---
language:
- ko
datasets: 
- instruction
library_name: transformers
pipeline_tag: text-generation
license: mit
---
# **etri-ones-solar**  

## Model Details   
**Model Developers**  
- the model is fine-tuned by open instruction dataset

**Model Architecture**  
- this model is an auto-regressive language model based on the solar transformer architecture.  

**Base Model**   
- solar https://huggingface.co/upstage/SOLAR-10.7B-v1.0  

**Training Dataset**   
- 

---  
# Model comparisons1
> comming soon
   
| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| --- | --- | --- | --- | --- | --- | --- |
| **[...your_model_name...]** | NaN | NaN | NaN | NaN | NaN | NaN | 
  
---  
# Model comparisons2
> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness)   
   
| Model | Copa | Copa | HellaSwag | HellaSwag | BoolQ | BoolQ | Sentineg | Sentineg |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot |
| **[...your_model_name...]** | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
  
---
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "[...your_model_repo...]"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```

---