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README.md
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This HF repository hosts instruction fine-tuned multilingual BLOOM model using the parallel instruction dataset called Bactrain-X in 52 languages.
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We progressively add a language during instruction fine-tuning at each time, and train 52 models in total. Then, we evaluate those models in three multilingual benchmarks.
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Please refer to our paper for more details.
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#### Instruction tuning details
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* Base model: [BLOOM 7B1](https://huggingface.co/bigscience/bloom-7b1)
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* Instruction languages: English, Chinese, Afrikaans, Arabic, Azerbaijani, Bengali, Czech, German, Spanish, Estonian
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* Instruction language codes: en, zh, af, ar, az, bn, cs, de, es, et
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* Training method: full-parameter fine-tuning.
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The model checkpoint should be loaded using `transformers` library.
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```python
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model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-10")
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```
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```
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```
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This HF repository hosts instruction fine-tuned multilingual BLOOM model using the parallel instruction dataset called Bactrain-X in 52 languages.
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We progressively add a language during instruction fine-tuning at each time, and train 52 models in total. Then, we evaluate those models in three multilingual benchmarks.
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Please refer to [our paper](https://arxiv.org/abs/2404.04850) for more details.
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* Base model: [BLOOM 7B1](https://huggingface.co/bigscience/bloom-7b1)
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* Instruction languages: English, Chinese, Afrikaans, Arabic, Azerbaijani, Bengali, Czech, German, Spanish, Estonian
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* Instruction language codes: en, zh, af, ar, az, bn, cs, de, es, et
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* Training method: full-parameter fine-tuning.
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### Usage
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The model checkpoint should be loaded using `transformers` library.
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```python
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model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-10")
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```
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### Citation
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```
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@misc{lucky52,
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title = "Lucky 52: How Many Languages Are Needed to Instruction Fine-Tune Large Language Models?",
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author = "Shaoxiong Ji and Pinzhen Chen",
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year = "2024",
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eprint = "2404.04850",
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archiveprefix = "arXiv",
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primaryclass = "cs.CL"
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}
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```
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