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library_name: transformers
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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##
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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<!-- This
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[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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[More Information Needed]
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:**
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- **Hours used:**
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- **Cloud Provider:**
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- **Compute Region:**
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- **Carbon Emitted:**
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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<!-- 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 Needed]
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---
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license: apache-2.0
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language:
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- ko
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library_name: transformers
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# Model Details
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## Model Description
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<!-- Provide a longer summary of what this model is/does. -->
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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.
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- **Developed by:** AI-Lab of Plateer(Woomun Jung, Eunsoo Ha, MinYoung Joo, Seongjun Son)
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- **Model type:** Language model
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- **Language(s) (NLP):** ko
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- **License:** apache-2.0
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- Parent Model: upstage/SOLAR-10.7B-v1.0
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# Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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## Direct Use
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("x2bee/POLAR-14B-v0.2")
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model = AutoModelForCausalLM.from_pretrained("x2bee/POLAR-14B-v0.2")
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```
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## Downstream Use [Optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- 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." -->
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## Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- 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." -->
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# Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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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.
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## Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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# Training Details
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## Training Data
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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More information on training data needed
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## Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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### Preprocessing
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### Speeds, Sizes, Times
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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More information needed
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# Evaluation
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## Testing Data, Factors & Metrics
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### Testing Data
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More information needed
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### Factors
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More information needed
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### Metrics
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More information needed
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## Results
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# Model Examination
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# Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** More information needed
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- **Hours used:** More information needed
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- **Cloud Provider:** More information needed
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- **Compute Region:** More information needed
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- **Carbon Emitted:** More information needed
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# Technical Specifications [optional]
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## Model Architecture and Objective
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More information needed
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## Compute Infrastructure
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More information needed
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### Hardware
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### Software
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# Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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More information needed
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**APA:**
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More information needed
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# Glossary [optional]
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More information needed
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# More Information [optional]
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If you would like more information about our company, please visit the link below.
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[tech.x2bee.com](https://tech.x2bee.com/)
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# Model Card Authors [optional]
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<!-- 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. -->
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Woomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son
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# Model Card Contact
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More information needed
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# How to Get Started with the Model
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Use the code below to get started with the model.
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<details>
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<summary> Click to expand </summary>
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More information needed
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</details>
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