The license is cc-by-nc-sa-4.0.
(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄으로 개발된 모델입니다
🐻❄️COKAL_merged_test-v1-13B🐻❄️
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
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 for sharing useful tips related to the merge method.
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 |
|---|---|---|---|---|---|---|
| COKAL_merged_test-v1-13B🐻❄️ | 52.72 | 51.45 | 60.55 | 44.8 | 49.05 | 57.73 |
| COKAL-DPO-13b-v2🐻❄️ | 52.69 | 54.95 | 63.02 | 43.98 | 51.67 | 49.82 |
| COKAL-DPO_test-v2-13b🐻❄️ | 52.67 | 55.63 | 63.5 | 43.49 | 51.5 | 49.23 |
| hyeogi/Yi-6b-dpo-v0.2 | 52.63 | 41.72 | 52.96 | 46.69 | 52.38 | 69.42 |
| DopeorNope-maestro-v2-DPO-13b🐻❄️ | 49.42 | 45.14 | 56.69 | 41.37 | 42.26 | 61.63 |
Implementation Code
Load model
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)
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.
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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.