README.md

metadata

license: apache-2.0
datasets:
  - FreedomIntelligence/ApolloMoEDataset
language:
  - ar
  - en
  - zh
  - ko
  - ja
  - mn
  - th
  - vi
  - lo
  - mg
  - de
  - pt
  - es
  - fr
  - ru
  - it
  - hr
  - gl
  - cs
  - co
  - la
  - uk
  - bs
  - bg
  - eo
  - sq
  - da
  - sa
  - 'no'
  - gn
  - sr
  - sk
  - gd
  - lb
  - hi
  - ku
  - mt
  - he
  - ln
  - bm
  - sw
  - ig
  - rw
  - ha
metrics:
  - accuracy
base_model:
  - google/gemma-2-2b
pipeline_tag: question-answering
tags:
  - biology
  - medical

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！🎉

Architecture

Click to view the MoE routing image

Results

Dense

🤗 Apollo2-0.5B • 🤗 Apollo2-1.5B • 🤗 Apollo2-2B • 🤗 Apollo2-3.8B • 🤗 Apollo2-7B • 🤗 Apollo2-9B

Click to view the Dense Models Results

Post-MoE

🤗 Apollo-MoE-0.5B • 🤗 Apollo-MoE-1.5B • 🤗 Apollo-MoE-7B

Click to view the Post-MoE Models Results

​

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

  Click to expand

  

  • Data category

• 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 Gemma-2b as example

1. Download Dataset for project:

   bash 0.download_data.sh
   

2. 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
   

3. 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
   

4. 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
   

5. Evaluate your model: Generate score for benchmark

   bash 4.eval.sh
   

6. Evaluate your model: Play with your ckpts in bash

   python ./src/evaluate/cli_demo.py --model_name='./ckpts/your/path/tfmr'
   

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}, 
}