metadata

library_name: peft
license: mit
language:
  - ar

BLOOM-7B Arabic [LAPT + CLP+]

How to use

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