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+
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+ ---
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+ language:
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+ - bg
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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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+
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+ ### Model Description
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+
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+ 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.
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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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+
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+ #### Instruction tuning details
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+ * Base model: [pythia-6.9b](https://huggingface.co/pythia-6.9b)
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+ * Instruction tuning language: Bulgarian
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+ * Training method: full-parameter fine-tuning.
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+ * Best checkpoint: best cross-entropy on a validation set, trained for 3 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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+
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+ #### Usage
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+ The model checkpoint should be loaded using `transformers` library.
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+
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+ Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/fpft) for inference and training instructions.
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+
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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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+