Model Details

Model Description

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

Uses

Direct Use

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("x2bee/POLAR-7B-SFT-V1.0")
model = AutoModelForCausalLM.from_pretrained("x2bee/POLAR-7B-SFT-V1.0")

Downstream Use [Optional]

Out-of-Scope Use

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). 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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Training Details

Training Data

More information on training data needed

Training Procedure

Preprocessing

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Speeds, Sizes, Times

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Evaluation

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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

If you would like more information about our company, please visit the link below. tech.x2bee.com

Model Card Authors [optional]

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.

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