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---
library_name: transformers
license: apache-2.0
datasets:
- We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs
language:
- ko
pipeline_tag: text-generation
---
**Exllamav2** quant (**exl2** / **6.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-2_2bpw_exl2)**</center> | <center>4051 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-2_5bpw_exl2)**</center> | <center>4510 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-3_0bpw_exl2)**</center> | <center>5341 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-3_5bpw_exl2)**</center> | <center>6173 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-3_75bpw_exl2)**</center> | <center>6589 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-4_0bpw_exl2)**</center> | <center>7004 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-4_25bpw_exl2)**</center> | <center>7420 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-5_0bpw_exl2)**</center> | <center>8670 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-6_0bpw_exl2)**</center> | <center>10348 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-6_5bpw_exl2)**</center> | <center>11183 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/x2bee_POLAR-14B-DPO-v1.3-8_0bpw_exl2)**</center> | <center>12815 MB</center> | <center>8</center> |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
## 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
## Direct Use
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("x2bee/POLAR-14B-DPO-v1.3")
model = AutoModelForCausalLM.from_pretrained("x2bee/POLAR-14B-DPO-v1.3")
```
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
# 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
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
# 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
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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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
More information needed
### Hardware
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### Software
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# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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>
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