metadata
language:
- en
- ko
datasets:
- kyujinpy/KOR-OpenOrca-Platypus-v3
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
SOLAR-tail-10.7B-instruct-v1.0
Model Details
Model Developers Kyujin Han (kyujinpy)
Method
Instruction-tuning with PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0.
Datasets
datasets: kyujinpy/KOR-OpenOrca-Platypus-v3.
Hyperparameters
(I will update all!)
Model Benchmark
Open leaderboard
- Follow up as link.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 |
|---|---|---|---|---|---|---|
| PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0 | 51.70 | 46.93 | 58.19 | 53.15 | 46.52 | 53.72 |
| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 | 48.32 | 45.73 | 56.97 | 38.77 | 38.75 | 61.16 |
| jjourney1125/M-SOLAR-10.7B-v1.0 | 55.15 | 49.57 | 60.12 | 54.60 | 49.23 | 62.22 |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)