Democratizing Medical LLMs For Much More Languages
Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38 Minor Languages So far.
📃 Paper • 🌐 Demo • 🤗 ApolloMoEDataset • 🤗 ApolloMoEBench • 🤗 Models •🌐 Apollo • 🌐 ApolloMoE
🌈 Update
- [2024.10.15] ApolloMoE repo is published!🎉
Languages Coverage
12 Major Languages and 38 Minor Languages
Architecture
Results
Dense
🤗 Apollo2-0.5B • 🤗 Apollo2-1.5B • 🤗 Apollo2-2B
🤗 Apollo2-3.8B • 🤗 Apollo2-7B • 🤗 Apollo2-9B
Post-MoE
🤗 Apollo-MoE-0.5B • 🤗 Apollo-MoE-1.5B • 🤗 Apollo-MoE-7B
Usage Format
Apollo2
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
- 2B, 9B: User:{query}\nAssistant:{response}<eos>
- 3.8B: <|user|>\n{query}<|end|><|assisitant|>\n{response}<|end|>
Apollo-MoE
- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
Dataset & Evaluation
Dataset 🤗 ApolloMoEDataset
Evaluation 🤗 ApolloMoEBench
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
- [MMLU_FR]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
HI: MMLU_HI
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
AR: MMLU_AR
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
JA: IgakuQA
KO: KorMedMCQA
IT:
- MedExpQA
- [MMLU_IT]
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
DE: BioInstructQA: German part
PT: BioInstructQA: Portuguese part
RU: RuMedBench
Results reproduction
Click to expand
We take Apollo2-7B or Apollo-MoE-0.5B as example
Download Dataset for project:
bash 0.download_data.sh
Prepare test and dev data for specific model:
- Create test data for with special token
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 ./src/sft/training_config/zero_multi.yaml
bash 3.single_node_train.sh
Evaluate your model: Generate score for benchmark
bash 4.eval.sh
Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
@misc{zheng2024efficientlydemocratizingmedicalllms,
title={Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts},
author={Guorui Zheng and Xidong Wang and Juhao Liang and Nuo Chen and Yuping Zheng and Benyou Wang},
year={2024},
eprint={2410.10626},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.10626},
}
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