---
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.
```python
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}, 
}
```