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RichardErkhov/x2bee_-_POLAR-14B-v0.5-gguf
x2bee_-_POLAR-14B-v0.5-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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4% of all-time downloads
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.gguf186 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
POLAR-14B-v0.5 - GGUF
| Name | Quant method | Size |
|---|---|---|
| POLAR-14B-v0.5.Q2_K.gguf | Q2_K | 4.92GB |
| POLAR-14B-v0.5.IQ3_XS.gguf | IQ3_XS | 5.47GB |
| POLAR-14B-v0.5.IQ3_S.gguf | IQ3_S | 5.77GB |
| POLAR-14B-v0.5.Q3_K_S.gguf | Q3_K_S | 5.74GB |
| POLAR-14B-v0.5.IQ3_M.gguf | IQ3_M | 5.97GB |
| POLAR-14B-v0.5.Q3_K.gguf | Q3_K | 6.4GB |
| POLAR-14B-v0.5.Q3_K_M.gguf | Q3_K_M | 6.4GB |
| POLAR-14B-v0.5.Q3_K_L.gguf | Q3_K_L | 6.97GB |
| POLAR-14B-v0.5.IQ4_XS.gguf | IQ4_XS | 7.18GB |
| POLAR-14B-v0.5.Q4_0.gguf | Q4_0 | 7.48GB |
| POLAR-14B-v0.5.IQ4_NL.gguf | IQ4_NL | 7.57GB |
| POLAR-14B-v0.5.Q4_K_S.gguf | Q4_K_S | 7.54GB |
| POLAR-14B-v0.5.Q4_K.gguf | Q4_K | 7.97GB |
| POLAR-14B-v0.5.Q4_K_M.gguf | Q4_K_M | 7.97GB |
| POLAR-14B-v0.5.Q4_1.gguf | Q4_1 | 8.3GB |
| POLAR-14B-v0.5.Q5_0.gguf | Q5_0 | 9.12GB |
| POLAR-14B-v0.5.Q5_K_S.gguf | Q5_K_S | 9.12GB |
| POLAR-14B-v0.5.Q5_K.gguf | Q5_K | 9.37GB |
| POLAR-14B-v0.5.Q5_K_M.gguf | Q5_K_M | 9.37GB |
| POLAR-14B-v0.5.Q5_1.gguf | Q5_1 | 9.94GB |
| POLAR-14B-v0.5.Q6_K.gguf | Q6_K | 10.87GB |
| POLAR-14B-v0.5.Q8_0.gguf | Q8_0 | 14.07GB |
license: apache-2.0 language:

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.
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("x2bee/POLAR-14B-v0.5")
model = AutoModelForCausalLM.from_pretrained("x2bee/POLAR-14B-v0.5")
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.
More information on training data needed
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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BibTeX:
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If you would like more information about our company, please visit the link below. tech.x2bee.com
Woomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son
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Use the code below to get started with the model.
<details> <summary> Click to expand </summary>More information needed
</details>