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metadata
language:
  - ko
datasets:
  - kyujinpy/OpenOrca-KO
  - kyujinpy/KOpen-platypus
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0

(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다
The license is cc-by-nc-sa-4.0.

🐳KoR-Orca-Platypus-13B🐳

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Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture
KoR-Orca-Platypus-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.

Repo Link
Github Korean-OpenOrca: 🐳KoR-Orca-Platypus-13B🐳

Base Model hyunseoki/ko-en-llama2-13b

Training Dataset
Version of combined dataset: kyujinpy/KOR-OpenOrca-Platypus

I combined OpenOrca-KO and kyujinpy/KOpen-platypus. I use A100 GPU 40GB and COLAB, when trianing.

Model Benchmark

KO-LLM leaderboard

Model Average Ko-ARC Ko-HellaSwag Ko-MMLU Ko-TruthfulQA Ko-CommonGen V2
KoR-Orca-Platypus-13B🐳(ours) 50.13 42.06 53.95 42.28 43.55 68.78
GenAI-llama2-ko-en-platypus 49.81 45.22 55.25 41.84 44.78 61.97
KoT-Platypus2-13B 49.55 43.69 53.05 42.29 43.34 65.38
KO-Platypus2-13B 47.90 44.20 54.31 42.47 44.41 54.11
Korean-OpenOrca-13B🐳 47.85 43.09 54.13 40.24 45.22 56.57

Compare with Top 4 SOTA models. (update: 10/14)

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/KoR-Orca-Platypus-13B"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)