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---
license: llama3
language:
- si
base_model: meta-llama/Meta-Llama-3-8B
library_name: transformers
---
# Llama3 8B for Sinhala: 5000 target vocabulary size + Align target vocabulary initialization + 2x2LS/MTP/512 training
This model is built on top of Llama3 8B 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 and LM head were initialized using Align 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:** Llama 3 Community License Agreement
- **Fine-tuned from model:** meta-llama/Meta-Llama-3-8B
## 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/Llama-3-8B-si-30K-5000-align"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Llama-3-8B-si-30K-5000-align"
)
```
## 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},
}
```
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