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README.md

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+
+---
+language:
+- es
+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: [pythia-6.9b-deduped](https://huggingface.co/pythia-6.9b-deduped)
+* Instruction tuning language: 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](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",
+}
+```
+