---
license: gemma
language:
- en
- te
base_model:
- atsuki-yamaguchi/gemma-2-9b-te-30K-align
---

# Gemma2 9B for Telugu + ElChat

This model is built on top of [atsuki-yamaguchi/gemma-2-9b-te-30K-align](https://huggingface.co/atsuki-yamaguchi/gemma-2-9b-te-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:** Telugu
- **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-te-30K-align-merge"
)
tokenizer = AutoTokenizer.from_pretrained(
    "atsuki-yamaguchi/gemma-2-9b-te-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}, 
}
```