metadata
license: gemma
language:
- my
base_model: google/gemma-2-9b
library_name: transformers
Gemma2 9B for Burmese: No vocabulary adaptation
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 no additional target vocabulary. It retains the original vocabulary of Gemma2 9B.
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.
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-9b"
)
model = PeftModelForCausalLM.from_pretrained(
model,
"atsuki-yamaguchi/gemma-2-9b-my-30K-lapt"
)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(
"google/gemma-2-9b"
)
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},
}