43 lines
1.4 KiB
Markdown
43 lines
1.4 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- pkupie/mc2_corpus
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language:
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- bo
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base_model:
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- Qwen/Qwen2.5-1.5B
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pipeline_tag: text-generation
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---
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# Qwen2.5-1.5B Continually Pretrained on Tibetan
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This model is a continual pretraining (CPT) checkpoint built by further pretraining Qwen2.5 1.5B on the Tibetan portion of the [MC^2 Corpus](https://huggingface.co/datasets/pkupie/mc2_corpus).
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The model is intended to improve Tibetan language modeling and to support research on low-resource language adaptation.
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Training details and methodology are described in: ["Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion"](https://arxiv.org/abs/2604.18106) (ACL 2026).
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## Training Data
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* **Corpus:** Tibetan subset of MC^2 Corpus
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* **Language:** Tibetan (`bo`)
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* **Training paradigm:** Continual pretraining (CPT) starting from Qwen2.5-1.5B
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## Intended Use
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This checkpoint is released primarily for research purposes. Researchers are welcome to use this CPT checkpoint as a base model for future work, particularly in model merging and logit fusion.
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## Citation
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If you use this model, please cite:
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```bibtex
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@article{zhang2026efficient,
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title={Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion},
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author={Zhang, Chen and Lin, Jiuheng and Liao, Zhiyuan and Feng, Yansong},
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journal={arXiv preprint arXiv:2604.18106},
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year={2026}
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}
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```
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