metadata
license: gemma
language:
- si
base_model: google/gemma-2-9b
library_name: transformers
Gemma2 9B for Sinhala: 5000 target vocabulary size + Random target vocabulary initialization + 2x2LS/MTP/512 training
This model is built on top of Gemma2 9B adapted for Sinhala using 30K target language sentences sampled from CC-100.
Model Details
- Vocabulary: This model has an additional 5000 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: 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-5000-rand"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/gemma-2-9b-si-30K-5000-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},
}