base_model: Translation-EnKo/exaone3-instrucTrans-v2-enko-7.8b
datasets:
- nayohan/aihub-en-ko-translation-12m
- nayohan/instruction_en_ko_translation_1.4m
- Translation-EnKo/trc_uniform_313k_eval_45_filtered
language:
- en
- ko
library_name: transformers
quantized_by: mradermacher
tags:
- translation
- enko
- ko
About
weighted/imatrix quants of https://huggingface.co/Translation-EnKo/exaone3-instrucTrans-v2-enko-7.8b
static quants are available at https://huggingface.co/mradermacher/exaone3-instrucTrans-v2-enko-7.8b-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.