Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.0.21
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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
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]
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
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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
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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
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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
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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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