README.md

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

## 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🐳](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**  
I use [kyujinpy/KOR-OpenOrca-Platypus-v3(private! wait!)](https://huggingface.co./datasets/kyujinpy/KOR-OpenOrca-Platypus-v3).  

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🐳] | 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 |  

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

  
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/KOR-Orca-Platypus-13B-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```

---