---
license: mit
language: sw
---
TigerBot-7B Swahili [LAPT + CLP+]
===

## How to use
```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained(
    "atsuki-yamaguchi/tigerbot-7b-base-clpp-sw"
)
tokenizer = AutoTokenizer.from_pretrained(
    "atsuki-yamaguchi/tigerbot-7b-base-clpp-sw"
)

# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
    "atsuki-yamaguchi/tigerbot-7b-base-clpp-sw",
    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