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POLAR-7B-DPO-v1.02

Text Generation
Transformers
Safetensors
Korean
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text-generation-inference
Inference Endpoints
4-bit precision
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POLAR-7B-DPO-v1.02 / README.md
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metadata
library_name: transformers
license: apache-2.0
datasets:
  - We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs
language:
  - ko
pipeline_tag: text-generation

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Model Details

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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

Model Sources [optional]

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Uses

Direct Use 

from transformers import AutoTokenizer, AutoModelForCausalLM

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

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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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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Summary

Model Examination [optional]

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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]
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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 [optional]

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