atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-slerp0305-emb-special

Qwen2.5 7B Instruct for Sinhala: ElChat

This model is built on top of Qwen2.5 7B Instruct adapted for Sinhala using 500M target language tokens sampled from MADLAD-400. It has an additional target vocabulary of 10K. The model was trained using the ElChat method.

Model Details

• Vocabulary: This model has an additional target vocabulary of 10K.
• Target vocabulary initialization: The target weights of the embedding and LM head were initialized using mean initialization.
• Training: This model was continually pre-trained on 500M target language tokens sampled from MADLAD-400.
• Post-processing: The model was post-processed using the ElChat method.

Model Description

• Language: Sinhala
• License: Apache 2.0
• Fine-tuned from model: Qwen/Qwen2.5-7B-Instruct

Model Sources

• Repository: https://github.com/gucci-j/chat-cve
• Paper: https://arxiv.org/abs/2412.11704

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-slerp0305-emb-special"
)
tokenizer = AutoTokenizer.from_pretrained(
    "atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-slerp0305-emb-special"
)

Citation

@misc{yamaguchi2024vocabularyexpansionchatmodels,
      title={{ElChat}: Adapting Chat Language Models Using Only Target Unlabeled Language Data}, 
      author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras},
      year={2024},
      eprint={2412.11704},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.11704},
}