This model is built on top of Qwen3 14B adapted for Amharic 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: Amharic
License: Apache 2.0
Fine-tuned from model: Qwen/Qwen3-14B

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.

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "atsuki-yamaguchi/Qwen3-14B-am-madlad-mean-slerp-emb-special"
)
tokenizer = AutoTokenizer.from_pretrained(
    "atsuki-yamaguchi/Qwen3-14B-am-madlad-mean-slerp-emb-special"
)

Citation

@article{yamaguchi2025adapting,
      title={Adapting Chat Language Models Using Only Target Unlabeled Language Data}, 
      author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras},
      journal={Transactions on Machine Learning Research},
      issn={2835-8856},
      year={2025},
      url={https://openreview.net/forum?id=6IdoIKowfe},
      note={}
}