GGUF
Inference Endpoints

Multilingual Medicine: Model, Dataset, Benchmark, Code

Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far

πŸ‘¨πŸ»β€πŸ’»Github β€’πŸ“ƒ Paper β€’ 🌐 Demo β€’ πŸ€— ApolloCorpus β€’ πŸ€— XMedBench
δΈ­ζ–‡ | English

Apollo

🌈 Update

  • [2024.03.07] Paper released.
  • [2024.02.12] ApolloCorpus and XMedBench is publishedοΌπŸŽ‰
  • [2024.01.23] Apollo repo is publishedοΌπŸŽ‰

Results

πŸ€—Apollo-0.5B β€’ πŸ€— Apollo-1.8B β€’ πŸ€— Apollo-2B β€’ πŸ€— Apollo-6B β€’ πŸ€— Apollo-7B

πŸ€— Apollo-0.5B-GGUF β€’ πŸ€— Apollo-2B-GGUF β€’ πŸ€— Apollo-6B-GGUF β€’ πŸ€— Apollo-7B-GGUF

Apollo

Dataset & Evaluation

  • Dataset πŸ€— ApolloCorpus

    Click to expand

    Apollo

    • 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",
                ...
              ],
              ...
            ]
            
      • 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",
                ...
              ],
              ...
            ]
          
  • Evaluation πŸ€— 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

Results reproduction

Click to expand

Waiting for Update

Citation

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}
}
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