---
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🐳**  
![img](./Korean-OpenOrca.png)  

## 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🐳](https://github.com/Marker-Inc-Korea/Korean-OpenOrca)  

**Base Model**  [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b)   

**Training Dataset**  
Version of combined dataset: [kyujinpy/KOR-OpenOrca-Platypus](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus)

I combined [OpenOrca-KO](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) and [kyujinpy/KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus).
I use A100 GPU 40GB and COLAB, when trianing.

# **Model Benchmark**

## KO-LLM leaderboard
- Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-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](https://huggingface.co/42MARU/GenAI-llama2-ko-en-platypus) | 49.81 | 45.22 | 55.25 | 41.84 | 44.78 | 61.97 |  
| [KoT-Platypus2-13B](https://huggingface.co/kyujinpy/KoT-platypus2-13B) | 49.55 | 43.69 | 53.05 | 42.29 | 43.34 | 65.38 |  
| [KO-Platypus2-13B](https://huggingface.co/kyujinpy/KO-Platypus2-13B) | 47.90 | 44.20 | 54.31 | 42.47 | 44.41 | 54.11 | 
| [Korean-OpenOrca-13B🐳](https://huggingface.co/kyujinpy/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
```python
### 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)
```

---