README.md

---
license: llama2
language:
- si
base_model: meta-llama/Llama-2-7b-hf
library_name: transformers
---
# Llama2 7B for Sinhala: 100 target vocabulary size + Random target vocabulary initialization

This model is built on top of Llama2 7B adapted for Sinhala 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 and LM head were initialized using Random initialization.
* **Training**: This model was additionally pre-trained on 30K target language sentences sampled from CC-100.

## Model Description

- **Language:** Sinhala
- **License:** Llama 2 Community License Agreement
- **Fine-tuned from model:** meta-llama/Llama-2-7b-hf


## 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(
    "atsuki-yamaguchi/Llama-2-7b-hf-si-30K-rand"
)
model = PeftModelForCausalLM.from_pretrained(
    model,
    "atsuki-yamaguchi/Llama-2-7b-hf-si-30K-rand"
)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(
    "atsuki-yamaguchi/Llama-2-7b-hf-si-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}, 
}
```