README.md

metadata

language:
  - ko
datasets:
  - kyujinpy/KOR-OpenOrca-Platypus-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.

🐳KOR-Orca-Platypus-13B🐳

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

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 kyujinpy/KOR-OpenOrca-Platypus-v3.
(with NEFTune.)

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

Model Benchmark

KO-LLM leaderboard

• Follow up as Open KO-LLM LeaderBoard.

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
[KOR-Orca-Platypus-13B🐳]	46.59	42.06	53.95	42.28	43.55	51.12
KOR-Orca-Platypus-13B🐳-v2	49.48	44.03	54.43	42.23	41.64	65.05
KOR-Orca-Platypus-13B🐳-v3	48.37	43.77	54.27	42.66	38.58	62.57

Compare with Top 4 SOTA models. (update: 10/09)

Implementation Code

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

repo = "kyujinpy/KOR-Orca-Platypus-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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