nicoboss/Apollo2-9B-llamacppfixed

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.

<p align="center"> 📃 <a href="https://arxiv.org/abs/2410.10626" target="blank">Paper</a> • 🌐 <a href="" target="blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="blank">ApolloMoEDataset</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="blank">ApolloMoEBench</a> • 🤗 <a href="https://huggingface.co/collections/FreedomIntelligence/apollomoe-and-apollo2-670ddebe3bb1ba1aebabbf2c" target="blank">Models</a> •🌐 <a href="https://github.com/FreedomIntelligence/Apollo" target="blank">Apollo</a> • 🌐 <a href="https://github.com/FreedomIntelligence/ApolloMoE" target="_blank">ApolloMoE</a> </p>

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🌈 Update

• —[2024.10.15] ApolloMoE repo is published！🎉

Languages Coverage

12 Major Languages and 38 Minor Languages

<details> <summary>Click to view the Languages Coverage</summary>

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Architecture

<details> <summary>Click to view the MoE routing image</summary>

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Results

Dense

🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-0.5B" target="blank">Apollo2-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-1.5B" target="blank">Apollo2-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-2B" target="_blank">Apollo2-2B</a>

🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-3.8B" target="blank">Apollo2-3.8B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-7B" target="blank">Apollo2-7B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-9B" target="_blank">Apollo2-9B</a>

<details> <summary>Click to view the Dense Models Results</summary>

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Post-MoE

🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-0.5B" target="blank">Apollo-MoE-0.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-1.5B" target="blank">Apollo-MoE-1.5B</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-7B" target="_blank">Apollo-MoE-7B</a>

<details> <summary>Click to view the Post-MoE Models Results</summary>

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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 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a>

<details><summary>Click to expand</summary>

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• —Data category

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• —Evaluation 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>

<details><summary>Click to expand</summary>

• —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, Clinicalknowledge, Collegemedicine, Genetics, Nutrition, Traditionalchinesemedicine, 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

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Results reproduction

<details><summary>Click to expand</summary>

We take Apollo2-7B or Apollo-MoE-0.5B as example

1. 1.Download Dataset for project:

      bash 0.download_data.sh  

1. 1.Prepare test and dev data for specific model:

• —Create test data for with special token

       bash 1.data_process_test&dev.sh

1. 1.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

1. 1.Train the model

• —If you want to train in Multi Nodes please refer to ./src/sft/trainingconfig/zeromulti.yaml

       bash 3.single_node_train.sh

1. 1.Evaluate your model: Generate score for benchmark

         bash 4.eval.sh

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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}, 
}