library_name: transformers | |
pipeline_tag: text-generation | |
language: | |
- multilingual | |
tags: | |
- generation | |
- question answering | |
- instruction tuning | |
datasets: | |
- MBZUAI/Bactrian-X | |
license: cc-by-nc-4.0 | |
### Model Description | |
This HF repository hosts instruction fine-tuned multilingual BLOOM model using the parallel instruction dataset called Bactrain-X in 52 languages. | |
We progressively add a language during instruction fine-tuning at each time, and train 52 models in total. Then, we evaluate those models in three multilingual benchmarks. | |
Please refer to [our paper](https://arxiv.org/abs/2404.04850) for more details. | |
* Base model: [BLOOM 7B1](https://huggingface.co/bigscience/bloom-7b1) | |
* Instruction languages: English, Chinese, Afrikaans | |
* Instruction language codes: en, zh, af | |
* Training method: full-parameter fine-tuning. | |
### Usage | |
The model checkpoint should be loaded using `transformers` library. | |
```python | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
tokenizer = AutoTokenizer.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-3") | |
model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-3") | |
``` | |
### Citation | |
``` | |
@misc{lucky52, | |
title = "Lucky 52: How Many Languages Are Needed to Instruction Fine-Tune Large Language Models?", | |
author = "Shaoxiong Ji and Pinzhen Chen", | |
year = "2024", | |
eprint = "2404.04850", | |
archiveprefix = "arXiv", | |
primaryclass = "cs.CL" | |
} | |
``` | |