README.md

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

# **Sakura-SOLAR-Instruct-DPO-v2**  
<img src='./sakura.png' width=512>
  
**(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](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo). 
     
I shared the information about my model. (training and code)  
Please see: ⭐[Sakura-SOLAR](https://github.com/KyujinHan/Sakura-SOLAR-DPO).  

# **Model Benchmark**  

## Open leaderboard
- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).  

| 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](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct) | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20

   
# Implementation Code
```python
### 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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