metadata
license: apache-2.0
datasets:
- allenai/MADLAD-400
language:
- ig
base_model:
- allenai/OLMo-2-1124-13B-Instruct
OLMo 2 1124 13B Instruct for Igbo: SSU-Rand
This model is built on top of OLMo 2 1124 13B Instruct adapted for Igbo using 200M target language tokens sampled from MADLAD-400. The model is adapted using the SSU-Rand approach (i.e., randomly selecting parameters to update by column).
Model Description
- Language: Igbo
- License: Apache 2.0
- Fine-tuned from model: allenai/OLMo-2-1124-13B-Instruct
Model Sources
- Repository: https://github.com/gucci-j/ssu
- Paper: https://arxiv.org/abs/2512.04844
How to Get Started with the Model
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-ig-random"
)
tokenizer = AutoTokenizer.from_pretrained(
"ssu-project/OLMo-2-1124-13B-Instruct-ig-random"
)
Citation
@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},
}