HPLT/sft-fpft-multilingual-downsampled-bloom-7b1

Model Description

This HF repository contains LLMs instruction tuned with full-parameter fine-tuning and then used to study whether monolingual or multilingual instruction tuning is more favourable.

• GitHub
• Paper

Instruction tuning details

• Base model: bigscience/bloom-7b1
• Instruction tuning language: multilingual downsampled (Bulgarian, Czech, Chinese, German, Finnish, French, Russian, and Spanish)
• Training method: full-parameter fine-tuning.
• Best checkpoint: best cross-entropy on a validation set, trained for 3 epochs.
• Dataset: machine-translated from yahma/alpaca-cleaned. You can download our data HERE.

Usage

The model checkpoint should be loaded using transformers library.

Please refer to our Github repository HERE for inference and training instructions.

Citation

@inproceedings{chen-etal-2024-monolingual,
  title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}",
  author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield",
  year="2024",
  booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
}