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language license tags pipeline_tag model-index
en
cc-by-nc-sa-4.0
merge
text-generation
name results
Sakura-SOLAR-Instruct
task dataset metrics source
type name
text-generation
Text Generation
name type config split args
AI2 Reasoning Challenge (25-Shot)
ai2_arc
ARC-Challenge
test
num_few_shot
25
type value name
acc_norm
70.99
normalized accuracy
task dataset metrics source
type name
text-generation
Text Generation
name type split args
HellaSwag (10-Shot)
hellaswag
validation
num_few_shot
10
type value name
acc_norm
88.42
normalized accuracy
task dataset metrics source
type name
text-generation
Text Generation
name type config split args
MMLU (5-Shot)
cais/mmlu
all
test
num_few_shot
5
type value name
acc
66.33
accuracy
task dataset metrics source
type name
text-generation
Text Generation
name type config split args
TruthfulQA (0-shot)
truthful_qa
multiple_choice
validation
num_few_shot
0
type value
mc2
71.79
task dataset metrics source
type name
text-generation
Text Generation
name type config split args
Winogrande (5-shot)
winogrande
winogrande_xl
validation
num_few_shot
5
type value name
acc
83.66
accuracy
task dataset metrics source
type name
text-generation
Text Generation
name type config split args
GSM8k (5-shot)
gsm8k
main
test
num_few_shot
5
type value name
acc
65.2
accuracy

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)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 74.40
AI2 Reasoning Challenge (25-Shot) 70.99
HellaSwag (10-Shot) 88.42
MMLU (5-Shot) 66.33
TruthfulQA (0-shot) 71.79
Winogrande (5-shot) 83.66
GSM8k (5-shot) 65.20