---
license: apache-2.0
datasets:
- allenai/MADLAD-400
language:
- ig
base_model:
- allenai/OLMo-2-1124-7B-Instruct
---
# OLMo 2 1124 7B Instruct for Igbo: LoTA (37.5% sparsity)

This model is built on top of OLMo 2 1124 7B Instruct adapted for Igbo using 200M target language tokens sampled from MADLAD-400. The model is adapted using the LoTA approach with 37.5% sparsity.

## Model Description

- **Language:** Igbo
- **License:** Apache 2.0
- **Fine-tuned from model:** [allenai/OLMo-2-1124-7B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-7B-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.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "ssu-project/OLMo-2-1124-7B-Instruct-ig-lota_0.375"
)
tokenizer = AutoTokenizer.from_pretrained(
    "ssu-project/OLMo-2-1124-7B-Instruct-ig-lota_0.375"
)
```


## 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}, 
}
```