README.md

metadata

language:
  - en
datasets:
  - argilla/distilabel-math-preference-dpo
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0

Sakura-SOLAR-Instruct-DPO-v2

(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다

Model Details

Model Developers Kyujin Han (kyujinpy)

Method
Using DPO method.
With argilla/distilabel-math-preference-dpo.

I shared the information about my model. (training and code)
Please see: ⭐Sakura-SOLAR.

Model Benchmark

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Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
Sakura-SOLAR-Instruct-DPO-v2	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Sakura-SOLAR-Instruct-DPO-v1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
kyujinpy/Sakura-SOLAR-Instruct	74.40	70.99	88.42	66.33	71.79	83.66	65.20

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/Sakura-SOLAR-Instruct-DPO-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

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