README.md

metadata

language:
  - ko
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0

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

🐻‍❄️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.

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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.

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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

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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"

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