metadata
language:
- en
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
tags:
- merge
Sakura-SOLAR-Instruct
(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다
Model Details
Model Developers Kyujin Han (kyujinpy)
Method
Using Mergekit.
I shared the information about my model. (training and code)
Please see: ⭐Sakura-SOLAR.
Blog
Model Benchmark
Open leaderboard
- Follow up as link.
Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
---|---|---|---|---|---|---|---|
Sakura-SOLRCA-Instruct-DPO | 74.05 | 71.16 | 88.49 | 66.17 | 72.10 | 82.95 | 63.46 |
Sakura-SOLAR-Instruct-DPO-v2 | 74.14 | 70.90 | 88.41 | 66.48 | 71.86 | 83.43 | 63.76 |
kyujinpy/Sakura-SOLAR-Instruct | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20 |
Rank1 2023.12.27 PM 11:50
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/Sakura-SOLAR-Instruct"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)