This repo aims to provide the data, models, evaluation benchmark for multilingual instruction fine-tuning.
We translate Alpaca-GPT4 and Evol-Instruct from English to languages using GPT-3.5 Turbo, where
| Language | Alpaca-GPT4 | Evol-instruct | ShareGPT |
|---|---|---|---|
| Chinese | [huggingface] | [huggingface] | [huggingface] |
| Japanese | [huggingface] | [huggingface] | [huggingface] |
| Korean | [huggingface] | [huggingface] | [huggingface] |
| German | [huggingface] | [huggingface] | [huggingface] |
| French | [huggingface] | [huggingface] | [huggingface] |
| Italian | [huggingface] | [huggingface] | [huggingface] |
| Arabic | [huggingface] | [huggingface] | [huggingface] |
| Portuguese | [huggingface] | [huggingface] | [huggingface] |
| Spanish | [huggingface] | [huggingface] | [huggingface] |
| Hindi | [huggingface] | [huggingface] | [huggingface] |
| Indonesian | [huggingface] | [huggingface] | [huggingface] |
python -m src.deploy.cli --model-path /path/to/weights/
For example, you can use FreedomIntelligence/phoenix-multiple-langs-v1 fine-tuned on eight languages (English, Chinese, French, Spanish, Portuguese, Arabic, Indonesian, Hindi):
python -m src.deploy.cli --model-path FreedomIntelligence/phoenix-multiple-langs-v1
python -m src.deploy.webapp.controller
python -m src.deploy.webapp.model_worker --model-path /path/to/weights/
python -m src.deploy.webapp.gradio_web_server
Now, you can open your browser and chat with a model.
Specify the train_data_path and val_data_path and then run
bash scripts/train.sh
| Language | MMLU |
|---|---|
| Chinese | [huggingface] |
| Japanese | [huggingface] |
| Korean | [huggingface] |
| German | [huggingface] |
| French | [huggingface] |
| Italian | [huggingface] |
| Arabic | [huggingface] |
| Portuguese | [huggingface] |
| Spanish | [huggingface] |
| Hindi | [huggingface] |
| Indonesian | [huggingface] |
bash scripts/eval_mmlu.sh ${LANGUAGE} ${MODEL_PATH} ${MODEL_ID}
bash scripts/eval_vicuna-80.sh ${LANGUAGE} ${MODEL_PATH} ${MODEL_ID}
If you find this repository helpful, please cite the repository below.
@software{Chen_MultilingualSIFT_Multilingual_Supervised_2023,
author = {Chen, Zhihong and Yan, Shuo and Liang, Juhao and Jiang, Feng and Wu, Xiangbo and Yu, Fei and Chen, Guiming Hardy and Chen, Junying and Zhang, Hongbo and Li Jianquan and Wan Xiang and Wang, Benyou},
month = jul,
title = {{MultilingualSIFT: Multilingual Supervised Instruction Fine-tuning}},
url = {https://github.com/FreedomIntelligence/MultilingualSIFT.git},
version = {0.1},
year = {2023}
}
25 commits
14 commits
Python
98.6%
Shell
1.4%
This repo aims to provide the data, models, evaluation benchmark for multilingual instruction fine-tuning.
We translate Alpaca-GPT4 and Evol-Instruct from English to languages using GPT-3.5 Turbo, where
| Language | Alpaca-GPT4 | Evol-instruct | ShareGPT |
|---|---|---|---|
| Chinese | [huggingface] | [huggingface] | [huggingface] |
| Japanese | [huggingface] | [huggingface] | [huggingface] |
| Korean | [huggingface] | [huggingface] | [huggingface] |
| German | [huggingface] | [huggingface] | [huggingface] |
| French | [huggingface] | [huggingface] | [huggingface] |
| Italian | [huggingface] | [huggingface] | [huggingface] |
| Arabic | [huggingface] | [huggingface] | [huggingface] |
| Portuguese | [huggingface] | [huggingface] | [huggingface] |
| Spanish | [huggingface] | [huggingface] | [huggingface] |
| Hindi | [huggingface] | [huggingface] | [huggingface] |
| Indonesian | [huggingface] | [huggingface] | [huggingface] |
python -m src.deploy.cli --model-path /path/to/weights/
For example, you can use FreedomIntelligence/phoenix-multiple-langs-v1 fine-tuned on eight languages (English, Chinese, French, Spanish, Portuguese, Arabic, Indonesian, Hindi):
python -m src.deploy.cli --model-path FreedomIntelligence/phoenix-multiple-langs-v1
python -m src.deploy.webapp.controller
python -m src.deploy.webapp.model_worker --model-path /path/to/weights/
python -m src.deploy.webapp.gradio_web_server
Now, you can open your browser and chat with a model.
Specify the train_data_path and val_data_path and then run
bash scripts/train.sh
| Language | MMLU |
|---|---|
| Chinese | [huggingface] |
| Japanese | [huggingface] |
| Korean | [huggingface] |
| German | [huggingface] |
| French | [huggingface] |
| Italian | [huggingface] |
| Arabic | [huggingface] |
| Portuguese | [huggingface] |
| Spanish | [huggingface] |
| Hindi | [huggingface] |
| Indonesian | [huggingface] |
bash scripts/eval_mmlu.sh ${LANGUAGE} ${MODEL_PATH} ${MODEL_ID}
bash scripts/eval_vicuna-80.sh ${LANGUAGE} ${MODEL_PATH} ${MODEL_ID}
If you find this repository helpful, please cite the repository below.
@software{Chen_MultilingualSIFT_Multilingual_Supervised_2023,
author = {Chen, Zhihong and Yan, Shuo and Liang, Juhao and Jiang, Feng and Wu, Xiangbo and Yu, Fei and Chen, Guiming Hardy and Chen, Junying and Zhang, Hongbo and Li Jianquan and Wan Xiang and Wang, Benyou},
month = jul,
title = {{MultilingualSIFT: Multilingual Supervised Instruction Fine-tuning}},
url = {https://github.com/FreedomIntelligence/MultilingualSIFT.git},
version = {0.1},
year = {2023}
}
25 commits
14 commits
Python
98.6%
Shell
1.4%