atsuki-yamaguchi/gemma-2-9b-si-30K-align-merge

Gemma2 9B for Sinhala + ElChat

This model is built on top of atsuki-yamaguchi/gemma-2-9b-si-30K-align. The model uses the ElChat approach to mitigate catastrophic forgetting of the original capabilities of the source Gemma2 model.

Model Details

• Vocabulary: This model has an additional 100 target vocabulary.
• Target vocabulary initialization: The target weights of the embedding were initialized using Align.
• Training: This model was additionally pre-trained on 30K target language sentences sampled from CC-100. The training was conducted with the 2x2LS/MTP/512 strategies introduced in the paper.
• Post-hoc adaptation: This model used ElChat, a training-free, post-hoc method. See https://arxiv.org/abs/2412.11704 for details.

Model Description

• Language: Sinhala
• License: Gemma Terms of Use
• Fine-tuned from model: google/gemma-2-9b

Model Sources

• Repository: https://github.com/gucci-j/lowres-cve
• Paper: https://arxiv.org/abs/2406.11477

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/gemma-2-9b-si-30K-align-merge"
)
tokenizer = AutoTokenizer.from_pretrained(
    "atsuki-yamaguchi/gemma-2-9b-si-30K-align-merge"
)

Citation

@article{yamaguchi-etal-2024-effectively,
    title={How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?}, 
    author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras},
    year={2024},
    journal={ArXiv},
    year={2024},
    volume={abs/2406.11477},
    url={https://arxiv.org/abs/2406.11477}, 
}