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ssu-project/
OLMo-2-1124-13B-Instruct-ne-adalora

Name: OLMo-2-1124-13B-Instruct-ne-adalora
Availability: InStock

allenai/OLMo-2-1124-13B-Instruct

finetune

apache-2.0

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OLMo 2 1124 13B Instruct for Nepali: AdaLoRA

This model is built on top of OLMo 2 1124 13B 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-2-1124-13B-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.

python

from transformers import AutoTokenizer, AutoModelForCausalLM

from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "allenai/OLMo-2-1124-13B-Instruct",
)
model = PeftModel.from_pretrained(
    base_model,
    "ssu-project/OLMo-2-1124-13B-Instruct-ne-adalora",
)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(
    "allenai/OLMo-2-1124-13B-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}, 
}