**The license is `cc-by-nc-sa-4.0`.**  

**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄으로 개발된 모델입니다**
  
# **🐻‍❄️COKAL_merged_test-v1-13B🐻‍❄️**  
![img](https://drive.google.com/uc?export=view&id=1Uwj17SlMfaE3fqiVFrnTOdnEWoZqYJmr)  

## Model Details

**Model Developers** Seungyoo Lee(DopeorNope)

**Input** Models input text only.

**Output** Models generate text only.

**Model Architecture**  
COKAL_merged_test-v1-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.


---

## **Base Model**  

[HumanF-MarkrAI/COKAL-DPO-13b-v2](https://huggingface.co/HumanF-MarkrAI/COKAL-DPO-13b-v2)   

[MarkrAI/DopeorNope-maestro-v2-DPO-13b](https://huggingface.co/MarkrAI/DopeorNope-maestro-v2-DPO-13b)   


## **Implemented Method**

I utilized `slerp merge` to smoothly blend the gradients of the base models to create it.

The merging approach relies on some luck, but at the same time, if I have an accurate understanding of my model's performance, I can carefully select models that excel in each aspect to develop a well-balanced model.

Thanks to [maywell](https://huggingface.co/maywell) for sharing useful tips related to the merge method.
 

---

# **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 |
| --- | --- | --- | --- | --- | --- | --- |
| COKAL_merged_test-v1-13B🐻‍❄️ | 52.72 | 51.45 | 60.55 | 44.8 | 49.05 | 57.73 |
| [COKAL-DPO-13b-v2🐻‍❄️](https://huggingface.co/HumanF-MarkrAI/COKAL-DPO-13b-v2)  | 52.69 | 54.95 | 63.02 | 43.98 | 51.67 | 49.82 |
| [COKAL-DPO_test-v2-13b🐻‍❄️](https://huggingface.co/DopeorNope/COKAL-DPO_test-v2-13b)  | 52.67 | 55.63 | 63.5 | 43.49 | 51.5 | 49.23 | 
| [hyeogi/Yi-6b-dpo-v0.2](https://huggingface.co/hyeogi/Yi-6b-dpo-v0.2) | 52.63 | 41.72 | 52.96 | 46.69 | 52.38 | 69.42 |
| [DopeorNope-maestro-v2-DPO-13b🐻‍❄️](https://huggingface.co/MarkrAI/DopeorNope-maestro-v2-DPO-13b)  | 49.42 | 45.14 | 56.69 | 41.37 | 42.26 | 61.63 |


---
  
# Implementation Code


## Load model
```python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "DopeorNope/COKAL_merged_test-v1-13B"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
## Prompt (Alpaca format)

```python

prompt= f"아래는 문제를 설명하는 지시사항과, 구체적인 답변을 방식을 요구하는 입력이 함께 있는 문장입니다. 이 요청에 대해 적절하게 답변해주세요.\n\n### 지시사항:\n{instruction}\n\n### 입력:\n{input}\n\n### 답변:\n"

prompt_no_input = f"아래는 문제를 설명하는 지시사항입니다. 이 요청에 대해 적절하게 답변해주세요.\n\n### 지시사항:\n{instruction}\n\n### 답변:\n"


```

# Acknowledgement

- 이 모델은 과학기술정보통신부·광주광역시가 공동 지원한 '인공지능 중심 산업융합 집적단지 조성사업'으로 지원을 받아 수행된 연구 결과입니다.

- This model was supported by Artificial intelligence industrial convergence cluster development project funded by the Ministry of Science and ICT(MSIT, Korea)&Gwangju Metropolitan City.


---
{"language": ["ko"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "tags": ["merge"], "pipeline_tag": "text-generation"}
transformers safetensors llama text-generation merge ko license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us