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Poly-platypus-ko

KO-platypus Korean-Open-platypus를 활용하여 Polyglot-ko를 fine-tuning한 Korean-Platypus model

  • 본 연구는 (주)마커와 (주)미디어그룹사람과숲의 오픈소스 LLM 연구 컨소시엄에서 진행되었습니다.

Model description

Poly-platypus-ko-12.8b🥮: Hugging Face
KOpen-Platypus🥮: Hugging Face

Polyglot-ko + KO-Platypus


CoTy-platypus-ko-12.8b: Hugging Face
KoCoT_2000: Hugging Face

Polyglot-ko + CoT-llama2-ko


KO-platypus2 & CoT-llama2-ko

KO-platypus2
CoT-llama2-ko

Performance

When I evaluated Ko-Platy, I used this repo.
And, implement below code.

# In colab,
!python main.py \
    --model gpt2 \ 
    --model_args pretrained=..your_model_name.. \
    --tasks kobest_hellaswag,kobest_copa,kobest_boolq,kobest_sentineg \
    --device cuda:0 \
    --num_fewshot 0 # 5, 10, 25, ...

COPA (F1)

Model 0-shot 5-shot 10-shot 50-shot
Polyglot-ko-5.8b 0.7745 0.7676 0.7775 0.7887
Polyglot-ko-12.8b 0.7937 0.8108 0.8037 0.8369
Llama-2-Ko-7b 20B 0.7388 0.7626 0.7808 0.7979
Llama-2-Ko-7b 40B 0.7436 0.7927 0.8037 0.8259
KO-platypus2-7B-EX 0.7509 0.7899 0.8029 0.8290
KoT-platypus2-7B 0.7517 0.7868 0.8009 0.8239
Poly-platypus-ko-12.8b(ours) 0.7876 0.8099 0.8008 0.8239

HellaSwag (F1)

Model 0-shot 5-shot 10-shot 50-shot
Polyglot-ko-5.8b 0.5976 0.5998 0.5979 0.6208
Polyglot-ko-12.8b 0.5954 0.6306 0.6098 0.6118
Llama-2-Ko-7b 20B 0.4518 0.4668 0.4726 0.4828
Llama-2-Ko-7b 40B 0.4562 0.4657 0.4698 0.4774
KO-platypus2-7B-EX 0.4571 0.4461 0.4371 0.4525
KoT-platypus2-7B 0.4432 0.4382 0.4550 0.4534
Poly-platypus-ko-12.8b(ours) 0.4838 0.4858 0.5005 0.5062

BoolQ (F1)

Model 0-shot 5-shot 10-shot 50-shot
Polyglot-ko-5.8b 0.4356 0.5698 0.5187 0.5236
Polyglot-ko-12.8b 0.4818 0.6041 0.6289 0.6448
Llama-2-Ko-7b 20B 0.3607 0.6797 0.6801 0.6622
Llama-2-Ko-7b 40B 0.5786 0.6977 0.7084 0.7144
KO-platypus2-7B-EX 0.6028 0.6979 0.7016 0.6988
KoT-platypus2-7B 0.6142 0.6757 0.6839 0.6878
Poly-platypus-ko-12.8b(ours) 0.4888 0.6520 0.6568 0.6835

SentiNeg (F1)

Model 0-shot 5-shot 10-shot 50-shot
Polyglot-ko-5.8b 0.3394 0.8841 0.8808 0.9521
Polyglot-ko-12.8b 0.9117 0.9015 0.9345 0.9723
Llama-2-Ko-7b 20B 0.4855 0.8295 0.8711 0.8513
Llama-2-Ko-7b 40B 0.4594 0.7611 0.7276 0.9370
KO-platypus2-7B-EX 0.5821 0.7653 0.7991 0.8643
KoT-platypus2-7B 0.6127 0.7199 0.7531 0.8381
Poly-platypus-ko-12.8b(ours) 0.8490 0.9597 0.9723 0.9847

References

Kopen-Platypus🥮
MarkrAI/kyujin-Poly-platypus-ko-12.8b🥮
Platypus
Polyglot-ko-12.8b
ko-lm-evaluation-harness

Additional info about image

I made the image, inspired by Platypus-LLM.
I used Playground AI, then applying prompt engineering. (For example, img2img, guidance etc...)

img
When I made Poly-platypus image, I use prompt like above image.

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Polyglot-Ko를 기반으로 KOpen-platypus dataset을 이용하여 fine-tuning한 model

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