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---
license: llama2
language:
- my
base_model: meta-llama/Llama-2-7b-hf
library_name: transformers
---
# Llama2 7B for Burmese: 100 target vocabulary size + Merge target vocabulary initialization
This model is built on top of Llama2 7B adapted for Burmese 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 Merge initialization.
* **Training**: This model was additionally pre-trained on 30K target language sentences sampled from CC-100.
## Model Description
- **Language:** Burmese
- **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-my-30K-merge"
)
model = PeftModelForCausalLM.from_pretrained(
model,
"atsuki-yamaguchi/Llama-2-7b-hf-my-30K-merge"
)
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/Llama-2-7b-hf-my-30K-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},
}
```
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