File size: 1,834 Bytes
a4b1595 f582267 a4b1595 f582267 a4b1595 b0714c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
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},
}
```
|