README.md

metadata

language:
  - ko
datasets:
  - kyujinpy/OpenOrca-ko-v3
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.

🐳Korean-OpenOrca-13B-v2🐳

Model Details

Model Developers Kyujin Han (kyujinpy)

Model Architecture
Korean-OpenOrca-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.

Repo Link
Github Korean-OpenOrca: 🐳Korean-OpenOrca🐳

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

Training Dataset
I use OpenOrca-ko-v3.
Using DeepL, translate about OpenOrca.

I use A100 GPU 40GB and COLAB, when trianing.

Model comparisons

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
[Korean-OpenOrca-13B🐳]	48.79	43.09	54.13	40.24	45.22	61.28
[Korean-OpenOrca-13B-v2🐳]	48.17	43.17	54.51	42.90	41.82	58.44
Korean-OpenOrca-13B-v3🐳	48.86	43.77	54.30	41.79	43.85	60.57

Implementation Code

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

repo = "kyujinpy/Korean-OpenOrca-13B-v3"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

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