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README.md

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+
+---
+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: SSU-Wanda (Calibration with 128 samples)
+
+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 SSU-Wanda approach but calibrated with 128 samples instead of 500 samples.
+
+## 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-ssu_128"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "ssu-project/OLMo-2-1124-7B-Instruct-ig-ssu_128"
+)
+```
+
+
+## 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}, 
+}
+```
+
+