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---
license: mit
language: ja
---
TigerBot-7B Japanese [LAPT + CLP+ (Untied)]
===
## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ja"
)
tokenizer = AutoTokenizer.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ja"
)
# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
"atsuki-yamaguchi/tigerbot-7b-base-clpp-untied-ja",
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
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