gemma-2-9b-my-30K-rand – AI Model by atsuki-yamaguchi | AlphaNeural AI

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atsuki-yamaguchi/
gemma-2-9b-my-30K-rand

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google/gemma-2-9b

text-generation-inference

endpoints_compatible

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Gemma2 9B for Burmese: 100 target vocabulary size + Random target vocabulary initialization + 2x2LS/MTP/512 training

This model is built on top of Gemma2 9B adapted for Burmese using 30K target language sentences sampled from CC-100.

Model Details

Vocabulary: This model has an additional 100 target vocabulary.
Target vocabulary initialization: The target weights of the embedding were initialized using Random initialization.
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.

Model Description

Language: Burmese
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.

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "atsuki-yamaguchi/gemma-2-9b-my-30K-rand"
)
tokenizer = AutoTokenizer.from_pretrained(
    "atsuki-yamaguchi/gemma-2-9b-my-30K-rand"
)

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}, 
}