metadata
language:
- de
tags:
- generation
- question answering
- instruction tuning
license: cc-by-nc-4.0
Model Description
This HF repository contains base LLMs instruction tuned (SFT) with full-parameter fine-tuning and then used to study whether monolingual or multilingual instruction tuning is more favourable.
Instruction tuning details
- Base model: bloom-560m
- Instruction tuning language: German
- 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",
}