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
The difference between "DopeorNope/COKAL-DPO_test-v2" and this model is that this model has different hyper-parameters from the one in that setting regarding the final version.

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.

Reference papers

Data Strategy:
- LIMA(Zhou et al., 2023)
- Near Dedup algorithm(Lee et al., 2022)
Model Architecture:
- Llama2(Touvron et al., 2023)

Implementation Code

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "HumanF-MarkrAI/COKAL-DPO-13b-v2"
model = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
model_tokenizer = AutoTokenizer.from_pretrained(repo)

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.

Markr-AI/COKAL-DPO-13b-v2

🐻‍❄️COKAL-DPO_13b-v2🐻‍❄️

Model Details

Implementation Code

Acknowledgement

Markr-AI/
COKAL-DPO-13b-v2