metadata
language:
- en
datasets:
- kyujinpy/orca_math_dpo
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
Sakura-SOLRCA-Math-Instruct-DPO-v2
Model Details
Model Developers Kyujin Han (kyujinpy)
Method
Using DPO method.
With Intel/orca_dpo_pairs and argilla/distilabel-math-preference-dpo.
I shared the merge version kyujinpy/orca_math_dpo.
I shared the information about my model. (training and code)
Please see: ⭐Sakura-SOLAR.
Model Benchmark
Open leaderboard
- Follow up as link.
Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
---|---|---|---|---|---|---|---|
Sakura-SOLRCA-Math-Instruct-DPO-v2 | 74.17 | 71.25 | 88.52 | 66.13 | 72.16 | 83.03 | 63.91 |
Sakura-SOLRCA-Math-Instruct-DPO-v1 | 74.13 | 71.25 | 88.48 | 66.21 | 72.12 | 82.87 | 63.84 |
Sakura-SOLRCA-Instruct-DPO | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
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-SOLRCA-Math-Instruct-DPO-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)