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https://github.com/FreedomIntelligence/Apollo
Multilingual Medicine: Model, Dataset, Benchmark, Code
https://github.com/FreedomIntelligence/Apollo
llm medical open-source
Last synced: 3 months ago
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Multilingual Medicine: Model, Dataset, Benchmark, Code
- Host: GitHub
- URL: https://github.com/FreedomIntelligence/Apollo
- Owner: FreedomIntelligence
- License: apache-2.0
- Created: 2024-01-22T11:31:15.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-03-09T12:52:55.000Z (4 months ago)
- Last Synced: 2024-03-09T15:21:18.977Z (4 months ago)
- Topics: llm, medical, open-source
- Language: Python
- Homepage: https://apollo.llmzoo.com/#/
- Size: 12 MB
- Stars: 72
- Watchers: 9
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Lists
- awesome-latest-LLM - Apollo - 7B) | ~7B | | | | | | multilingual | (Model)
README
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
   
π Paper β’ π Demo β’ π€ ApolloCorpus β’ π€ XMedBench
δΈζ | English
## π Update
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** ApolloCorpus and XMedBench is publishedοΌπ
* **[2024.01.23]** Apollo repo is publishedοΌπ
## Results
π€ Apollo-0.5B β’ π€ Apollo-1.8B β’ π€ Apollo-2B β’ π€ Apollo-6B β’ π€ Apollo-7B
π€ Apollo-0.5B-GGUF β’ π€ Apollo-2B-GGUF β’ π€ Apollo-6B-GGUF β’ π€ Apollo-7B-GGUF
## Dataset & Evaluation
- Dataset
π€ ApolloCorpus
Click to expand
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/Medbase_data/blob/main/Medbase_data-datasets.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/Medbase_data/tree/main/train)
- 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
π€ XMedBench
Click to expand
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
## 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. (Optional) Proxy-Tuning: Directly improve model capabilities without fine-tuning
```
bash src/proxy-tuning/scripts/eval/proxy_tuning.sh
```
6. Evaluate your model: Generate score for benchmark
```
bash 4.eval.sh
```
7. Evaluate your model: Play with your ckpts in bash
```
python ./src/evaluate/cli_demo.py --model_name='./ckpts/your/path/tfmr'
```
## Acknowledgment
- [HuatuoGPT-II](https://github.com/FreedomIntelligence/HuatuoGPT-II)
- [proxy-tuning](https://github.com/alisawuffles/proxy-tuning)
## 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}
}
```
## Star History
