(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄으로 개발된 모델입니다
The license is cc-by-nc-sa-4.0.
🐻❄️COKAL-DPO_test-v2🐻❄️
Model Details
Model Developers Seungyoo Lee (DopeorNope)
Input Models input text only.
Output Models generate text only.
Model Architecture
COKAL-DPO_test-v2 is an auto-regressive 13B language model based on the LLaMA2 transformer architecture.
Base Model DopeorNope/COKAL_pre_DPO_Test_v1-13b
COKAL_pre_DPO_Test_v1-13b is SFT model to train DPO method
Training Dataset
- DPO training dataset: DopeorNope/DPO-Ko-Dataset - private
This dataset was constructed by directly collecting and reorganizing data by DopeorNope, obtaining insights from "lvwerra/stack-exchange-paired" to create a paired dataset. (It means I do not use stack-exchange-paired; I just got an insight from it.)
- SFT training dataset: DopeorNope/Orca_Near_Dedup-v2 - private
This dataset is based on "kyujinpy/OpenOrca-KO" and has been processed using the Near Dedup algorithm to remove items with a Jaccard Similarity threshold of 0.8 or higher. In addition, inconsistent inputs have been cleaned and modified.
Training
I developed the model in an environment with four RTX 3090 GPUs running Ubuntu 18.04.
It seems that when uploading the model directly to a repository from a Linux server, there may be an issue causing the model to appear to have more parameters. However, this model is based on a 13B architecture.
Implementation Code
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "DopeorNope/COKAL-DPO_test-v2"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
model_tokenizer = AutoTokenizer.from_pretrained(repo)
Acknowledgement
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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.