license: mit | |
language: ar | |
BLOOM-1B Arabic [LAPT] | |
=== | |
## How to use | |
```python | |
from peft import AutoPeftModelForCausalLM | |
from transformers import AutoTokenizer | |
model = AutoPeftModelForCausalLM.from_pretrained( | |
"atsuki-yamaguchi/bloom-1b1-lapt-ar" | |
) | |
tokenizer = AutoTokenizer.from_pretrained( | |
"bigscience/bloom-1b1" | |
) | |
# w/ GPU | |
model = AutoPeftModelForCausalLM.from_pretrained( | |
"atsuki-yamaguchi/bloom-1b1-lapt-ar", | |
device_map="auto", | |
load_in_8bit=True, | |
) | |
``` | |
## Citation | |
``` | |
@article{yamaguchi2024empirical, | |
title={An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Generative {LLM} Inference}, | |
author={Atsuki Yamaguchi and Aline Villavicencio and Nikolaos Aletras}, | |
journal={ArXiv}, | |
year={2024}, | |
volume={abs/2402.10712}, | |
url={https://arxiv.org/abs/2402.10712} | |
} | |
``` | |
## Link | |
For more details, please visit https://github.com/gucci-j/llm-cva | |