Evaluation 🤗 XMedBench
Click to expand
-
EN:
- MedQA-USMLE
- MedMCQA
- PubMedQA: Because the results fluctuated too much, they were not used in the paper.
- MMLU-Medical
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
-
ZH:
- MedQA-MCMLE
- CMB-single: Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- CMMLU-Medical
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- CExam: Not used in the paper
- Randomly sample 2,000 multiple-choice questions
-
ES: Head_qa
-
FR: Frenchmedmcqa
-
HI: MMLU_HI
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
-
AR: MMLU_Ara
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
-
Click to expand
We take Gemma-2b as example
-
Download Dataset for project:
bash 0.download_data.sh
-
Prepare test and dev for specific model:
- Create test data for with special token, you can use ./util/check.ipynb to check models' special tokens
bash 1.data_process_test&dev.sh
-
Prepare train data for specific model (Create tokenized data in advance):
- You can adjust data Training order and Training Epoch in this step
bash 2.data_process_train.sh
-
Train the model
- If you want to train in Multi Nodes please refer to ./scripts/multi_node_train_*.sh
bash 3.single_node_train_gemma.sh
-
(Optional) Proxy-Tuning: Directly improve model capabilities without fine-tuning
bash src/proxy-tuning/scripts/eval/proxy_tuning.sh
-
Evaluate your model: Generate score for benchmark
bash 4.eval.sh
-
Evaluate your model: Play with your ckpts in bash
python ./src/evaluate/cli_demo.py --model_name='./ckpts/your/path/tfmr'
Please use the following citation if you intend to use our dataset for training or evaluation:
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}