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--- |
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language: |
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- en |
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tags: |
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- generation |
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- question answering |
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- instruction tuning |
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license: cc-by-nc-4.0 |
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--- |
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### Model Description |
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This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable. |
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* [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) |
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* [Paper](https://arxiv.org/abs/2309.08958) |
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#### Instruction tuning details |
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* Base model: [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) |
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* Instruction tuning language: English |
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* Training method: LoRA. |
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* LoRA details: rank=8, alpha=16, target modules={key, query, value}. |
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* Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs. |
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* 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). |
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#### Usage |
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The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries. |
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Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions. |
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#### Citation |
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``` |
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@inproceedings{chen-etal-2024-monolingual, |
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title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", |
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author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", |
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year="2024", |
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booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", |
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} |
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``` |
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