63 lines
1.6 KiB
Markdown
63 lines
1.6 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- allenai/MADLAD-400
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language:
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- bn
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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library_name: transformers
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---
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# Qwen2.5 7B Instruct for Bengali: Continual pre-training only
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This model is built on top of Qwen2.5 7B Instruct adapted for Bengali using 500M target language tokens sampled from MADLAD-400.
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## Model Details
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* **Vocabulary**: This model has no additional target vocabulary. It retains the original vocabulary of Qwen2.5 7B Instruct.
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* **Training**: This model was continually pre-trained on 500M target language tokens sampled from MADLAD-400.
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## Model Description
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- **Language:** Bengali
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- **License:** Apache 2.0
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- **Fine-tuned from model:** Qwen/Qwen2.5-7B-Instruct
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## Model Sources
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- **Repository:** https://github.com/gucci-j/chat-cve
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- **Paper:** https://arxiv.org/abs/2412.11704
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(
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"atsuki-yamaguchi/Qwen2.5-7B-Instruct-bn-lapt-madlad"
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"Qwen/Qwen2.5-7B-Instruct"
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)
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```
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## Citation
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```
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@article{yamaguchi2025adapting,
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title={Adapting Chat Language Models Using Only Target Unlabeled Language Data},
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author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras},
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journal={Transactions on Machine Learning Research},
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issn={2835-8856},
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year={2025},
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url={https://openreview.net/forum?id=6IdoIKowfe},
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note={}
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}
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```
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