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---
license: apache-2.0
datasets:
- allenai/MADLAD-400
language:
- ne
base_model:
- allenai/OLMo-2-1124-7B-Instruct
---
# OLMo 2 1124 7B Instruct for Nepali: AdaLoRA
This model is built on top of OLMo 2 1124 7B Instruct adapted for Nepali using 200M target language tokens sampled from MADLAD-400. The model is adapted using the AdaLoRA approach. This is based on https://arxiv.org/abs/2303.10512 and was the best-performing LoRA-based method in the HFT paper.
## Model Description
- **Language:** Nepali
- **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
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained(
"allenai/OLMo-2-1124-7B-Instruct",
)
model = PeftModel.from_pretrained(
base_model,
"ssu-project/OLMo-2-1124-7B-Instruct-ne-adalora",
)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(
"allenai/OLMo-2-1124-7B-Instruct"
)
```
## 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},
}
```
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