---
library_name: transformers
license: apache-2.0
language:
- ko
pipeline_tag: text-generation
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f3ee48b1a907c6aa6d8f06/nGbRfMQEfAW_aDwisKn9T.png)


## Model Description

<!-- Provide a longer summary of what this model is/does. -->
POLAR is a Korean LLM developed by Plateer's AI-lab. It was inspired by Upstage's SOLAR. We will continue to evolve this model and hope to contribute to the Korean LLM ecosystem.

- **Developed by:** AI-Lab of Plateer(Woomun Jung, Eunsoo Ha, MinYoung Joo, Seongjun Son)
- **Model type:** Language model
- **Language(s) (NLP):** ko
- **License:** apache-2.0
- Parent Model: x2bee/POLAR-14B-v0.2
- wiki Data 를 활용하여 DPO 데이터 1만건 생성하여 학습했습니다.


## Direct Use
```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("x2bee/POLAR-14B-DPO-v1.4")
model = AutoModelForCausalLM.from_pretrained("x2bee/POLAR-14B-DPO-v1.4")
```

## Downstream Use [Optional]

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## Out-of-Scope Use

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# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


## Recommendations

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# Training Details

## Training Data

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More information on training data needed


## Training Procedure

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### Preprocessing

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### Speeds, Sizes, Times

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# Evaluation

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## Testing Data, Factors & Metrics

### Testing Data

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### Factors

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### Metrics

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## Results 

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# Model Examination

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# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed

# Technical Specifications [optional]

## Model Architecture and Objective

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## Compute Infrastructure

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### Hardware

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### Software

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# Citation

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**BibTeX:**

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**APA:**

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# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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# More Information [optional]

If you would like more information about our company, please visit the link below. 
[tech.x2bee.com](https://tech.x2bee.com/)


# Model Card Authors [optional]

<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->

Woomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son

# Model Card Contact

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# How to Get Started with the Model

Use the code below to get started with the model.

<details>
<summary> Click to expand </summary>

More information needed

</details>