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  should probably proofread and complete it, then remove this comment. -->
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  <p align="center" width="100%">
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- <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini/main/images/LaMnin.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
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  </p>
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  # LaMini-T5-61M
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  [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]()
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- This model is one of our LaMini model series in paper "[LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini)". This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini/).
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  You can view other LaMini model series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.
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  <table>
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  ## Training Procedure
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  <p align="center" width="100%">
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- <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
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  </p>
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  We initialize with [t5-small](https://huggingface.co/t5-small) and fine-tune it on our [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 61M.
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  # Citation
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  ```bibtex
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- @misc{lamini,
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- title={LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions},
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- author={},
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  year={2023},
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  publisher = {GitHub},
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  journal = {GitHub repository},
 
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  should probably proofread and complete it, then remove this comment. -->
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  <p align="center" width="100%">
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+ <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/LaMnin.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
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  </p>
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  # LaMini-T5-61M
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  [![Model License](https://img.shields.io/badge/Model%20License-CC%20By%20NC%204.0-red.svg)]()
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+ This model is one of our LaMini model series in paper "[LaMini: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)". This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
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  You can view other LaMini model series as follow. Note that not all models are performing as well. Models with ✩ are those with the best overall performance given their size/architecture. More details can be seen in our paper.
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  <table>
 
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  ## Training Procedure
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  <p align="center" width="100%">
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+ <a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
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  </p>
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  We initialize with [t5-small](https://huggingface.co/t5-small) and fine-tune it on our [LaMini dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 61M.
 
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  # Citation
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  ```bibtex
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+ @misc{lamini-lm,
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+ title={LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
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+ author={Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji},
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  year={2023},
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  publisher = {GitHub},
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  journal = {GitHub repository},