| language: | |
| - cs | |
| 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. | |
| * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) | |
| * [Paper](https://arxiv.org/abs/2309.08958) | |
| #### Instruction tuning details | |
| * Base model: [bloom-1b1](https://huggingface.co/bloom-1b1) | |
| * Instruction tuning language: Czech | |
| * 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](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). | |
| #### Usage | |
| The model checkpoint should be loaded using `transformers` library. | |
| Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/fpft) 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", | |
| } | |
| ``` | |