Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates
Paper
•
2512.04844
•
Published
•
4
This model is built on top of OLMo 2 1124 13B Instruct adapted for Amharic using 200M target language tokens sampled from MADLAD-400. The model is adapted using the SSU-Mag approach (i.e., selecting parameters to update by column based on their magnitudes).
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"ssu-project/OLMo-2-1124-13B-Instruct-am-magnitude"
)
tokenizer = AutoTokenizer.from_pretrained(
"ssu-project/OLMo-2-1124-13B-Instruct-am-magnitude"
)
@misc{yamaguchi2025mitigatingcatastrophicforgettingtarget,
title={Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates},
author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras},
year={2025},
eprint={2512.04844},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.04844},
}
Base model
allenai/OLMo-2-1124-7B