README.md

---
library_name: peft
license: mit
language:
- ar
---
BLOOM-7B Arabic [LAPT + CLP+]
===

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

model = AutoPeftModelForCausalLM.from_pretrained(
  "atsuki-yamaguchi/bloom-7b1-clpp-ar"
)

# w/ GPU
model = AutoPeftModelForCausalLM.from_pretrained(
  "atsuki-yamaguchi/bloom-7b1-clpp-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


## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
  
### Framework versions
- PEFT 0.5.0