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Toprak Kesgin
2024-12-03 18:01:34 +00:00
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@@ -92,9 +92,19 @@ Each model's memory footprint can be anticipated by the qunatization docs in eit
- Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️ - Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗 - Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
## Contact # Citation
*Feel free to contact us whenever you confront any problems :)* ```bibtex
@inproceedings{kesgin2024optimizing,
title={Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training},
author={Kesgin, H Toprak and Yuce, M Kaan and Dogan, Eren and Uzun, M Egemen and Uz, Atahan and {\.I}nce, Elif and Erdem, Yusuf and Shbib, Osama and Zeer, Ahmed and Amasyali, M Fatih},
booktitle={2024 Innovations in Intelligent Systems and Applications Conference (ASYU)},
pages={1--6},
year={2024},
organization={IEEE}
}
```
## Contact
COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department
https://cosmos.yildiz.edu.tr/ https://cosmos.yildiz.edu.tr/
cosmos@yildiz.edu.tr cosmos@yildiz.edu.tr